Regular Path Query Evaluation on Streaming Graphs

Pacaci, Anil; Bonifati, Angela; Özsu, M. Tamer

doi:10.1145/3318464.3389733

Cited by 41 publications

(32 citation statements)

References 54 publications

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“…Since current streaming processing technologies are fairly simple, for instance aggregations and projections as in industrial graph processing libraries (such as Gelly on Apache Flink), the need for "complex graph data streams" is evident, along with more advanced graph analytics and ML ad hoc operators. Another research challenge is to identify the graph-query processing operators that can be evaluated on dynamic and streaming graphs while taking into account recursive operators 7,23 and pathoriented semantics, as needed for standard query languages such as GQL and G-Core. 4 Graph processing platforms are also dynamic; discovering, understanding, and controlling the dynamic phenomena that occur in complex graph processing ecosystems is an open challenge.…”

Section: Resource Managersmentioning

confidence: 99%

The future is big graphs

Sakr

Bonifati

Voigt³

et al. 2021

Commun. ACM

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Section: Resource Managersmentioning

confidence: 99%

The future is big graphs

Sakr

Bonifati

Voigt³

et al. 2021

Commun. ACM

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“…It is currently not clear which of these semantics is preferable in practice. Whereas the arbitrary path semantics has a lower evaluation complexity [62], simple path and trail semantics avoid issues with infinitely many results. Furthermore, arbitrary path semantics may lead to some issues when used for evaluating the queries within key constraints.…”

Section: Regular Path Queriesmentioning

confidence: 99%

PG-Keys: Keys for Property Graphs

Angles

Bonifati

Dumbrava

et al. 2021

Proceedings of the 2021 International Conference on Management of Data

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We report on a community effort between industry and academia to shape the future of property graph constraints. The standardization for a property graph query language is currently underway through the ISO Graph Query Language (GQL) project. Our position is that this project should pay close attention to schemas and constraints, and should focus next on key constraints.The main purposes of keys are enforcing data integrity and allowing the referencing and identifying of objects. Motivated by use cases from our industry partners, we argue that key constraints should be able to have different modes, which are combinations of basic restriction that require the key to be exclusive, mandatory, and singleton. Moreover, keys should be applicable to nodes, edges, and properties since these all can represent valid real-life entities. Our result is PG-Keys, a flexible and powerful framework for defining key constraints, which fulfills the above goals.PG-Keys is a design by the Linked Data Benchmark Council's Property Graph Schema Working Group, consisting of members from industry, academia, and ISO GQL standards group, intending to bring the best of all worlds to property graph practitioners. PG-Keys aims to guide the evolution of the standardization efforts towards making systems more useful, powerful, and expressive.

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“…The latter compares the performance of the method using the compressed index and the one using the uncompressed index. Pacaci et al [27] focused on the evaluation over streaming graphs. All of these existing methods did not consider redundant and useless operations and focused only on single RPQ evaluation but did not consider evaluation of multiple RPQs except for Tetzel et al [26].…”

Section: Related Workmentioning

confidence: 99%

Regular Path Query Evaluation Sharing a Reduced Transitive Closure Based on Graph Reduction

Na¹,

Yi²,

Whang³

et al. 2021

Preprint

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Regular path queries (RPQs) find pairs of vertices of paths satisfying given regular expressions on an edge-labeled, directed multigraph. When evaluating an RPQ, the evaluation of a Kleene closure (i.e., Kleene plus or Kleene star) is very expensive. Furthermore, when multiple RPQs include a Kleene closure as a common sub-query, repeated evaluations of the common sub-query cause serious performance degradation. In this paper, we present a novel concept of RPQ-based graph reduction, which significantly simplifies the original graph through edge-level and vertex-level reductions. Interestingly, RPQ-based graph reduction can replace the evaluation of the Kleene closure on the large original graph to that of the transitive closure to the small reduced graph. We then propose a reduced transitive closure (RTC) as a lightweight structure for efficiently sharing the result of a Kleene closure. We also present an RPQ evaluation algorithm, RTCSharing, which treats each clause in the disjunctive normal form of the given RPQ as a batch unit. If the batch units include a Kleene closure as a common sub-query, we share the lightweight RTC instead of the heavyweight result of the Kleene closure. RPQ-based graph reduction further enables us to formally represent the result of an RPQ including a Kleene closure as a relational algebra expression including the RTC. Through the formal expression, we optimize the evaluation of the batch unit by eliminating useless and redundant operations of the previous method. Experiments show that RTCSharing improves the performance significantly by up to 8.86 times compared with existing methods in terms of query response time.

show abstract

Regular Path Query Evaluation on Streaming Graphs

Cited by 41 publications

References 54 publications

The future is big graphs

The future is big graphs

PG-Keys: Keys for Property Graphs

Regular Path Query Evaluation Sharing a Reduced Transitive Closure Based on Graph Reduction

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