Extending Weakly-Sticky Datalog $$^\pm $$ : Query-Answering Tractability and Optimizations

Abstract

Weakly-sticky (WS ) Datalog ± is an expressive member of the family of Datalog ± programs that is based on the syntactic notions of stickiness and weak-acyclicity. Query answering over the WS programs has been investigated, but there is still much work to do on the design and implementation of practical query answering (QA) algorithms and their optimizations. Here, we study sticky and WS programs from the point of view of the behavior of the chase procedure, extending the stickiness property of the chase to that of generalized stickiness of the chase (gschproperty). With this property we specify the semantic class of GSCh programs, which includes sticky and WS programs, and other syntactic subclasses that we identify. In particular, we introduce joint-weakly-sticky (JWS ) programs, that include WS programs. We also propose a bottomup QA algorithm for a range of subclasses of GSCh. The algorithm runs in polynomial time (in data) for JWS programs. Unlike the WS class, JWS is closed under a general magic-sets rewriting procedure for the optimization of programs with existential rules. We apply the magicsets rewriting in combination with the proposed QA algorithm for the optimization of QA over JWS programs.

Ontological Multidimensional Data Models and Contextual Data Quality

“…It is also independent of any procedural mechanism for quality data extraction and data cleaning, but computational methods can be extracted from (or be based on) the speci cations e implementation of the alityQA algorithm and experiments to evaluate its performance correspond to ongoing work. e algorithm and its optimization is based on our work on QA under WS programs [74,75].…”

“…e stickiness property for a program guarantees that, given a CQ, a nite initial fragment of the possibly in nite chase can be used for answering the query; actually, a fragment of polynomial size in that of the extensional data (cf. [29] and [74] for a more detailed discussion). As a consequence, CQA on sticky programs is in in data.…”

“…Next, according to Step 4, each (conjunctive) disjunct of Q M can be answered by the given algorithm for CQA from O M with extensional data in E and D (the la er obtained by importing D into context O c ). e algorithm can be applied in particular to compute the core clean version, Core q (D), of D. 20 Actually the algorithm applies to a larger class of Datalog ± ontologies, that of join-weakly-sticky programs that is closed under magic-sets optimizations [74]. 21 If they are more general, but under the modularity assumption of Section 5.1, we do not unfold, but do rst CQA on the Datalog programs de ning the top, non-dimensional predicates, and next, when the "extensional" dimensional predicates have to be evaluated, we call the algorithm for CQA for the MD ontology.…”

“…Step 3: Unfold in Q c the de nitions of quality predicates given by the rules (26) Step 3 of alityQA uses the quality predicate de nition (28) for unfolding, obtaining the query: Finally, at Step 4 of alityQA, Q M is answered as a CQ over O M and database D , 22 using, for example, the QA algorithms in [74,75].…”

“…In this work, we do not provide algorithms for (tractable) QA on weakly-sticky Datalog ± programs. However, in [74] a practical algorithm was proposed, together with a methodology for magic-setbased query optimization. 5 In the case of duplicate records in a data source, the context could contain an answer set program or a Datalog program to enforce matching dependencies for entity resolution [10].…”