Worst-Case Optimal Graph Joins in Almost No Space

Arroyuelo, Diego; Hogan, Aidan; Navarro, Gonzalo; Reutter, Juan L.; Rojas-Ledesma, Javiel; Soto, Adrián

doi:10.1145/3448016.3457256

Cited by 21 publications

(4 citation statements)

References 23 publications

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“…However, Aluç et al [7] and Saleem et al [83] find the queries of these benchmarks to often be too narrow and simplistic. Building benchmarks from real-world queries can help tune implementations and guide research towards better support for the types of queries most commonly encountered in practical settings [13,16,62,65,79,101]. Yet another use case is caching [50,54,100].…”

Section: Stadler Et Al / Lsq 20: a Linked Dataset Of Sparql Query Logsmentioning

confidence: 99%

“…2(b), we present Lorenz curves for the number of executions per client and per query, respectively. 13 We present results for Bio2RDF together as one series to ensure better readability. In general, we see a skew in the graph away from the equality curve towards the bottom-left corner, meaning that a small number of clients/queries are involved in a large number of executions.…”

Section: Provenance: Executions and Agentsmentioning

confidence: 99%

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LSQ 2.0: A linked dataset of SPARQL query logs

Stadler

Saleem

Mehmood

et al. 2024

Self Cite

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We present the Linked SPARQL Queries (LSQ) dataset, which currently describes 43.95 million executions of 11.56 million unique SPARQL queries extracted from the logs of 27 different endpoints. The LSQ dataset provides RDF descriptions of each such query, which are indexed in a public LSQ endpoint, allowing interested parties to find queries with the characteristics they require. We begin by describing the use cases envisaged for the LSQ dataset, which include applications for research on common features of queries, for building custom benchmarks, and for designing user interfaces. We then discuss how LSQ has been used in practice since the release of four initial SPARQL logs in 2015. We discuss the model and vocabulary that we use to represent these queries in RDF. We then provide a brief overview of the 27 endpoints from which we extracted queries in terms of the domain to which they pertain and the data they contain. We provide statistics on the queries included from each log, including the number of query executions, unique queries, as well as distributions of queries for a variety of selected characteristics. We finally discuss how the LSQ dataset is hosted and how it can be accessed and leveraged by interested parties for their use cases.

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Section: Stadler Et Al / Lsq 20: a Linked Dataset Of Sparql Query Logsmentioning

confidence: 99%

Section: Provenance: Executions and Agentsmentioning

confidence: 99%

LSQ 2.0: A linked dataset of SPARQL query logs

Stadler

Saleem

Mehmood

et al. 2024

Self Cite

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“…Worst-case optimal multi-way algorithms [26] have recently gained a lot of attention (e.g., [3,27,28,29]) and have demonstrated high performance in evaluating graph pattern queries [27,29,30]. Such algorithms satisfy the AGM bound [31] and their runtime matches the worst-case size of the result of the input query [23,27].…”

Section: Worst-case Optimal Multi-way Join Algorithmsmentioning

confidence: 99%

Native Execution of GraphQL Queries over RDF Graphs Using Multi-Way Joins

Karalis,

Bigerl,

Ngonga Ngomo

2023

Knowledge Graphs: Semantics, Machine Learning, and Languages

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Purpose: The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database communities and is now often used as a means to query knowledge graphs. Most of the storage solutions that support GraphQL rely on a translation layer to map the said language to another query language that they support natively, for example SPARQL. Methodology: Our main innovation is a multi-way left-join algorithm inspired by worst-case optimal multi-way join algorithms. This novel algorithm enables the native execution of GraphQL queries over RDF knowledge graphs. We evaluate our approach in two settings using the LinGBM benchmark generator. Findings: The experimental results suggest that our solution outperforms the state-of-the-art graph storage solution for GraphQL with respect to both query runtimes and scalability. Value: Our solution is implemented in an open-sourced triple store, and is intended to advance the development of representation-agnostic storage solutions for knowledge graphs.

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“…Approaches suitable for large graphs typically build up the set of matchings in a relational table [32] by a series of natural joins over the edge relation; the aim is then to find an optimal join order. Until recently, the best-performing approaches were based on edgegrowing pairwise join plans [31,49,51], but a new family of vertex-growing plans, known as worst-case optimal joins, have emerged [5,42,54], with better performance for cyclic patterns such as triangles. While we use the latter approach and implement our algorithms using relational operators, any method capable of finding matchings on a static graph can be combined with our timed automaton-based algorithms.…”

Section: Related Workmentioning

confidence: 99%

Temporal graph patterns by timed automata

Aghasadeghi¹,

Bussche²,

Stoyanovich³

2022

Preprint

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Temporal graphs represent graph evolution over time, and have been receiving considerable research attention. Work on expressing temporal graph patterns or discovering temporal motifs typically assumes relatively simple temporal constraints, such as journeys or, more generally, existential constraints, possibly with finite delays. In this paper we propose to use timed automata to express temporal constraints, leading to a general and powerful notion of temporal basic graph pattern (BGP).The new difficulty is the evaluation of the temporal constraint on a large set of matchings. An important benefit of timed automata is that they support an iterative state assignment, which can be useful for early detection of matches and pruning of non-matches. We introduce algorithms to retrieve all instances of a temporal BGP match in a graph, and present results of an extensive experimental evaluation, demonstrating interesting performance trade-offs. We show that an on-demand algorithm that processes total matchings incrementally over time is preferable when dealing with cyclic patterns on sparse graphs. On acyclic patterns or dense graphs, and when connectivity of partial matchings can be guaranteed, the best performance is achieved by maintaining partial matchings over time and allowing automaton evaluation to be fully incremental. The code and datasets are available at http: //github.com/amirpouya/TABGP.

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Worst-Case Optimal Graph Joins in Almost No Space

Cited by 21 publications

References 23 publications

LSQ 2.0: A linked dataset of SPARQL query logs

LSQ 2.0: A linked dataset of SPARQL query logs

Native Execution of GraphQL Queries over RDF Graphs Using Multi-Way Joins

Temporal graph patterns by timed automata

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