Querying Regular Graph Patterns

Barceló, Pablo; Libkin, Leonid; Reutter, Juan L.

doi:10.1145/2559905

Cited by 43 publications

(34 citation statements)

References 69 publications

(70 reference statements)

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“…In this graph, the nodes are the authors, and an edge between two nodes describes the authors' relationship (e.g., whether they have the same political opinion) [34]. Substantial research has been devoted to the e ective processing of graph queries, including reachability [16], shortest paths [19], frequent subgraphs [32], and graph pa erns [13].…”

Section: Introductionmentioning

confidence: 99%

An Indexing Framework for Queries on Probabilistic Graphs

Maniu

Cheng

Senellart

2017

ACM Trans. Database Syst.

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Information in many applications, such as mobile wireless systems, social networks, and road networks, is captured by graphs. In many cases, such information is uncertain. We study the problem of querying a probabilistic graph, in which vertices are connected to each other probabilistically. In particular, we examine "source-to-target" queries (or ST-queries), such as computing the shortest path between two vertices. e major di erence with the deterministic se ing is that query answers are enriched with probabilistic annotations. Evaluating ST-queries over probabilistic graphs is #P-hard, as it requires examining an exponential number of "possible worlds" -database instances generated from the probabilistic graph. Existing solutions to the ST-query problem, which sample possible worlds, have two downsides: (i) a possible world can be very large and (ii) many samples are needed for reasonable accuracy. To tackle these issues, we study the ProbTree, a data structure that stores a succinct, or indexed, version of the possible worlds of the graph. Existing ST-query solutions are executed on top of this structure, with the number of samples and sizes of the possible worlds reduced. We examine lossless and lossy methods for generating the ProbTree, which re ect the trade-o between the accuracy and e ciency of query evaluation. We analyze the correctness and complexity of these approaches. Our extensive experiments on real datasets show that the ProbTree is fast to generate and small in size. It also enhances the accuracy and e ciency of existing ST-query algorithms signi cantly.

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Section: Introductionmentioning

confidence: 99%

An Indexing Framework for Queries on Probabilistic Graphs

Maniu

Cheng

Senellart

2017

ACM Trans. Database Syst.

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“…This representation exhibits a duality between formulae and objects: that is, positive and negative theories of query answers can be viewed as set of conventional tuples that use null values. With this duality, we define query answers using (6). To check that the definition makes sense, we have to make sure that it preserves informativeness.…”

Section: Representation Of Relational Query Answers: Incomplete Tuplesmentioning

confidence: 99%

“…This definition has been so dominant in the literature that even in models where queries do not return sets, languages have been adjusted to make this definition applicable (e.g., for XML and graph data [3,5,6]). …”

Section: Introductionmentioning

confidence: 99%

Negative Knowledge for Certain Query Answers

Libkin

2016

Web Reasoning and Rule Systems

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Abstract. Querying incomplete data usually amounts to finding answers we are certain about. Standard approaches concentrate on positive information about query answers, and miss negative knowledge, which can be useful for two reasons. First, sometimes it is the only type of knowledge one can infer with certainty, and second, it may help one find good and efficient approximations of positive certain answers. Our goal is to consider a framework for defining both positive and negative certain knowledge about query answers and to show two applications of it. First, we demonstrate that it naturally leads to a way of representing certain information that has hitherto not been used in querying incomplete databases. Second, we show that approximations of such certain information can be computed efficiently for all first-order queries over relational databases.

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“…As discussed in the introduction, logical languages for XML and graph data are largely based on patterns (either tree or graph patterns) [3,7,9,13,23,36]. Such a pattern is a small subtree or a subgraph that needs to have a match in the large database.…”

Section: Patterns and Local Auxiliary Relationsmentioning

confidence: 99%

“…These often provide the basis for language constructors, for instance, for the navigational language XPath [35,36], more general XML query languages [3,13,17], or path queries over graphs [7,23]. Patterns also provide a standard abstraction of incomplete data in relational, XML, and graph models [1,2,8,9,29].…”

Section: Introductionmentioning

confidence: 99%

Pattern logics and auxiliary relations

Figueira

Libkin

2014

Proceedings of the Joint Meeting of the Twenty-Third EACSL Annual Conference on Computer Science Logic (CSL) and the Twenty-Nin

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A common theme in the study of logics over finite structures is adding auxiliary predicates to enhance expressiveness and convey additional information. Examples include adding an order or arithmetic for capturing complexity classes, or the power of real-life declarative languages. A recent trend is to add a data-value comparison relation to words, trees, and graphs, for capturing modern data models such as XML and graph databases.Such additions often result in the loss of good properties of the underlying logic. Our goal is to show that such a loss can be avoided if we use pattern-based logics, standard in XML and graph data querying. The essence of such logics is that auxiliary relations are tested locally with respect to other relations in the structure. These logics are shown to admit strong versions of Hanf and Gaifman locality theorems, which are used to prove a homomorphism preservation theorem, and a decidability result for the satisfiability problem. We discuss applications of these results to pattern logics over data forests, and consequently to querying XML data.

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Querying Regular Graph Patterns

Cited by 43 publications

References 69 publications

An Indexing Framework for Queries on Probabilistic Graphs

An Indexing Framework for Queries on Probabilistic Graphs

Negative Knowledge for Certain Query Answers

Pattern logics and auxiliary relations

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