Using Graph Summarization for Join-Ahead Pruning in a Distributed RDF Engine

Gurajada, Sairam; Seufert, Stephan; Miliaraki, Iris; Theobald, Martin

doi:10.1145/2630602.2630610

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Nodes and edges are summarized according to their frequencies and/or based on ontology patterns in [28,33]. Summaries where nodes are grouped by graph clustering [20], user-defined aggregation rules [34], mining [9], and identification of frequent subtrees [39] do not reflect the complete structure, and/or require user input. With different objectives, these summaries may omit part of the graph structure, or be much too large for visualization.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

RDF graph summarization for first-sight structure discovery

2020

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To help users get familiar with large RDF graphs, RDF summarization techniques can be used. In this work, we study quotient summaries of RDF graphs, that is: graph summaries derived from a notion of equivalence among RDF graph nodes. We make the following contributions: (i) four novel summaries which are often small and easy-to-comprehend, in the style of E-R diagrams; (ii) efficient (amortized linear-time) algorithms for computing these summaries either from scratch, or incrementally, reflecting additions to the graph; (iii) the first formal study of the interplay between RDF graph saturation in the presence of an RDFS ontology, and summarization; we provide a sufficient condition for a highly efficient shortcut method to build the quotient summary of a graph without saturating it; (iv) formal results establishing the shortcut conditions for some of our summaries and others from the literature; (v) experimental validations of our claim within a tool available online.

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Section: Related Work and Conclusionmentioning

confidence: 99%

RDF graph summarization for first-sight structure discovery

2020

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“…The architecture of our SPARQL 1.1 engine (in the following coined TriAD*) is based on TriAD [11,12], which we originally developed for processing conjunctions of triple patterns in SPARQL 1.0. The design of TriAD in principle follows a classical master-slave architecture at indexing time, but allows for a direct, asynchronous communication among the slaves at query-processing time.…”

Section: Architecturementioning

confidence: 99%

Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1

Gurajada,

Theobald

2016

Preprint

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We propose an efficient and scalable architecture for processing generalized graph-pattern queries as they are specified by the current W3C recommendation of the SPARQL 1.1 "Query Language" component. Specifically, the class of queries we consider consists of sets of SPARQL triple patterns with labeled property paths. From a relational perspective, this class resolves to conjunctive queries of relational joins with additional graph-reachability predicates. For the scalable, i.e., distributed, processing of this kind of queries over very large RDF collections, we develop a suitable partitioning and indexing scheme, which allows us to shard the RDF triples over an entire cluster of compute nodes and to process an incoming SPARQL query over all of the relevant graph partitions (and thus compute nodes) in parallel. Unlike most prior works in this field, we specifically aim at the unified optimization and distributed processing of queries consisting of both relational joins and graph-reachability predicates. All communication among the compute nodes is established via a proprietary, asynchronous communication protocol based on the Message Passing Interface.

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“…To overcome bisimulation issues, [5] suggests locality-based summaries, whose generation requires removing the (many) triples whose objects are literals from the input graph. Our summaries represent the queries over these triples as well.…”

Section: Related Workmentioning

confidence: 99%

Query-oriented summarization of RDF graphs

2015

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The Resource Description Framework (RDF) is a graph-based data model promoted by the W3C as the standard for Semantic Web applications. Its associated query language is SPARQL. RDF graphs are often large and varied , produced in a variety of contexts, e.g., scientific applications, social or online media, government data etc. They are heterogeneous , i.e., resources described in an RDF graph may have very different sets of properties. An RDF resource may have: no types, one or several types (which may or may not be related to each other). RDF Schema (RDFS) information may optionally be attached to an RDF graph, to enhance the description of its resources. Such statements also entail that in an RDF graph, some data is implicit. According to the W3C RDF and SPARQL specification, the semantics of an RDF graph comprises both its explicit and implicit data ; in particular, SPARQL query answers must be computed reflecting both the explicit and implicit data. These features make RDF graphs complex, both structurally and conceptually. It is intrinsically hard to get familiar with a new RDF dataset, especially if an RDF schema is sparse or not available at all.

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Using Graph Summarization for Join-Ahead Pruning in a Distributed RDF Engine

Cited by 6 publications

References 15 publications

RDF graph summarization for first-sight structure discovery

RDF graph summarization for first-sight structure discovery

Distributed Processing of Generalized Graph-Pattern Queries in SPARQL 1.1

Query-oriented summarization of RDF graphs

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