Threshold queries in theory and in the wild

Bonifati, Angela; Dumbrava, Stefania; Fletcher, George H. L.; Hidders, Jan; Hofer, Matthias; Martens, Wim; Murlak, Filip; Shinavier, Joshua; Staworko, Sławek; Tomaszuk, Dominik

doi:10.14778/3510397.3510407

Cited by 9 publications

(6 citation statements)

References 56 publications

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“…The answer is yes, where a number of restricted fragments of SPARQL queries have been identified that are less computationally costly for important tasks. These fragments include well-designed queries that use the OPTIONAL clause in restricted ways [21,67], queries with low treewidth [21] whose structure is close to that of a tree, queries such as simple transitive expressions [58] or (certain fragments of) simple conjunctive regular path queries [36] where only restricted use of Kleene star ( * ) is allowed in path expressions, certain types of simple conjunctive regular path queries where disjunction (|) is not allowed inside Kleene star, and threshold queries that limit the number of results returned [20]. Studies of SPARQL query logs have shown that these fragments cover many of the queries seen in practice [24,58], where query logs help to bridge the theory and practice of SPARQL [59].…”

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

confidence: 99%

“…UC2 SPARQL Adoption Various works have analysed SPARQL query logs in order to understand how features of the SPARQL standard are used "in the wild" as well as to extract structural properties of real-world queries [12,21,23,24,57,68,72]. In turn, this family of works has led to the definition of tractable fragments of queries that are common in practice [20,58]. LSQ can facilitate further research on the use of SPARQL in the wild as it compiles logs from different domains.…”

Section: Uc1 Custom Benchmarksmentioning

confidence: 99%

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LSQ 2.0: A linked dataset of SPARQL query logs

Stadler

Saleem

Mehmood

et al. 2024

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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: Uc1 Custom Benchmarksmentioning

confidence: 99%

LSQ 2.0: A linked dataset of SPARQL query logs

Stadler

Saleem

Mehmood

et al. 2024

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“…The width of a conjunctive query Q is the minimal width of an equivalent project-join expression. This notion can be equivalently defined in terms of tree decompositions, and coincides (up to 1) with a variant of tree-width suitable for non-boolean conjunctive queries (some bag must contain all answer columns) [7]. In the running example, the chosen expression has width 3 (and it is optimal), so we only get a cubic bound.…”

Section: Evaluation In Theorymentioning

confidence: 99%

Threshold Queries

et al. 2023

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Threshold queries are an important class of queries that only require computing or counting answers up to a specified threshold value. To the best of our knowledge, threshold queries have been largely disregarded in the research literature, which is surprising considering how common they are in practice. We explore how such queries appear in practice and present a method that can be used to significantly improve the asymptotic bounds of their state-of-the-art evaluation algorithms. Our experimental evaluation of these methods shows order-of-magnitude performance improvements.

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“…Work has also been done in the direction of achieving instance-optimality [10,86,101]. Bonifati et al [27] explore the impact of thresholds (i.e., LIMIT clauses) in query evaluation. Our approach can leverage prior work on decompositions to obtain the top-ranked answer in the same time bound as the Boolean CQ.…”

Section: Related Workmentioning

confidence: 99%

Any-k Algorithms for Enumerating Ranked Answers to Conjunctive Queries

Tziavelis¹,

Gatterbauer²,

Riedewald³

2022

Preprint

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We study ranked enumeration for Conjunctive Queries (CQs) where the answers are ordered by a given ranking function (e.g., an ORDER BY clause in SQL). We develop "any-k" algorithms which, without knowing the number 𝑘 of desired answers, push the ranking into joins and avoid materializing the join output earlier than necessary. For this to be possible, the ranking function needs to obey a certain kind of monotonicity; the supported ranking functions include the common sum-of-weights case where query answers are compared by sums of input weights, as well as any commutative selective dioid. One core insight of our work is that the problem is closely related to the fundamental task of path enumeration in a weighted DAG. We generalize and improve upon classic research on finding the 𝑘 th shortest path and unify into the same framework several solutions from different areas that had been studied in isolation. For the time to the 𝑘 th ranked CQ answer (for every value of 𝑘), our approach is optimal in data complexity precisely for the same class of queries where unranked enumeration is optimal-and only slower by a logarithmic factor. In a more careful analysis of combined complexity, we uncover a previously unknown tradeoff between two different any-k algorithms: one has lower complexity when the number of returned results is small, the other when the number is very large. This tradeoff is eliminated under a stricter monotonicity property that we exploit to design a novel algorithm that asymptotically dominates all previously known alternatives, including the well-known algorithm of Eppstein for sum-of-weights path enumeration. We empirically demonstrate the findings of our theoretical analysis in an experimental study that highlights the superiority of our approach over the join-then-rank approach that existing database systems typically follow.

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Threshold queries in theory and in the wild

Cited by 9 publications

References 56 publications

LSQ 2.0: A linked dataset of SPARQL query logs

LSQ 2.0: A linked dataset of SPARQL query logs

Threshold Queries

Any-k Algorithms for Enumerating Ranked Answers to Conjunctive Queries

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