Efficient provenance tracking for datalog using top-k queries

Deutch, Daniel; Gilad, Amir; Moskovitch, Yuval

doi:10.1007/s00778-018-0496-7

Cited by 9 publications

(11 citation statements)

References 43 publications

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“…as well as to Datalog programs used in previous works [5]. The performance of the implementation competes with dedicated solutions specific to graph databases [25].…”

Section: Xx:3mentioning

confidence: 99%

“…In cases where keeping the full provenance of a program (how-provenance) is still prohibitively large, [5,4] propose to select only a relevant subset of such trees using selection criteria based on tree patterns and ranking over rules and facts occurring in the derivation. First, given a Datalog program P and a pattern q, an offline instrumentation is performed, leading to an instrumented program P q .…”

Section: Related Workmentioning

confidence: 99%

“…Our solution can be seen as a hybrid of the ideas introduced in [16] and [5]. We generalize the semi-naïve evaluation to a specific class of semirings in order to achieve a more efficient algorithm, one that can be used in practical real-world scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…A work of note that does come with an implementation is [4,5], concerned about the computation of a relevant subset of provenance information through selection criteria based on tree patterns and the ranking of rules and facts, when the computation of the full provenance (how-provenance) is unfeasible in practice.…”

Section: Introductionmentioning

confidence: 99%

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A Practical Dynamic Programming Approach to Datalog Provenance Computation

Ramusat¹,

Maniu²,

Senellart³

2021

Preprint

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We establish a translation between a formalism for dynamic programming over hypergraphs and the computation of semiring-based provenance for Datalog programs. The benefit of this translation is a new method for computing provenance for a specific class of semirings. Theoretical and practical optimizations lead to an efficient implementation using Soufflé, a state-of-the-art Datalog interpreter. Experimental results on real-world data suggest this approach to be efficient in practical contexts, even competing with our previous dedicated solutions for computing provenance in annotated graph databases. The cost overhead compared to plain Datalog evaluation is fairly moderate in many cases of interest.

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“…as well as to Datalog programs used in previous works [5]. The performance of the implementation competes with dedicated solutions specific to graph databases [25].…”

Section: Xx:3mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Practical Dynamic Programming Approach to Datalog Provenance Computation

Ramusat¹,

Maniu²,

Senellart³

2021

Preprint

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“…Semiring provenance has also been studied for Datalog queries, for which it was defined based on the set of all derivation trees for the query (Green, Karvounarakis, and Tannen 2007;Deutch et al 2014;Deutch, Gilad, and Moskovitch 2018). However, this definition seems less axiomatic than in the case of relational databases.…”

Section: Introductionmentioning

confidence: 99%

Revisiting Semiring Provenance for Datalog

Bourgaux¹,

Bourhis²,

Peterfreund³

et al. 2022

Preprint

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Data provenance consists in bookkeeping meta information during query evaluation, in order to enrich query results with their trust level, likelihood, evaluation cost, and more. The framework of semiring provenance abstracts from the specific kind of meta information that annotates the data. While the definition of semiring provenance is uncontroversial for unions of conjunctive queries, the picture is less clear for Datalog. Indeed, the original definition might include infinite computations, and is not consistent with other proposals for Datalog semantics over annotated data. In this work, we propose and investigate several provenance semantics, based on different approaches for defining classical Datalog semantics. We study the relationship between these semantics, and introduce properties that allow us to analyze and compare them. Example 2. Let Σ contain r 1 := R(x, y) → H(x, x), r 2 := R(x, y) → H(x, y) and r 3 := S(x, y, z) ∧ S(x, z, y) → H(x, x). If D = {R(a, a), S(a, b, c), S(a, c, b)}, then the fact α := H(a, a) has the following derivation trees (α, r 1 , h) R(a, a) (α, r 2 , h) R(a, a) (α, r 3 , h 3 ) S(a, b, c)S(a, c, b) (α, r 3 , h ′ 3 ) S(a, c, b)S(a, b, c)whereNote that when the program at hand is recursive (i.e., the dependency graph of its atoms contain cycles) a fact may our results are valid under this assumption as well.

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Online maintenance of evolving knowledge graphs with RDFS-based saturation and why-provenance support

Belhajjame

Mejri

2023

Journal of Web Semantics

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Efficient provenance tracking for datalog using top-k queries

Cited by 9 publications

References 43 publications

A Practical Dynamic Programming Approach to Datalog Provenance Computation

A Practical Dynamic Programming Approach to Datalog Provenance Computation

Revisiting Semiring Provenance for Datalog

Online maintenance of evolving knowledge graphs with RDFS-based saturation and why-provenance support

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