uk: Комп'ютерні науки

description: Michel Foucault showed the transformation punishment from the individual body to a more economic way of Punishment. I will show how this translates to modern web, and data services. I will show how Data is used to segment users and achieve desired behaviour . Furthermore Data is used to „punish“ users.Data is used to score Individuals for all kinds of values. Individuals have no transparency over their „Guards“ they don‘t know who owns their Data!

uk: вхід без авторизації

name: Data Privacy in the times of decentralized Surveilla


Tom Wezepoel

type: Person

cs: Informatika

de: Informatik

fi: tietojenkäsittelytiede

uk: Комп'ютерні науки

en: Computer Science

fr: Informatique

da: Datalogi

es: Ciencias de la Computación

nl: Informatica

cs: Technické vědy

de: Ingenieurwissenschaften

fi: tekniikka

uk: Інженерія

en: Engineering Sciences

fr: Ingénierie

da: Ingeniørvidenskab

es: Ingeniería

nl: Engineering

description: Currently we’re also building a Docker environment for OwnCloud in combinatie with Object Storage. In addition to our SURFdrive OwnCloud environment, where we support today more than 37,000 users from Dutch higher education and science with their need for personal data storage. Was there a request for a more flexible storage solution where researchers are able to share more and larger datasets with a group of researchers from home or abroad and third party. As solution for this we have designed a solution on based of OwnCloud, running on Docker, in combination with Swift S3 Object Storage. In this presentation we want to tell you something about our setup and the challenges we have encountered.

TIB AV-Portal

type: Service

type: WebContent

type: MediaObject

@language: en

cs: přihlášení není vyžadováno

de: keine Anmeldung erforderlich

uk: вхід без авторизації

en: no login required

nl: geen login vereist

name: Docker environment for OwnCloud in combination with Object Storage

ownClouders

type: Organization

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Комп'ютерні науки

description: tbd tbd https://cfp.owncloud.com/occon18/talk/TFWHKX

uk: вхід без авторизації

name: Document Classification and Policy Enforcement with ownCloud


Antipatterns

uk: Комп'ютерні науки

description: Felix von Leitner will give a talk on Admin Antipatterns. Admin Antipatterns are Patterns of System-Administrators that you can find in almost any IT Environment that should be avoided.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: tbd tbd https://cfp.owncloud.com/occon18/talk/7MME9K

uk: вхід без авторизації

name: Kopano: future teamwork with data ownership


uk: Комп'ютерні науки

description: To hook up ownCloud to Active Directory can be tedious and error prone. Administrators increase their options in such environments by relying on Univention Corporate Server (UCS). The ownCloud setup for AD is simplified and maintainability is increased. This talk is about the challenge and the use cases UCS offers to administrators that have to deal with Microsoft Active Directory environments and want to use ownCloud. Univention Corporate Server (UCS) is one operating platform for ownCloud and the backbone of the official ownCloud Appliance. To hook up ownCloud to Active Directory is a manual process and can be tedious and error prone. It requires documentation reading and testing. Having ownCloud on UCS allows three options for running ownCloud in an Active Directory environment: 1. UCS is part of the AD domain. 2. UCS provides a seperate domain that is synchronized with AD. 3. UCS replaces AD and takes over all the directory data. In all cases, users in AD can authenticate with ownCloud as soon as they are activated in UCS. Customers have an increased flexibility for how they use ownCloud in their Active Directory environment. It increases their options on how to deal with Active Directory in the future. Furthermore, the administrative efforts are reduced, because of the maintained operating platform Univention Corporate Server. The talk is shortly about the challenges of setting up ownCloud in AD environments and then pans to the benefits UCS offers for ownCloud in such environments. On UCS systems with ownCloud installed, the Active Directory Connector is the next most installed app together with ownCloud. Thus, this topic is very relevant to ownCloud users.

uk: вхід без авторизації

name: Easy and maintainable ownCloud setups in Active Directory environments


From application to service development

uk: Комп'ютерні науки

description: ownCloud offers a great platform for developing applications that can integrate a variety of services, from document editing apps to image previews and more. After running the CERNBox service for the last 5 years, an ownCloud based cloud sync and share for CERN, we have identified that as more integrations are done in the ownCloud platform more knowledge is needed by the ownCloud operators to manage the platform. In CERNBox, we have introduced a distributed approach to split team responsibilities when developing service integrations. ownCloud offers a great platform for developing applications that can integrate a variety of services, from document editing apps to image previews and more. After running the CERNBox service for the last 5 years, an ownCloud based cloud sync and share for CERN, we have identified that as more integrations are done in the ownCloud platform more knowledge is needed by the ownCloud operators to manage the platform. In CERNBox, we have introduced a distributed approach to split team responsibilities when developing service integrations.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Shed some light on the change to MSI. Show usage of the .msi and ask for feedback. ownCloud is switching to MSI for packaging on Windows. This talk shows a few of the reasons and consequences. Live demos included. After the talk there's going to be enough time for questions.

uk: вхід без авторизації

name: From NSIS to MSI: Deploying the ownCloud Client on Windows


uk: Комп'ютерні науки

description: TOdo

uk: вхід без авторизації

name: Gerneral Data Protection Rules: CLoud Act & Other legal challenges


uk: Комп'ютерні науки

description: Overview of GSoC 2018 project with ownCloud. ownCloud's Google Summer of Code Project for this year was titled as 'Petabyte Scale Cloud Storage File Manager'. We'll talk about this project and how it relates to Phoenix.

uk: вхід без авторизації

name: GSoC (Google Summer of Code) with ownCloud: Petabyte Scale Cloud Storage File Manager


How much do you know about ownCloud?

uk: Комп'ютерні науки

description: How much do you know about ownCloud? The definitely Quiz Have you been using ownCloud for many years? Do you think you know how all ownCloud features work? You will have the opportunity to show everybody your knowledge about oC participating in this Quiz.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Demo how to prevent that unbranded ownCloud Clients (Desktop/iOS/Android) are able to log in to your ownCloud instance - thanks to OAuth. This session will showcase how OAuth can be used to lock out unbranded clients in order to make sure, that your users can only use the clients that you provide to them.

uk: вхід без авторизації

name: Lock-out unbranded ownCloud clients with OAuth


uk: Комп'ютерні науки

description: This talk is about the upcoming Office Online Server integration which is currently being developed by ownCloud engineers The Office Online Integration combines ownCloud private storage with online editing capabilities of Microsoft Office Online Server. We will show viewing, editing and even collaborative editing of Word, Excel and Powerpoint documents. In addition to technical background information a live demo will show the current existing functionality.

uk: вхід без авторизації

name: Microsoft Office Online Server Integration with ownCloud


uk: Комп'ютерні науки

description: Over the last couple of years, I have heard of Event sourcing but didn't really know where to start until I did a tutorial at DPC '17. After having some basic information it was time to start a Hackathon and after that something production worthy . In this talk I will try to give the best information to get started and to know some of the problems you can face if you begin event-sourcing. Talk about event sourcing in php with prooph as framework. It should provide all the necessary information to get started with little to no knowledge about event sourcing.

uk: вхід без авторизації

name: My first year with Event-sourcing: And a little bit about tracking your beer


uk: Комп'ютерні науки

description: We'll briefly present ONLYOFFICE and tell the audience what we're doing and planning in terms of our integration with ownCloud. We'll tell you about ONLYOFFICE online editors and our integration app (connector) for ownCloud, that enables real-time collaborative document editing within ownCloud interface. We'll show you how to extend ownCloud with powerful document editors, as well as why ONLYOFFICE online editors advantageously stand out from other suites, what real-time co-editing looks like, collaborate on a doc and of course discuss all follow-up questions.

uk: вхід без авторизації

name: ONLYOFFICE: seamless collaboration on documents for your enterprise


uk: Комп'ютерні науки

description: ownCloud 11 marks a turning point for the ownCloud Development Vision and Strategy outlook for ownCloud

uk: вхід без авторизації

name: ownCloud 11 - The strategic approach to the future of ownCloud


ownCloud Android app in review heaven

uk: Комп'ютерні науки

description: In this lightning talk, I will go through some keys to achieve better reviews in an Android app and how we have implemented them in the ownCloud Android app. How did the ownCloud Android app get from 2-3 to 4-5 stars ratings? This is the question we are going to answer in this lightning talk through the next points: - OwnCloud Android app reviews in the past and main reasons for the bad ones. - New process to receive better reviews, including some tips and real examples that can be useful for other apps. - How to monitor user reviews by using the Google Play Console and real numbers of the ownCloud Android app. https://cfp.owncloud.com/occon18/talk/BSGK7D

uk: вхід без авторизації


uk: Комп'ютерні науки

description: The ownCloud Foundation will focus on supporting the collaboration of the developer community and establishing different working groups. Other tasks are further technical improvement, knowledge exchange and any community related activities. ownCloud and seven board members have officially established the ownCloud Foundation. The organization is dedicated to preserve a sustainable community ecosystem around ownCloud in order to accelerate technology development, improve open source collaboration and provide an open, manufacturer independent governance model. It provides developers, users and the entire global ecosystem with a set of shared resources to grow the footprint of public, private and enterprise ownClouds, producing the best decentralized cloud platform.

uk: вхід без авторизації

name: ownCloud Foundation - A huge Milestone for the Project


uk: Комп'ютерні науки

description: In this talk we will review the changes and new features of the past year within ownCloud Server and give an outlook of where our journey is going.

uk: вхід без авторизації

name: ownCloud Server - Latest News and Roadmap


uk: Комп'ютерні науки

description: I will demonstrate how I use VirtualBox, vagrant, ansible and Apache Jmeter to run reproducable performance tests of different versions of OwnCloud. At SURFsara we use this tool to measure performance of a new OwnCloud version before upgrading. In a nutshell, it starts with a level playing field every time, since installation of sources and populating the database are part of the process. We never go have to go back to old results and compare them with new results, that would not give a clear picture. Rather, we can spin up the environment afresh, be it on a laptop or a high-end server, and re-run the test suite for selected versions, giving a fair comparison. I will explain the concepts and tools used, give a live demonstration of a short test run, and will present results of longer test, that actually took several days to complete and consisted of millions of requests.

uk: вхід без авторизації

name: Automated testing of OwnCloud using VirtualBox, Vagrant, Ansible and Apache JMeter


Phoenix - up close

uk: Комп'ютерні науки

uk: вхід без авторизації


Present & Future of the oC mobile apps

uk: Комп'ютерні науки

description: Review of the status of ownCloud mobile apps: what's new and challenges for the close future. An overview over the ownCloud mobile apps, its new features included during the last year, and how the apps grew up. Also, the new challenges awaiting to be faced. https://cfp.owncloud.com/occon18/talk/WDY8Y7

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Rocket.Chat is an open-source web chat server. With the next release of Rocket.Chat, ownCloud and Rocket.Chat will be able to work together by using WebDAV. This speech is a short introduction to these features. At the beginning of the project, we aimed to integrate ownCloud project to Rocket.Chat. For this purpose, based on current GitHub issues and community expectations, we determined three main features: - Enable Rocket.Chat server administrator to use an ownCloud instance for storage of upload - Allow a user to browse ownCloud files in Rocket.Chat and add files directly from there to Rocket conversation - Allow a user to upload an attachment from Rocket conversation to an ownCloud instance account. To communicate with ownCloud instance, we used a generic WebDAV client. This decision expanded our aim and made the integration compatible with all WebDAV servers (ownCloud and so on). To add ownCloud as a new storage provider for Rocket.Chat, a new UploadFS(https://github.com/jalik/jalik-ufs) store was created. For the integration, a GUI file picker interface was created within Rocket.Chat to allow any user to select files from their ownCloud servers. In this talk, I will shortly explain my summer of code experience with ownCloud and RocketChat projects.

uk: вхід без авторизації

name: Rocket.Chat: WebDAV integration GSoC2018


keywords: TBD Tbd

uk: Комп'ютерні науки

uk: вхід без авторизації

name: A Praise of Folly: Security Theater - The (mostly) unknown OSI Layers 8 and above


uk: Комп'ютерні науки

description: How ownCloud enables Partners to generate revenue

uk: вхід без авторизації

name: Partnerstrategy: (Ecosystem and Infrastructure)


Welcome to ownCloud Conference 2018

uk: Комп'ютерні науки

description: Welcome to the ownCloud Conference 2018! A warm welcome from CEO to the ownCloud Conference 2018.

uk: вхід без авторизації


What is special in CERNBox?

uk: Комп'ютерні науки

description: CERNBox is based on ownCloud for providing sync and share to more than 14,000 users. Besides this use case, CERNBox expands the ownCloud platform to provide advance use cases to the scientific community at CERN. CERNBox is based on ownCloud for providing sync and share to more than 14,000 users. Besides this use case, CERNBox expands the ownCloud platform to provide advance use cases to the scientific community at CERN.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: We study linear programming and general LP-type problems in several big data (streaming and distributed) models. We mainly focus on low dimensional problems in which the number of constraints is much larger than the number of variables. Low dimensional LP-type problems appear frequently in various machine learning tasks such as robust regression, support vector machines, and core vector machines. As supporting large-scale machine learning queries in database systems has become an important direction for database research, obtaining efficient algorithms for low dimensional LP-type problems on massive datasets is of great value. In this paper we give both upper and lower bounds for LP-type problems in distributed and streaming models. Our bounds are almost tight when the dimensionality of the problem is a fixed constant.

uk: вхід без авторизації

name: Distributed and Streaming Linear Programming in Low Dimensions


uk: Комп'ютерні науки

description: The streaming computation model is a standard model for large-scale data analysis: the input arrives one element at a time, and the goal is to maintain an approximately optimal solution using only a constant, or, at worst, polylogarithmic space.In practice, however, recency plays a large role, and one often wishes to consider only the last w elements that have arrived, the so-called sliding window problem. A trivial approach is to simply store the last w elements in a buffer; our goal is to develop algorithms with space and update time sublinear in w. In this regime, there are two frameworks: exponential histograms and smooth histograms, which can be used to obtain sliding window algorithms for families of functions satisfying certain properties.Unfortunately, these frameworks have limitations and cannot always be applied directly. A prominent example is the problem of maximizing submodular function with cardinality constraints. While some of these difficulties can be rectified on a case-by-case basis, here, we describe an alternative approach to designing efficient sliding window algorithms for maximization problems. Then we instantiate this approach on a wide range of problems, yielding better algorithms for submodular function optimization, diversity optimization and general subadditive optimization. In doing so, we improve state-of-the art results obtained using problem-specific algorithms.

uk: вхід без авторизації

name: Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives


uk: Комп'ютерні науки

description: We study the maximum k-coverage problem in the general edge-arrival streaming model: given a collection of m sets F, each subset of a ground set of elements U of size n, the task is to find k sets whose coverage is maximized. The sets are specified as a sequence of (element, set) pairs in an arbitrary order. Our main result is a tight (up to polylogarithmic factors) trade-off between the space complexity and the approximation factor alphain(1/(1-1/e), tildeOmega(sqrtm)] of any single-pass streaming algorithm that estimates the maximum coverage size. Specifically, we show that the optimal space bound is tildeTheta(m/alpha^2). Moreover, we design a single-pass algorithm that reports an alpha-approximate solution in tildeO(m/alpha^2 + k) space. Our algorithm heavily exploits data stream sketching techniques, which could lead to further connections between vector sketching methods and streaming algorithms for combinatorial optimization tasks.

uk: вхід без авторизації

name: Tight Trade-offs for the Maximum k-Coverage Problem in the General Streaming Model


uk: Комп'ютерні науки

description: We consider message-efficient continuous random sampling from a distributed stream, where the probability of inclusion of an item in the sample is proportional to a weight associated with the item. The unweighted version, where all weights are equal, is well studied, and admits tight upper and lower bounds on message complexity. For weighted sampling with replacement, there is a simple reduction to unweighted sampling with replacement. However, in many applications the stream may have only a few heavy items which may dominate a random sample when chosen with replacement. Weighted sampling without replacement (weighted SWOR) eludes this issue, since such heavy items can be sampled at most once. In this work, we present the first message-optimal algorithm for weighted SWOR from a distributed stream. Our algorithm also has optimal space and time complexity. As an application of our algorithm for weighted SWOR, we derive the first distributed streaming algorithms for tracking heavy hitters with residual error. Here the goal is to identify stream items that contribute significantly to the residual stream, once the heaviest items are removed. Residual heavy hitters generalize the notion of ell_1 heavy hitters and are important in streams that have a skewed distribution of weights. In addition to the upper bound, we also provide a lower bound on the message complexity that is nearly tight up to a log(1/eps) factor. Finally, we use our weighted sampling algorithm to improve the message complexity of distributed L_1 tracking, also known as count tracking, which is a widely studied problem in distributed streaming. We also derive a tight message lower bound, which closes the message complexity of this fundamental problem.

uk: вхід без авторизації

name: Weighted Reservoir Sampling from Distributed Streams


uk: Комп'ютерні науки

description: XPath is arguably the most popular query language for selecting elements in XML documents. Besides query evaluation, query satisfiability and containment are the main computational problems for XPath; they are useful, for instance, to detect dead code or validate query optimisations. These problems are undecidable in general, but several fragments have been identified over time for which satisfiability (or query containment) is decidable: CoreXPath 1.0 and 2.0 without so-called data joins, fragments with data joins but limited navigation, etc. However, these fragments are often given in a simplified syntax, and sometimes w.r.t. a simplified XPath semantics. Moreover, they have been studied mostly with theoretical motivations, with little consideration for the practically relevant features of XPath. To investigate the practical impact of these theoretical fragments, we design a benchmark compiling thousands of real-world XPath queries extracted from open-source projects, and match them against syntactic fragments from the literature. We investigate how to extend these fragments with seldom-considered features such as free variables, data tests, data joins, and the last () and id () functions, for which we provide both undecidability and decidability results. We analyse the coverage of the original and extended fragments, and further provide a glimpse at which other practical features might be worth investigating in the future.

uk: вхід без авторизації

name: Decidable XPath Fragments in the Real World


uk: Комп'ютерні науки

description: We study the problem of containment of shape expression schemas ShEx for RDF graphs. We identify a subclass of ShEx that has a natural graphical representation in the form of shape graphs and whose semantics is captured with a tractable notion of embedding of an RDF graph in a shape graph. When applied to pairs of shape graphs, an embedding is a sufficient condition for containment, and for a practical subclass of deterministic shape graphs, it is also a necessary one, thus yielding a subclass with tractable containment. Containment for general shape graphs is EXP-complete. Finally, we show that containment for arbitrary ShEx is decidable.

uk: вхід без авторизації

name: Containment of Shape Expression Schemas for RDF


The Space-Efficient Core of Vadalog

uk: Комп'ютерні науки

description: Vadalog is a system for performing complex reasoning tasks such as those required in advanced knowledge graphs. The logical core of the underlying Vadalog language is the warded fragment of tuple-generating dependencies (TGDs). This formalism ensures tractable reasoning in data complexity, while a recent analysis focusing on a practical implementation led to the reasoning algorithm around which the Vadalog system is built. A fundamental question that has emerged in the context of Vadalog is the following: can we limit the recursion allowed by wardedness in order to obtain a formalism that provides a convenient syntax for expressing useful recursive statements, and at the same time achieves space-efficiency? After analyzing several real-life examples of warded sets of TGDs provided by our industrial partners, as well as recent benchmarks, we observed that recursion is often used in a restricted way: the body of a TGD contains at most one atom whose predicate is mutually recursive with a predicate in the head. We show that this type of recursion, known as piece-wise linear in the Datalog literature, is the answer to our main question. We further show that piece-wise linear recursion alone, without the wardedness condition, is not enough as it leads to the undecidability of reasoning. We finally study the relative expressiveness of the query languages based on (piece-wise linear) warded sets of TGDs.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: A prominent research direction of the database theory community is to develop techniques for verification of database-driven systems operating over relational and numerical data. Along this line, we lift the framework of database manipulating systems citeAbdullaAAMR-pods-16 which handle relational data to also accommodate numerical data and the natural order on them. We study an under-approximation called recency bounding under which the most basic verification problem --reachability, is decidable. Even under this under-approximation the reachability space is infinite in multiple dimensions -- owing to the unbounded sizes of the active domain, the unbounded numerical domain it has access to, and the unbounded length of the executions. We show that, nevertheless, reachability is ExpTime complete. Going beyond reachability to LTL model checking renders verification undecidable.

uk: вхід без авторизації

name: Reachability in Database-driven Systems with Numerical Attributes under Recency Bounding


uk: Комп'ютерні науки

description: We investigate the complexity of evaluating queries in Relational Algebra (RA) over the relations extracted by regex formulas (i.e., regular expressions with capture variables) over text documents. Such queries, also known as the regular document spanners, were shown to have an evaluation with polynomial delay for every positive RA expression (i.e., consisting of only natural joins, projections and unions); here, the RA expression is fixed and the input consists of both the regex formulas and the document. In this work, we explore the implication of two fundamental generalizations. The first is adopting the 'schemaless' semantics for spanners, as proposed and studied by Maturana et al. The second is going beyond the positive RA to allowing the difference operator. We show that each of the two generalizations introduces computational hardness: it is intractable to compute the natural join of two regex formulas under the schemaless semantics, and the difference between two regex formulas under both the ordinary and schemaless semantics. Nevertheless, we propose and analyze syntactic constraints, on the RA expression and the regex formulas at hand, such that the expressive power is fully preserved and, yet, evaluation can be done with polynomial delay. Unlike the previous work on RA over regex formulas, our technique is not (and provably cannot be) based on the static compilation of regex formulas, but rather on an ad-hoc compilation into an automaton that incorporates both the query and the document. This approach also allows us to include black-box extractors in the RA expression.

uk: вхід без авторизації

name: Complexity Bounds for Relational Algebra over Document Spanners


uk: Комп'ютерні науки

description: A crucial property of bounded-variable fragments of first-order logic is that they can be evaluated in polynomial time. It is therefore a useful preprocessing step to rewrite, if possible, a first-order query to a logically equivalent one with a minimum number of variables. However, it may occur that reducing the number of variables causes an increase in formula size. We investigate this trade-off for the existential-positive fragment of first-order queries, where variable minimisation is decidable in general. In particular, we study the blow-up in the formula size when compiling existential-positive queries to the bounded variable fragment of positive first-order logic. While the increase of the formula size is always at most exponential, we identify situations (based on the signature and the number of variables) where only a polynomial blow-up is needed. In all other cases, we show that an exponential lower bound on the formula size of the compiled formula that matches the general upper bound. This exponential lower bound is unconditional, and is the first unconditional lower bound for formula size with respect to the studied compilation; it is proved via establishing a novel interface with circuit complexity which may be of future interest.

uk: вхід без авторизації

name: Compiling Existential Positive Queries to Bounded-Variable Fragments


uk: Комп'ютерні науки

description: Query containment is the fundamental problem of deciding, given two database queries, if the result of the first query is always contained in the result of the second query. For a number of established query classes, an instance of this problem can be decided by computing a set of models of the first query, and then evaluating the second query on each of the models. We formalize this phenomenon by introducing the selfish models property; this property gives an avenue for establishing both the decidability of and complexity upper bounds for containment problems. Using this property, we show how existing results can be uniformly derived, and we present two significant novel positive results for first-order query containment problems, exhibiting complexity upper bounds for containment problems that were not previously known to be decidable.

uk: вхід без авторизації

name: The Selfish Models Property: Bounding the Complexity of Query Containment and Entailment Problems


uk: Комп'ютерні науки

description: In this paper, we utilize the perspective of property testing to consider the testability of relational database queries. A primary motivation is the desire to avoid reading an entire database to decide a property thereof. We focus on conjunctive queries, which are the most basic and heavily studied database queries. Each conjunctive query can be represented as a relational structure A such that deciding if the conjunctive query is satisfied by a relational structure B is equivalent to deciding if there exists a homomorphism from A to B. We phrase our results in terms of homomorphisms. Precisely, we study, for each relational structure A, the testability of homomorphism inadmissibility from A. We consider algorithms that have oracle access to an input relational structure B and that distinguish, with high probability, the case where there is no homomorphism from A to B, from the case where one needs to remove a constant fraction of tuples from B in order to suppress all such homomorphisms. We provide a complete characterization of the structures A from which one can test homomorphism inadmissibility with one-sided error by making a constant number of queries to B. Our characterization shows that homomorphism inadmissibility from A is constant-query testable with one-sided error if and only if the core of A is alpha-acyclic. We also show that the injective version of the problem is constant-query testable with one-sided error if A is alpha-acyclic; this result generalizes existing results for testing subgraph-freeness in the general graph model.

uk: вхід без авторизації

name: Testability of Homomorphism Inadmissibility: Property Testing Meets Database Theory


uk: Комп'ютерні науки

description: To cope with the intractability of answering Conjunctive Queries (CQs) and solving Constraint Satisfaction Problems (CSPs), several notions of hypergraph decompositions have been proposed - giving rise to different notions of width, noticeably, plain, generalized, and fractional hypertree width (hw, ghw, and fhw). Given the increasing interest in using such decomposition methods in practice, a publicly accessible repository of decomposition software, as well as a large set of benchmarks, and a web-accessible workbench for inserting, analysing, and retrieving hypergraphs are called for. We address this need by providing (i) concrete implementations of hypergraph decompositions (including new practical algorithms), (ii) a new, comprehensive benchmark of hypergraphs stemming from disparate CQ and CSP collections, and (iii) HyperBench, our new web-interface for accessing the benchmark and the results of our analyses. In addition, we describe a number of actual experiments we carried out with this new infrastructure.

uk: вхід без авторизації

name: HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings


uk: Комп'ютерні науки

description: Massively parallel join algorithms have received much attention in recent years, while most prior work has focused on worst-optimal algorithms. However, the worst-case optimality of these join algorithms relies on hard instances having very large output sizes, which rarely appear in practice. A stronger notion of optimality is output-optimal, which requires an algorithm to be optimal within the class of all instances sharing the same input and output size. An even stronger optimality is instance-optimal, i.e., the algorithm is optimal on every single instance, but this may not always be achievable. In the traditional RAM model of computation, the classical Yannakakis algorithm is instance-optimal on any acyclic join. But in the massively parallel computation (MPC) model, the situation becomes much more complicated. We first show that for the class of r-hierarchical joins, instance-optimality can still be achieved in the MPC model. Then, we give a new MPC algorithm for an arbitrary acyclic join with load O (IN over p + sqrtIN cdot OUT over p), where IN,OUT are the input and output sizes of the join, and p is the number of servers in the MPC model. This improves the MPC version of the Yannakakis algorithm by an O (sqrtOUT over IN ) factor. Furthermore, we show that this is output-optimal when OUT = O(p cdot IN), for every acyclic but non-r-hierarchical join. Finally, we give the first output-sensitive lower bound for the triangle join in the MPC model, showing that it is inherently more difficult than acyclic joins.

uk: вхід без авторизації

name: Instance and Output Optimal Parallel Algorithms for Acyclic Joins


uk: Комп'ютерні науки

description: Conjunctive-query containment is the problem of deciding whether the answers of a given conjunctive query on an arbitrary database instance are always contained in the answers of a second query on the same instance. This is a very relevant question in query optimization, data integration, and other data management and artificial intelligence areas. The problem has been deeply studied and understood for the, so-called, set-semantics, i.e., when query answers and database instances are modelled as sets of tuples. In particular, it has been shown by Chandra and Merlin to be NPTIME-COMPLETE. On the contrary, when investigated under bag-semantics, a.k.a. multiset semantics, which allows for replicated tuples both in the underlying instance and in the query answers, it is not even clear whether the problem is decidable. Since this is exactly the standard interpretation for commercial relational database systems, the question turns out to be an important one. Multiple works on variations and restrictions of the bag-containment problem have been reported in the literature and, although the general problem is still open, we contribute with this article by solving a special case that has been identified as a major open problem on its own. More specifically, we study projection-free queries, i.e., queries without existentially quantified variables, and show decidability for the bag-containment problem of a projection-free conjunctive query into a generic conjunctive query. We prove indeed that deciding containment in this setting is in Pi^p_2. Our approach relies on the solution of a special case of the Diophantine inequality problem via a reduction to the linear inequality problem and clearly exposes inherent difficulties in the analysis of the general question.

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name: Attacking Diophantus: Solving a Special Case of Bag Containment


Topology Dependent Bounds For FAQs

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description: In this paper, we prove topology dependent bounds on the number of rounds needed to compute Functional Aggregate Queries (FAQs) studied by Abo Khamis et al. [PODS 2016] in a synchronous distributed network under the model considered by Chattopadhyay et al. [FOCS 2014, SODA 2017]. Unlike the recent work on computing database queries in the Massively Parallel Computation model, in the model of Chattopadhyay et al., nodes can communicate only via private point-to-point channels and we are interested in bounds that work over an arbitrary communication topology. This model, which is closer to the well-studied congest model in distributed computing and generalizes Yao's two party communication complexity model, has so far only been studied for problems that are common in the two-party communication complexity literature. This is the first work to consider more practically motivated problems in this distributed model. For the sake of exposition, we focus on two specific problems in this paper: Boolean Conjunctive Query (BCQ) and computing variable/factor marginals in Probabilistic Graphical Models (PGMs). We obtain tight bounds on the number of rounds needed to compute such queries as long as the underlying hypergraph of the query is O(1)-degenerate and has O(1)-arity. In particular, the O(1)-degeneracy condition covers most well-studied queries that are efficiently computable in the centralized computation model like queries with constant treewidth. These tight bounds depend on a new notion of `width' (namely internal-node-width) for Generalized Hypertree Decompositions (GHDs) of acyclic hypergraphs, which minimizes the number of internal nodes in a sub-class of GHDs. To the best of our knowledge, this width has not been studied explicitly in the theoretical database literature. Finally, we consider the problem of computing the product of a vector with a chain of matrices and prove tight bounds on its round complexity (over a finite field of two elements) using a novel min-entropy based argument.

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uk: Комп'ютерні науки

description: What happens when we replace — or augment — human decision-making with algorithms? This is a simple question, but the answers now define a new field of study — a field that I call algorithmic fairness, and that spans issues of fairness, discrimination, accountability, transparency, interpretability and responsibility, and so much more. While some of the early work in the area came out of data mining and machine learning, the field is now truly transdisciplinary, with contributions from all across computer science, as well as from all disciplines that touch on aspects of society - whether it be economics, philosophy, sociology, political science, or communication. In this tutorial, I’ll try to do three things: I’ll survey the main questions and some of the key insights we’ve developed over the years. I’ll explain the web of connections between the technical and the social disciplines that make up this area, and I’ll point to exciting directions that remain to be explored in both technical and social dimensions. Along the way I hope to illustrate what I think are some interesting “collisions” between computer science and the social sciences, and call for a reimagining of core ideas in our field, including the very idea of how we think about data representation.

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name: Algorithmic Fairness: Measures, Methods and Representations


uk: Комп'ютерні науки

description: Matei Zaharia is an Assistant Professor of Computer Science at Stanford University. He is Chief Technologist and Co-Founder of Databricks, a company that provides a unified cloud platform for data and AI. He started the Apache Spark project during his PhD at UC Berkeley in 2009, and has worked broadly in datacenter systems, co-starting the Apache Mesos project and contributing as a committer on Apache Hadoop. Today, Matei tech-leads the MLflow development effort at Databricks. Matei’s research work was recognized through the 2014 ACM Doctoral Dissertation Award for the best PhD dissertation in computer science, an NSF CAREER Award and several best paper awards.

uk: вхід без авторизації

name: Infrastructure for Machine Learning: Ideas from Industry and Research


uk: Комп'ютерні науки

description: A tutorial given at PODS 2019, focussed on several research agendas in the past decade or so, that examine weak isolation levels, and obtain many (or all) of the benefits for application integrity, traditionally achieved by serializable concurrency control. The tutorial presents both the mechanisms and the reasoning approaches from these research works.

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name: Making Consistency Protocols Serializable


Differential Privacy and the US Census

uk: Комп'ютерні науки

description: Differential privacy is a mathematically rigorous definition of privacy tailored to statistical analysis of large datasets. Differentially private systems simultaneously provide useful statistics to the well-intentioned data analyst and strong protection against arbitrarily powerful adversarial system users -- without needing to distinguish between the two. Differentially private systems 'don't care' what the adversary knows, now or in the future. Finally, differentially private systems can rigorously bound and control the cumulative privacy loss that accrues over many interactions with the confidential data. These unique properties, together with the abundance of auxiliary data sources and the ease with which they can be deployed by a privacy adversary, led the US Census Bureau to adopt differential privacy as the disclosure avoidance methodology of the 2020 decennial census. This talk will motivate the definition of differential privacy, reflect on the theory-meets-practice experiences of the decennial census, and highlight a few pressing challenges in the field.

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uk: Комп'ютерні науки

description: Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want the data exploration to ensure provable privacy guarantees. However, current systems for answering queries with differential privacy place an inordinate burden on the data analysts to understand differential privacy, manage their privacy budget, and even implement new algorithms for noisy query answering. Moreover, current systems do not provide any guarantees to the data analyst on the quality they care about, namely accuracy of query answers. We present APEx, a novel system that allows data analysts to pose adaptively chosen sequences of queries along with required accuracy bounds. By translating queries and accuracy bounds into differentially private algorithms with the least privacy loss, APEx returns query answers to the data analyst that meet the accuracy bounds, and proves to the data owner that the entire data exploration process is differentially private. Our comprehensive experimental study on real datasets demonstrates that APEx can answer a variety of queries accurately with moderate to small privacy loss, and can support data exploration for entity resolution with high accuracy under reasonable privacy settings.

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name: APEx: Accuracy-Aware Differentially Private Data Exploration


uk: Комп'ютерні науки

description: LSM-Trees and B-Trees are the two primary data structures used as storage engines in modern key-value (KV) stores. These two structures are optimal for different workloads; LSM-Trees perform better on update queries, whereas B-Trees are preferable for short range lookups. KV stores today use one or the other. However, for modern applications with increasingly diverse workloads, limiting KV stores to utilize only one of the two designs leads to a significant loss in performance. We propose a novel method of online transitioning a KV store from an LSM-Tree to a B-Tree and vice versa. This allows KV stores to smoothly adapt to changing workloads and use the optimal data structure as the workload changes.

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name: LSM-Trees and B-Trees: The Best of Both Worlds


Responsible Data Science

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description: Data science is an emerging discipline that offers both promise and peril. Responsible data science refers to efforts that address both the technical and societal issues in emerging data-driven technologies. How can machine learning and database systems reason effectively about complex dependencies and uncertainty? Furthermore, how do we understand the ethical and societal issues involved in data-driven decision-making? There is a pressing need to integrate algorithmic and statistical principles, social science theories, and basic humanist concepts so that we can think critically and constructively about the socio-technical systems we are building. In this talk, I will overview this emerging area, with an emphasis on relational learning.

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uk: Комп'ютерні науки

description: It has been a decade since the concept of blockchain was invented as the underlying core data structure of the permissionless or public Bitcoin cryptocurrency network. Since then, several cryptocurrencies, tokens and ICOs have emerged. After much speculation and hype, significant number of them have become problematic or worthless! The public blockchain system Ethereum emerged by generalizing the use of blockchains to manage any kind of asset, be it physical or purely digital, with the introduction of Smart Contracts. Over the years, numerous myths have developed with respect to the purported utility and the need for public blockchains. The adoption and further adaptation of blockchains and smart contracts for use in the permissioned or private environments is what I consider to be useful and of practical consequence. Hence, the technical aspects of only private blockchain systems will be the focus of my SIGMOD 2019 keynote. Along the way, I will bust many myths associated with public blockchains. I will also compare traditional database technologies with blockchain systems' features and identify desirable future research topics.

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name: State of Public and Private Blockchains: Myths and Reality


uk: Комп'ютерні науки

description: Within the last few years, a countless number of blockchain systems have emerged on the market, each one claiming to revolutionize the way of distributed transaction processing in one way or the other. Many blockchain features, such as byzantine fault tolerance, are indeed valuable additions in modern environments. However, despite all the hype around the technology, many of the challenges that blockchain systems have to face are fundamental transaction management problems. These are largely shared with traditional database systems, which have been around for decades already. These similarities become especially visible for systems, that blur the lines between blockchain systems and classical database systems. A great example of this is Hyperledger Fabric, an open-source permissioned blockchain system under development by IBM. By implementing parallel transaction processing, Fabric's workflow is highly motivated by optimistic concurrency control mechanisms in classical database systems. This raises two questions: (1)~Which conceptual similarities and differences do actually exist between a system such as Fabric and a classical distributed database system? (2)~Is it possible to improve on the performance of Fabric by transitioning technology from the database world to blockchains and thus blurring the lines between these two types of systems even further? To tackle these questions, we first explore Fabric from the perspective of database research, where we observe weaknesses in the transaction pipeline. We then solve these issues by transitioning well-understood database concepts to Fabric, namely transaction reordering as well as early transaction abort. Our experimental evaluation under the Smallbank benchmark as well as under a custom workload shows that our improved version Fabric++ significantly increases the throughput of successful transactions over the vanilla version by up to a factor of 12x, while decreasing the average latency to almost half.

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name: Blurring the Lines between Blockchains and Database Systems: the Case of Hyperledger Fabric


uk: Комп'ютерні науки

description: Speech-based inputs have become popular in many applications on constrained device environments such as smartphones and tablets, and even personal conversational assistants such as Siri, Alexa, and Cortana. Inspired by this recent success of speech-driven interfaces, in this work, we consider an important fundamental question: How should one design a speech-driven system to query structured data? Recent works have studied new querying modalities like visual [4, 8], touch-based [3, 7], and natural language interfaces (NLIs) [5, 6], especially for constrained querying environments such as tablets, smartphones, and conversational assistants. The commands given by the user are then translated to the Structured Query Language (SQL). But conspicuous by its absence is a speech-driven interface for regular SQL or other structured querying. One might wonder: Why dictate structured queries and not just use NLIs or visual interfaces? From a practical standpoint, many users, including in the C-suite, enterprise, Web, and other domains are already familiar with SQL (even if only a subset of it) and use it routinely. A spoken SQL interface could help them speed up query specification, especially in constrained settings such as smartphones and tablets, where typing SQL would be painful. More fundamentally, there is a trade-off inherent in any query interface, as illustrated in Figure 1(A).

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name: SpeakQL: Towards Speech-driven Multimodal Querying


uk: Комп'ютерні науки

description: Multi-dimensional analytical (MDA) queries are often issued against a fact table with predicates on (categorical or ordinal) dimensions and aggregations on one or more measures. In this paper, we study the problem of answering MDA queries under local differential privacy (LDP). In the absence of a trusted agent, sensitive dimensions are encoded in a privacy-preserving (LDP) way locally before being sent to the data collector. The data collector estimates the answers to MDA queries, based on the encoded dimensions. We propose several LDP encoders and estimation algorithms, to handle a large class of MDA queries with different types of predicates and aggregation functions. Our techniques are able to answer these queries with tight error bounds and scale well in high-dimensional settings (i.e., error is polylogarithmic in dimension sizes). We conduct experiments on real and synthetic data to verify our theoretical results, and compare our solution with marginal-estimation based solutions.

uk: вхід без авторизації

name: Answering Multi-Dimensional Analytical Queries under Local Differential Privacy


uk: Комп'ютерні науки

description: The ability to extract insights from data is critical for decision making. Intuitive natural language interfaces to databases provide non-technical users with an effective way to formulate complex questions and information needs efficiently and effectively. A recent trend in the area of Natural Language Interfaces for Databases (NLIDBs) has been the use of neural machine translation models to synthesize executable Structured Query Language (SQL) queries from natural language utterances. The main bottleneck in this type of approach is the acquisition of examples for training the model. Recent work has assumed access to a rich manually-curated training set for a given target database. However, this assumption ignores the large manual overhead required to curate the training set for any new database. As a result, NLIDB systems that can simply ‘plug in’ to any new database and perform effectively for naive users have yet to make their way into commercial products. Here we present DBPal, an end-to-end NLIDB framework in which a neural translation model is trained for any new database schema with minimal manual overhead. In addition to being the first off-the-shelf, neural machine translationbased system of its kind, the contributions of our project are 1) its use of a synthetic training set generation pipeline used to bootstrap a translation model without requiring manually curated data, and 2) its use of state-of-the-art multi-task and cross-domain learning techniques that increases the robustness of the translation model towards unseen linguistic phenomena in new domains. In experiments we show that our system can achieve competitive performance on the recently released benchmarks for nl-to-sql translation. Through ablation experiments we show the benefit of using cross-domain learning techniques on the performance of the system. In a user study we show that DBPal outperforms a well-known rule-based NLIDB and performs comparably to an approach using a similar neural model that relies on manually curated data.

uk: вхід без авторизації

name: Bootstrapping an End-to-End Natural Language Interface for Databases


uk: Комп'ютерні науки

description: We consider the feature-generation task wherein we are given a database with entities labeled as positive and negative examples, and the goal is to find feature queries that allow for a linear separation between the two sets of examples. We focus on conjunctive feature queries, and explore two fundamental problems: (a) deciding whether separating feature queries exist (separability), and (b) generating such queries when they exist. In the approximate versions of these problems, we allow a predefined fraction of the examples to be misclassified. To restrict the complexity of the generated classifiers, we explore various ways of regularizing (i.e., imposing simplicity constraints on) them by limiting their dimension, the number of joins in feature queries, and their generalized hypertree width (ghw). Among other results, we show that the separability problem is tractable in the case of bounded ghw; yet, the generation problem is intractable, simply because the feature queries might be too large. So, we explore a third problem: classifying new entities without necessarily generating the feature queries. Interestingly, in the case of bounded ghw we can efficiently classify without ever explicitly generating the feature queries.

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name: Regularizing Conjunctive Features for Classification


uk: Комп'ютерні науки

description: To enhance performance in main memory databases, compression techniques have been suggested to keep large volume of data in-memory, as opposed to loading data on demand from slower media storage. High compression ratio, however, comes with both memory and performance overhead for queries; packed data needs to be decompressed into vectors before applying optimized scan algorithms. In this work, we propose data summaries at column block level. Our preliminary experimental studies on TPC-H data confirm that under the same memory budget used for MinMax synopsis, our block headers can lower the false positive rates by up to 30% for compressed data scans and can reduce the overhead of employing advanced compression schemes.

uk: вхід без авторизації

name: Fingerprints for Compressed Columnar Data Search


uk: Комп'ютерні науки

description: A major factor that prevents the large scale deployment of Mobile Crowd Sensing (MCS) is its sensing and communication cost. Given the spatio-temporal correlation among the environment monitoring data, matrix completion (MC) can be exploited to only monitor a small part of locations and time, and infer the remaining data. Rather than only taking random measurements following the basic MC theory, to further reduce the cost of MCS while ensuring the quality of missing data inference, we propose an Active Sparse MCS (AS-MCS) scheme which includes a bipartite-graph-based sensing scheduling scheme to actively determine the sampling positions in each upcoming time slot, and a bipartite-graph-based matrix completion algorithm to robustly and accurately recover the un-sampled data in the presence of sensing and communications errors. We also incorporate the sensing cost into the bipartite-graph to facilitate low cost sample selection and consider the incentives for MCS. We have conducted extensive performance studies using the data sets from the monitoring of PM 2.5 air condition and road traffic speed, respectively. Our results demonstrate that our AS-MCS scheme can recover the missing data at very high accuracy with the sampling ratio only around 11%, while the peer matrix completion algorithms with similar recovery performance requires up to 4-9 times the number of samples of ours for both the data sets.

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name: Active Sparse Mobile Crowd Sensing Based on Matrix Completion


uk: Комп'ютерні науки

description: Blockchains have recently been under the spotlight due to the boom of cryptocurrencies and decentralized applications. There is an increasing demand for querying the data stored in a blockchain database. To ensure query integrity, the user can maintain the entire blockchain database and query the data locally. However, this approach is not economic, if not infeasible, because of the blockchain's huge data size and considerable maintenance costs. In this paper, we take the first step toward investigating the problem of verifiable query processing over blockchain databases. We propose a novel framework, called vChain, that alleviates the storage and computing costs of the user and employs verifiable queries to guarantee the results' integrity. To support verifiable Boolean range queries, we propose an accumulator-based authenticated data structure that enables dynamic aggregation over arbitrary query attributes. Two new indexes are further developed to aggregate intra-block and inter-block data records for efficient query verification. We also propose an inverted prefix tree structure to accelerate the processing of a large number of subscription queries simultaneously. Security analysis and empirical study validate the robustness and practicality of the proposed techniques.

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name: vChain: Enabling Verifiable Boolean Range Queries over Blockchain Databases


uk: Комп'ютерні науки

description: Existing blockchain systems scale poorly because of their distributed consensus protocols. Current attempts at improving blockchain scalability are limited to cryptocurrency. Scaling blockchain systems under general workloads (i.e., non-cryptocurrency applications) remains an open question. This work takes a principled approach to apply sharding to blockchain systems in order to improve their transaction throughput at scale. This is challenging, however, due to the fundamental difference in failure models between databases and blockchain. To achieve our goal, we first enhance the performance of Byzantine consensus protocols, improving individual shards' throughput. Next, we design an efficient shard formation protocol that securely assigns nodes into shards. We rely on trusted hardware, namely Intel SGX, to achieve high performance for both consensus and shard formation protocol. Third, we design a general distributed transaction protocol that ensures safety and liveness even when transaction coordinators are malicious. Finally, we conduct an extensive evaluation of our design both on a local cluster and on Google Cloud Platform. The results show that our consensus and shard formation protocols outperform state-of-the-art solutions at scale. More importantly, our sharded blockchain reaches a high throughput that can handle Visa-level workloads, and is the largest ever reported in a realistic environment.

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name: Towards Scaling Blickchain Systems via Sharding


uk: Комп'ютерні науки

description: Probabilistic databases (PDBs) introduce uncertainty into relational databases by specifying probabilities for several possible instances. Traditionally, they are finite probability spaces over database instances. Such finite PDBs inherently make a closed-world assumption: non-occurring facts are assumed to be impossible, rather than just unlikely. As convincingly argued by Ceylan et al. (KR '16), this results in implausibilities and clashes with intuition. An open-world assumption, where facts not explicitly listed may have a small positive probability can yield more reasonable results. The corresponding open-world model of Ceylan et al., however, assumes that all entities in the PDB come from a fixed finite universe. In this work, we take one further step and propose a model of truly open-world PDBs with an infinite universe. This is natural when we consider entities from typical domains such as integers, real numbers, or strings. While the probability space might become infinitely large, all instances of a PDB remain finite. We provide a sound mathematical framework for infinite PDBs generalizing the existing theory of finite PDBs. Our main results are concerned with countable, tuple-independent PDBs; we present a generic construction showing that such PDBs exist in the infinite and provide a characterization of their existence. This construction can be used to give an open-world semantics to finite PDBs. The construction can also be extended to so-called block-independent-disjoint probabilistic databases. Algorithmic questions are not the focus of this paper, but we show how query evaluation algorithms can be lifted from finite PDBs to perform approximate evaluation (with an arbitrarily small additive approximation error) in countably infinite tuple-independent PDBs.

uk: вхід без авторизації

name: Probabilistic Databases with an Infinite Open-World Assumption


Topic Pages: From Articles to Answers

uk: Комп'ютерні науки

description: Deep Kayal is a Senior Data Scientist at Elsevier, specializing in Natural Language Processing and Machine Learning, with several years of relevant experience in algorithm development and overall product development. He is familiar with the engineering concepts related to analytics as well as knowledge of various machine learning algorithms, and specializes in deployment of machine learning models and pipelines at scale. Besides his work at Elsevier, he is currently a part-time researcher at Erasmus MC in Rotterdam, exploring machine learning techniques for biomedical image analysis. He holds a MSc degree in Machine Learning from Helsinki University of Technology, Finland, and has also published several research papers and patents.

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Query Evaluation in Election Databases

uk: Комп'ютерні науки

description: Election databases are the main elements of a recently introduced framework that aims to create bridges between the computational social choice and the data management communities. An election database consists of incomplete information about the preferences of voters, in the form of partial orders, alongside with standard database relations that provide contextual information. Earlier work in computational social choice focused on the computation of possible winners and necessary winners that are determined by the available incomplete information and the voting rule at hand. The presence of the relational context, however, permits the formulation of sophisticated queries about voting rules, candidates, potential winners, issues, and positions on issues. Such queries can be given possible answer semantics and necessary answer semantics on an election database, where the former means that the query is true on some completion of the given partial orders and the latter means that the query is true on every such completion. %In this paper, We carry out a systematic investigation of query evaluation on election databases by analyzing how the interaction between the partial preferences, the voting rules and the relational context impacts on the complexity of query evaluation. To this effect, we focus on positional scoring rules and unions of conjunctive queries. We establish a number of results that delineate the complexity of the possible answers and of the necessary answers for different positional scoring rules and for various classes of unions of conjunctive queries. Furthermore, we show that query evaluation is fixed-parameter tractable, where the parameter is the number of candidates in the election.

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uk: Комп'ютерні науки

description: Xin Luna Dong is a Principal Scientist at Amazon, leading the efforts of constructing Amazon Product Knowledge Graph. She was one of the major contributors to the Google Knowledge Vault project, and has led the Knowledge-based Trust project, which is called the “Google Truth Machine” by Washington’s Post. She has co-authored the book “Big Data Integration”, was awarded ACM Distinguished Member, VLDB Early Career Research Contribution Award for 'advancing the state of the art of knowledge fusion', and Best Demo award in SIGMOD 2005. She serves in VLDB Endowment and PVLDB Advisory Committee, and is a PC co-chair for VLDB 2021, the ICDE 2019 Industry track, VLDB 2019 Tutorials, SIGMOD 2018 and WAIM 2015.

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name: Ceres: Harvesting knowledge from the semi-structured web


uk: Комп'ютерні науки

description: Managing inconsistencies in databases is an old, but recurring, problem. An inconsistent database is a database that violates one or more integrity constraints. In the real-world, inconsistent databases arise in several different contexts, including data warehousing and information integration. The framework of database repairs and consistent query answering (CQA) is a principled way of handling inconsistencies. In this work, we propose a novel approach that has a potential to build a comprehensive and scalable CQA system. We report preliminary experimental results on a prototype CQA system CAvSAT (Consistent Answering via Satisfiability), implemented using this approach.

uk: вхід без авторизації

name: CAvSAT: A System for Query Answering over Inconsistent Databases


uk: Комп'ютерні науки

uk: вхід без авторизації

name: A General Datalog-based Framework for Tractable Query Answering


uk: Комп'ютерні науки

description: Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some of the input factors are defined by a collection of additive inequalities between variables. We refer to these queries as FAQ-AI for short. To answer FAQ-AI in the Boolean semiring, we define relaxed tree decompositions and relaxed submodular and fractional hypertree width parameters. We show that an extension of the InsideOut algorithm using Chazelle's geometric data structure for solving the semigroup range search problem can answer Boolean FAQ-AI in time given by these new width parameters. This new algorithm achieves lower complexity than known solutions for FAQ-AI. It also recovers some known results in database query answering. Our second contribution is a relaxation of the set of polymatroids that gives rise to the counting version of the submodular width, denoted by #subw. This new width is sandwiched between the submodular and the fractional hypertree widths. Any FAQ and FAQ-AI over one semiring can be answered in time proportional to #subw and respectively to the relaxed version of #subw. We present three applications of our FAQ-AI framework to relational machine learning: k-means clustering, training linear support vector machines, and training models using non-polynomial loss. These optimization problems can be solved over a database asymptotically faster than computing the join of the database relations.

uk: вхід без авторизації

name: On Functional Aggregate Queries with Additive Inequalities


uk: Комп'ютерні науки

description: A tree decomposition of a graph facilitates computations by grouping vertices into bags that are interconnected in an acyclic structure; hence their importance in a plethora of problems such as query evaluation over databases and inference over probabilistic graphical models. The relative benefit from different tree decompositions is measured by diverse (sometime complex) cost functions that vary from one application to another. For generic cost functions like width and fill-in, an optimal tree decomposition can be efficiently computed in some cases, notably when the number of minimal separators is bounded by a polynomial (due to Bouchitte and Todinca); we refer to this assumption as 'poly-MS.' To cover the variety of cost functions in need, it has recently been proposed to devise algorithms for enumerating many decomposition candidates for applications to choose from using specialized, or even machine-learned, cost functions. We explore the ability to produce a large collection of 'high quality' tree decompositions. We present the first algorithm for ranked enumeration of the proper (non-redundant) tree decompositions, or equivalently minimal triangulations, under a wide class of cost functions that substantially generalizes the generic ones above. On the theoretical side, we establish the guarantee of polynomial delay if poly-MS is assumed, or if we are interested in tree decompositions of a width bounded by a constant. We describe an experimental evaluation on graphs of various domains (including join queries, Bayesian networks, treewidth benchmarks and random), and explore both the applicability of the poly-MS assumption and the performance of our algorithm relative to the state of the art.

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name: Ranked Enumeration of Minimal Triangulations


uk: Комп'ютерні науки

description: We study the problem of counting cycles in the adjacency list streaming model, fully resolving in which settings there exist sublinear space algorithms. Our main upper bound is a two-pass algorithm for estimating triangles that uses wtO(m/T^2/3) space, where m is the edge count and T is the triangle count of the graph. On the other hand, we show that no sublinear space multipass algorithm exists for counting ell-cycles for ell geq 5. Finally, we show that counting 4-cycles is intermediate: sublinear space algorithms exist in multipass but not single-pass settings.

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name: The Complexity of Counting Cycles in the Adjacency List Streaming Model


uk: Комп'ютерні науки

description: We study the enumeration complexity of Unions of Conjunctive Queries (UCQs). We aim to identify the UCQs that are tractable in the sense that the answer tuples can be enumerated with a linear preprocessing phase and a constant delay between every successive tuples. It has been established that, in the absence of self joins and under conventional complexity assumptions, the CQs that admit such an evaluation are precisely the free-connex ones. A union of tractable CQs is always tractable. We generalize the notion of free-connexity from CQs to UCQs, thus showing that some unions containing intractable CQs are, in fact, tractable. Interestingly, some unions consisting of only intractable CQs are tractable too. The question of a finding a full characterization of the tractability of UCQs remains open. Nevertheless, we prove that for several classes of queries, free-connexity fully captures the tractable UCQs.

uk: вхід без авторизації

name: On the Enumeration Complexity of Unions of Conjunctive Queries


uk: Комп'ютерні науки

description: In this work, we study two simple yet general complexity classes, based on logspace Turing machines, which provide a unifying framework for efficient query evaluation in areas like information extraction and graph databases, among others. We investigate the complexity of three fundamental algorithmic problems for these classes: enumeration, counting and uniform generation of solutions, and show that they have several desirable properties in this respect. Both complexity classes are defined in terms of nondeterministic logspace transducers (NL transducers). For the first class, we consider the case of unambiguous NL transducers, and we prove constant delay enumeration, and both counting and uniform generation of solutions in polynomial time. For the second class, we consider unrestricted NL transducers, and we obtain polynomial delay enumeration, approximate counting in polynomial time, and polynomial-time randomized algorithms for uniform generation. More specifically, we show that each problem in this second class admits a fully polynomial-time randomized approximation scheme (FPRAS) and a polynomial-time Las Vegas algorithm for uniform generation. Interestingly, the key idea to prove these results is to show that the fundamental problem #NFA admits an FPRAS, where #NFA is the problem of counting the number of strings of length n accepted by a nondeterministic finite automaton (NFA). While this problem is known to be #P-complete and, more precisely, SpanL-complete, it was open whether this problem admits an FPRAS. In this work, we solve this open problem, and obtain as a welcome corollary that every function in SpanL admits an FPRAS.

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name: Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation


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description: We give an algorithm to enumerate the results on trees of monadic second-order (MSO) queries represented by nondeterministic tree automata. After linear time preprocessing (in the input tree), we can enumerate answers with linear delay (in each answer). We allow updates on the tree to take place at any time, and we can then restart the enumeration after logarithmic time in the tree. Further, all our combined complexities are polynomial in the automaton. Our result follows our previous circuit-based enumeration algorithms based on deterministic tree automata, and is also inspired by our earlier result on words and nondeterministic sequential extended variable-set automata in the context of document spanners. We extend these results and combine them with a recent tree balancing scheme by Niewerth, so that our enumeration structure supports updates to the underlying tree in logarithmic time (with leaf insertions, leaf deletions, and node relabelings). Our result implies that, for MSO queries with free first-order variables, we can enumerate the results with linear preprocessing and constant-delay and update the underlying tree in logarithmic time, which improves on several known results for words and trees. Building on lower bounds from data structure research, we also show unconditionally that up to a doubly logarithmic factor the update time of our algorithm is optimal. Thus, unlike other settings, there can be no algorithm with constant update time.

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name: Enumeration on Trees with Tractable Combined Complexity and Efficient Updates


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description: We present a new solution for finding joinable tables in massive data lakes: given a table and one join column, find tables that can be joined with the given table on the largest number of distinct values. The problem can be formulated as an overlap set similarity search problem by considering columns as sets and matching values as intersection between sets. Although set similarity search is well-studied in the field of approximate string search (e.g., fuzzy keyword search), the solutions are designed for and evaluated over sets of relatively small size (average set size rarely much over 100 and maximum set size in the low thousands) with modest dictionary sizes (the total number of distinct values in all sets is only a few million). We observe that modern data lakes typically have massive set sizes (with maximum set sizes that may be tens of millions) and dictionaries that include hundreds of millions of distinct values. Our new algorithm, JOSIE (Joining Search using Intersection Estimation) minimizes the cost of set reads and inverted index probes used in finding the top-k sets. We show that JOSIE completely out performs the state-of-the-art overlap set similarity search techniques on data lakes. More surprising, we also consider state-of-the-art approximate algorithm and show that our new exact search algorithm performs almost as well, and even in some cases better, on real data lakes.

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name: JOSIE: Overlap Set Similarity Search for Finding Joinable Tables in Data Lakes


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description: Database administrators construct secondary indexes on data tables to accelerate query processing in relational database management systems (RDBMSs). These indexes are built on top of the most frequently queried columns according to the data statistics. Unfortunately, maintaining multiple secondary indexes in the same database can be extremely space consuming, causing significant performance degradation due to the potential exhaustion of memory space. In this paper, we demonstrate that there exist many opportunities to exploit column correlations for accelerating data access. We propose HERMIT, a succinct secondary indexing mechanism for modern RDBMSs. HERMIT judiciously leverages the rich soft functional dependencies hidden among columns to prune out redundant structures for indexed key access. Instead of building a complete index that stores every single entry in the key columns, HERMIT navigates any incoming key access queries to an existing index built on the correlated columns. This is achieved through the Tiered Regression Search Tree (TRS-Tree), a succinct, ML-enhanced data structure that performs fast curve fitting to adaptively and dynamically capture both column correlations and outliers. Our extensive experimental study in two different RDBMSs have confirmed that HERMIT can significantly reduce space consumption with limited performance overhead, especially when supporting complex range queries.

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name: Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations


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description: Data errors are ubiquitous in tables. Extensive research in this area has resulted in a rich variety of techniques, each often targeting a specific type of errors, e.g., numeric outliers, constraint violations, etc. While these diverse techniques clearly improve data quality, it places a significant burden on humans to configure these techniques with suitable rules and parameters for each data set. For example, an expert is expected to define suitable functional-dependencies between column pairs, or tune appropriate thresholds for outlier-detection algorithms, all of which are specific to one individual data set. As a result, users today often hire experts to cleanse only their high-value data sets. We propose sj, a unified framework to automatically detect diverse types of errors. Our approach employs a novel 'what-if' analysis that performs local data perturbations to reason about data abnormality, leveraging classical hypothesis-tests on a large corpus of tables. We test sj on a wide variety of tables including Wikipedia tables, and make surprising discoveries of thousands of FD violations, numeric outliers, spelling mistakes, etc., with better accuracy than existing algorithms specifically designed for each type of errors. For example, for spelling mistakes, sj outperforms the state-of-the-art spell-checker from a commercial search engine.

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name: Uni-Detect: A Unified Approach to Automated Error Detection in Tables


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description: Efficiently computing linear algebra expressions is central to machine learning (ML) systems. Most systems support sparse formats and operations because sparse matrices are ubiquitous and their dense representation can cause prohibitive overheads. Estimating the sparsity of intermediates, however, remains a key challenge when generating execution plans or performing sparse operations. These sparsity estimates are used for cost and memory estimates, format decisions, and result allocation. Existing estimators tend to focus on matrix products only, and struggle to attain good accuracy with low estimation overhead. However, a key observation is that real-world sparse matrices commonly exhibit structural properties such as a single non-zero per row, or columns with varying sparsity. In this paper, we introduce MNC (Matrix Non-zero Count), a remarkably simple, count-based matrix synopsis that exploits these structural properties for efficient, accurate, and general sparsity estimation. We describe estimators and sketch propagation for realistic linear algebra expressions. Our experiments - on a new estimation benchmark called SparsEst - show that the MNC estimator yields good accuracy with very low overhead. This behavior makes MNC practical and broadly applicable in ML systems.

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name: MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions


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description: Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by statistical anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they require specification of the underlying causal models. In this paper, we formalize the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model. We show that these conditions correctly capture subtle fairness violations. We then use these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels. We evaluate our algorithms on real data, demonstrating improvement over the state of the art on multiple fairness metrics proposed in the literature while retaining high utility.

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name: Interventional Fairness : Causal Database Repair for Algorithmic Fairness


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description: Oblivious RAM (ORAM) and private information retrieval (PIR) are classic cryptographic primitives used to hide the access pattern to data whose storage has been outsourced to an untrusted server. Unfortunately, both primitives require considerable overhead compared to plaintext access. For large-scale storage infrastructure with highly frequent access requests, the degradation in response time and the exorbitant increase in resource costs incurred by either ORAM or PIR prevent their usage. In an ideal scenario, a privacy-preserving storage protocols with small overhead would be implemented for these heavily trafficked storage systems to avoid negatively impacting either performance and/or costs. In this work, we study the problem of the best storage access privacy that is achievable with only small overhead over plaintext access. To answer this question, we consider differential privacy access which is a generalization of the oblivious access security notion that are considered by ORAM and PIR. Quite surprisingly, we present strong evidence that constant overhead storage schemes may only be achieved with privacy budgets of epsilon = Omega(log n). We present asymptotically optimal constructions for differentially private variants of both ORAM and PIR with privacy budgets epsilon = Theta(log n) with only O(1) overhead. In addition, we consider a more complex storage primitive called key-value storage in which data is indexed by keys from a large universe (as opposed to consecutive integers in ORAM and PIR). We present a differentially private key-value storage scheme with epsilon = Theta(log n) and O(loglog n) overhead. This construction uses a new oblivious, two-choice hashing scheme that may be of independent interest.

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name: What Storage Access Privacy is Achievable with Small Overhead?


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description: Data provenance is a powerful tool for debugging large-scale analytics on batch processing systems. This paper presents Ariadne, a system for capturing and querying provenance from Vertex-Centric graph processing systems. While the size of provenance from map-reduce-style workflows is often a fraction of the input data size, graph algorithms iterate over the input graph many times, producing provenance much larger than the input graph. And though current provenance tracing procedures support explicit debugging scenarios, like crash-culprit determination, developers are increasingly interested in the behavior of analytics when a crash or exception does not occur. To address this challenge, Ariadne offers developers a concise declarative query language to capture and query graph analytics provenance. Exploiting the formal semantics of this datalog-based language, we identify useful query classes that can run while an analytic computes. Experiments with various analytics and real-world datasets show the overhead of online querying is 1.3x over the baseline vs. 8x for the traditional approach. These experiments also illustrate how Ariadne's query language supports execution monitoring and performance optimization for graph analytics.

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name: Ariadne: Online Provenance for Big Graph Analytics


uk: Комп'ютерні науки

description: Deep Convolutional Neural Networks (CNNs) now match human accuracy in many image prediction tasks, resulting in a growing adoption in e-commerce, radiology, and other domains. Naturally, explaining CNN predictions is a key concern for many users. Since the internal workings of CNNs are unintuitive for most users, occlusion-based explanations (OBE) are popular for understanding which parts of an image matter most for a prediction. One occludes a region of the image using a patch and moves it around to produce a heat map of changes to the prediction probability. Alas, this approach is computationally expensive due to the large number of re-inference requests produced, which wastes time and raises resource costs. We tackle this issue by casting the OBE task as a new instance of the classical incremental view maintenance problem. We create a novel and comprehensive algebraic framework for incremental CNN inference combining materialized views with multi-query optimization to reduce computational costs. We then present two novel approximate inference optimizations that exploit the semantics of CNNs and the OBE task to further reduce runtimes. We prototype our ideas in Python to create a tool we call Krypton that supports both CPUs and GPUs. Experiments with real data and CNNs show that Krypton reduces runtimes by up to 5X (resp. 35X) to produce exact (resp. high-quality approximate) results without raising resource requirements.

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name: Incremental and Approximate Inference for Faster Occlusion-based Deep CNN Explanations


uk: Комп'ютерні науки

description: We consider variations of set reconciliation problems where two parties, Alice and Bob, each hold a set of points in a metric space, and the goal is for Bob to conclude with a set of points that is close to Alice's set of points in a well-defined way. This setting has been referred to as robust set reconciliation. In one variation, the goal is for Bob to end with a set of points that is close to Alice's in earth mover's distance, and in another the goal is for Bob to have a point that is close to each of Alice's. The first problem has been studied before; while previous results achieved an O(d) approximation, where d is the dimension of the space, we achieve an O(log n) approximation, where n is the number of points. The second problem appears new, and here we find schemes that, under certain conditions, use sublinear communication. Our primary novelty is utilizing Invertible Bloom Lookup Tables in combination with locality sensitive hashing. This combination allows us to cope with the geometric setting in a communication-efficient manner.

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name: Robust Set Reconciliation via Locality Sensitive Hashing


uk: Комп'ютерні науки

description: Provenance and intervention-based techniques have been used to explain surprisingly high or low outcomes of aggregation queries. However, such techniques may miss interesting explanations emerging from data that is not in the provenance. For instance, an unusually low number of publications of a prolific researcher in a certain venue and year can be explained by an increased number of publications in another venue in the same year. We present a novel approach for explaining outliers in aggregation queries through counter- balancing. That is, explanations are outliers in the opposite direction of the outlier of interest. Outliers are defined w.r.t. patterns that hold over the data in aggregate. We present efficient methods for mining such aggregate regression pat- terns (ARPs), discuss how to use ARPs to generate and rank explanations, and experimentally demonstrate the efficiency and effectiveness of our approach.

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name: Going Beyond Provenance: Explaining Query Answers with Pattern-based Counterbalances


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description: For testing the correctness of SQL queries, a standard practice is to execute the query in question on some test database instance and compare its result with that of the correct query. Given two queries Q_1 and Q_2, we say that a database instance D is a counterexample (for Q_1 and Q_2) if Q_1(D) differs from Q_2(D); such a counterexample can serve as an explanation of why Q_1 and Q_2 are not equivalent. While the test database instance may serve as a counterexample, it may be too large or complex to understand where the inequivalence arises. Therefore, in this paper, given a known counterexample D for Q_1 and Q_2, we aim to find the smallest counterexample D' subseteq D where Q_1(D') neq Q_2(D'). The problem in general is NP-hard. Drawing techniques from provenance and constraint solving, we develop a suite of algorithms for finding small counterexamples for different classes of queries, including those involving negation and aggregation. We evaluate the effectiveness and scalability of our algorithms on student queries from an undergraduate database course, and on queries from the TPC-H benchmark. We also report a user study from the course where we deployed our tool to help students with an assignment on relational algebra.

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name: Explaining Wrong Queries Using Small Examples


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description: Detecting erroneous values is a key step in data cleaning. Error detection algorithms usually require a user to provide input configurations in the form of rules or statistical parameters. However, providing a complete, yet correct, set of configurations for each new dataset is not trivial, as the user has to know about both the dataset and the error detection algorithms upfront. In this paper, we present Raha, a new configuration-free error detection system. By generating a limited number of configurations for error detection algorithms that cover various types of data errors, we can generate an expressive feature vector for each tuple value. Leveraging these feature vectors, we propose a novel sampling and classification scheme that effectively chooses the most representative values for training. Furthermore, our system can exploit historical data to filter out irrelevant error detection algorithms and configurations. In our experiments, Raha outperforms the state-of-the-art error detection techniques with no more than 20 labeled tuples on each dataset.

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name: Raha: A Configuration-Free Error Detection System


uk: Комп'ютерні науки

description: Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potentially leads to a loss of sellers' revenue and buyers' affordability and efficiency. In this paper, we propose a model-based pricing (MBP) framework, which instead of pricing the data, directly prices ML model instances. We first formally describe the desired properties of the MBP framework, with a focus on avoiding arbitrage. Next, we show a concrete realization of the MBP framework via a noise injection approach, which provably satisfies the desired formal properties. Based on the proposed framework, we then provide algorithmic solutions on how the seller can assign prices to models under different market scenarios (such as to maximize revenue). Finally, we conduct extensive experiments, which validate that the MBP framework can provide high revenue to the seller, high affordability to the buyer, and also operate on low runtime cost.

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name: Towards Model-based Pricing for Machine Learning in a Data Marketplace


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description: Data analytics often involves hypothetical reasoning: repeatedly modifying the data and observing the induced effect on the computation result of a data-centric application. Previous work has shown that fine-grained data provenance can help make such an analysis more efficient: instead of a costly re-execution of the underlying application, hypothetical scenarios are applied to a pre-computed provenance expression. However, storing provenance for complex queries and large-scale data leads to a significant overhead, which is often a barrier to the incorporation of provenance-based solutions. To this end, we present a framework that allows to reduce provenance size. Our approach is based on reducing the provenance granularity using user defined abstraction trees over the provenance variables; the granularity is based on the anticipated hypothetical scenarios. We formalize the tradeoff between provenance size and supported granularity of the hypothetical reasoning, and study the complexity of the resulting optimization problem, provide efficient algorithms for tractable cases and heuristics for others. We experimentally study the performance of our solution for various queries and abstraction trees. Our study shows that the algorithms generally lead to substantial speedup of hypothetical reasoning, with a reasonable loss of accuracy.

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name: Hypothetical Reasoning via Provenance Abstraction


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description: State-of-the-art index tuners rely on query optimizer's cost estimates to search for the index configuration with the largest estimated execution cost improvement`. Due to well-known limitations in optimizer's estimates, in a significant fraction of cases, an index estimated to improve a query's execution cost, e.g., CPU time, makes that worse when implemented. Such errors are a major impediment for automated indexing in production systems. We observe that comparing the execution cost of two plans of the same query corresponding to different index configurations is a key step during index tuning. Instead of using optimizer's estimates for such comparison, our key insight is that formulating it as a classification task in machine learning results in significantly higher accuracy. We present a study of the design space for this classification problem. We further show how to integrate this classifier into the state-of-the-art index tuners with minimal modifications, i.e., how artificial intelligence (AI) can benefit automated indexing (AI). Our evaluation using industry-standard benchmarks and a large number of real customer workloads demonstrates up to 5x reduction in the errors in identifying the cheaper plan in a pair, which eliminates almost all query execution cost regressions when the model is used in index tuning.

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name: AI Meets AI: Leveraging Query Executions to Improve Index Recommendations


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description: In the era of big data, computing exact answers to analytical queries becomes prohibitively expensive. This greatly increases the value of approaches that can compute efficiently approximate, but highly-accurate, answers to analytical queries. Alas, the state of the art still suffers from many shortcomings: Errors are still high unless large memory investments are made. Many important analytics tasks are not supported. Query response times are too long and thus approaches rely on parallel execution of queries atop large big data analytics clusters, in-situ or in the cloud, whose acquisition/use costs dearly. Hence, the following questions are crucial: Can we develop AQP engines that reduce response times by orders of magnitude, ensure high accuracy, and support most aggregate functions? With smaller memory footprints and small overheads to build the state upon which they are based? With this paper, we show that the answers to all questions above can be positive. The paper presents DBEst, a system based on Machine Learning models (regression models and probability density estimators). It will discuss its limitations, promises, and how it can complement existing systems. It will substantiate its advantages using queries and data from the TPC-DS benchmark and real-life datasets, compared against state of the art AQP engines.

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name: DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models