ProvSQL

Senellart, Pierre; Jachiet, Louis; Maniu, Silviu; Ramusat, Yann

doi:10.14778/3229863.3236253

Cited by 38 publications

(4 citation statements)

References 31 publications

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“…Senellart et al developed an open-source module for the PostgreSQL database management system. The module developed by them adds support for the computation of provenance and probabilities of query results [11]. Garima et al presented a framework named HUKA that uses provenance polynomials for tracking the derivation of query results over knowledge graphs by encoding the edges involved in generating the answer [18].…”

Section: Related Workmentioning

confidence: 99%

“…To address the pivotal research problem, Annolog is designed in this paper for usage either as a standalone reasoner or to work synchronously with the database management system engine. When evaluating a query on annotated data, it is advantageous to capture meta-information about the result of the query along with the result itself [11]. The annotated meta-information indicates the multiplicity and probability of the results, and why as well as how the outputs are generated.…”

Section: Introductionmentioning

confidence: 99%

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Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Zou¹,

Zou²,

Wang³

2023

Journal of Computers

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<p>Data annotation is the categorization and labelling of data for applications, such as machine learning, artificial intelligence, and data integration. The categorization and labelling are done to achieve a specific use case in relation to solving problems. Existing data annotation systems and modules face imperfections such as knowledge and annotation not being formally integrated, narrow application range, and difficulty to apply on existing database management applications. To analyze and process annotated data, obtain the relationship between different annotations, and capture metainformation in data provenance and probabilistic databases, in this paper, we design a back-end query processing framework as a supplementary interface for the database management system to extend operation to datasets and boost efficiency. The framework utilizes Java language and the MVC model for development to achieve lightweight, cross-platform, and high adaptability identities. The contribution of this paper is mainly reflected in two aspects. The first contribution is to implement query processing, provenance semiring, and semiring homomorphism over annotated data. The second contribution is to combine query processing and provenance with SQL statements in order to enable the database manager to invoke operations to annotation.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Zou¹,

Zou²,

Wang³

2023

Journal of Computers

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“…P SQL is "an open-source module for the P SQL database management system that adds support for computation of provenance and probabilities of query results" [Senellart et al, 2018]. It supports where-provenance, semiring provenance, and m-semiring provenance (for negation and why-notprovenance).…”

Section: P Sqlmentioning

confidence: 99%

Language-integrated provenance

Fehrenbach

Cheney

2018

Science of Computer Programming

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When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given.My thanks, first and foremost, to my advisor James Cheney for his generous guidance, encouragement, and unyielding support. I have left every single one of our meetings happier and more confident than I entered it, even and especially when things had gone poorly. I would also like to thank Peter Buneman, Ian Stark, James McKinna, and Jan Stolarek for their interest and feedback. I would like to thank Torsten Grust and Sam Lindley for agreeing to be my examiners and their feedback, and Stephen Gilmore for chairing my viva.Thanks to my awesome office mates Karoliina, Fabian, Ricardo, and Rudi; to the Friday lunch crowd Brian,

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“…Kimmig et al (2017) showed that if the underlying logical theory is compiled into sd-DNNF representation T C, then we can solve weighted model counting instances over any semiring using polynomially (in |T C|) many additions and multiplications. This is also the strategy that current implementations like ProbLog (De Raedt et al, 2007), aspmc (Eiter, Hecher, & Kiesel, 2021) and ProvSQL (Senellart, Jachiet, Maniu, & Ramusat, 2018) predominantly employ. As Kimmig et al (2017) also noted, depending on the specific semiring in use, this is clearly far from the most efficient solution.…”

Section: Introductionmentioning

confidence: 99%

Semiring Reasoning Frameworks in AI and Their Computational Complexity

Eiter

Kiesel

2023

jair

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Many important problems in AI, among them #SAT, parameter learning and probabilistic inference go beyond the classical satisfiability problem. Here, instead of finding a solution we are interested in a quantity associated with the set of solutions, such as the number of solutions, the optimal solution or the probability that a query holds in a solution. To model such quantitative problems in a uniform manner, a number of frameworks, e.g. Algebraic Model Counting and Semiring-based Constraint Satisfaction Problems, employ what we call the semiring paradigm. In the latter the abstract algebraic structure of the semiring serves as a means of parameterizing the problem definition, thus allowing for different modes of quantitative computations by choosing different semirings. While efficiently solvable cases have been widely studied, a systematic study of the computational complexity of such problems depending on the semiring parameter is missing. In this work, we characterize the latter by NP(R), a novel generalization of NP over semiring R, and obtain NP(R)-completeness results for a selection of semiring frameworks. To obtain more tangible insights into the hardness of NP(R), we link it to well-known complexity classes from the literature. Interestingly, we manage to connect the computational hardness to properties of the semiring. Using this insight, we see that, on the one hand, NP(R) is always at least as hard as NP or ModpP depending on the semiring R and in general unlikely to be in FPSPACEpoly. On the other hand, for broad subclasses of semirings relevant in practice we can employ reductions to NP, ModpP and #P. These results show that in many cases solutions are only mildly harder to compute than functions in NP, ModpP and #P, give us new insights into how problems that involve counting on semirings can be approached, and provide a means of assessing whether an algorithm is appropriate for a given class of problems.

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ProvSQL

Cited by 38 publications

References 31 publications

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Language-integrated provenance

Semiring Reasoning Frameworks in AI and Their Computational Complexity

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