A Hybrid Approach to Query Answering Under Expressive Datalog $$^\pm $$

Milani, Mostafa; Calì, Andrea; Bertossi, Leopoldo

doi:10.1007/978-3-319-45276-0_11

Cited by 4 publications

(8 citation statements)

References 13 publications

(33 reference statements)

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“…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].…”

Section: Discussionmentioning

confidence: 99%

“…Unlike sticky programs, for complexity-theoretic reasons, WS programs do not allow FO rewritability for CQA. However, a hybrid algorithm is proposed in [75]. It is based on partial grounding of the tgds using the extensional data, obtaining a sticky program, and a subsequent rewriting of the query.…”

Section: Omd Ontologies As Weakly-sticky Datalog ± Programsmentioning

confidence: 99%

“…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].…”

Section: Algorithm 1: E Alityqa Algorithmmentioning

confidence: 99%

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Ontological Multidimensional Data Models and Contextual Data Quality

Bertossi

Milani

2017

J. Data and Information Quality

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Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based ontologies. e data under assessment is mapped into the context, for additional analysis, processing, and quality data extraction. e resulting contexts allow for the representation of dimensions, and multidimensional data quality assessment becomes possible. At the core of a multidimensional context we include a generalized multidimensional data model and a Datalog ± ontology with provably good properties in terms of query answering.ese main components are used to represent dimension hierarchies, dimensional constraints, dimensional rules, and de ne predicates for quality data speci cation. ery answering relies upon and triggers navigation through dimension hierarchies, and becomes the basic tool for the extraction of quality data. e OMD model is interesting per se, beyond applications to data quality. It allows for a logic-based, and computationally tractable representation of multidimensional data, extending previous multidimensional data models with additional expressive power and functionalities.

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Section: Discussionmentioning

confidence: 99%

Section: Omd Ontologies As Weakly-sticky Datalog ± Programsmentioning

confidence: 99%

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Ontological Multidimensional Data Models and Contextual Data Quality

Bertossi

Milani

2017

J. Data and Information Quality

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“…Actually, about (A), we provide for the class SCh(S ∃ ) a polynomial-time, chase-based, bottom-up QA algorithm, which can be applied to JWS (and all its semantic and syntactic subclasses) in particular. This is a general situation: The polynomial-time QA algorithms for the classes Sticky [10], WS [10,22], and JWS (this work) rely basically on the properties of the semantic class rather than on the specific syntactic restrictions. Hence our interest is in investigating the particular semantics classes, and semantic classes in general, as defined by selection functions.…”

Section: S ⊥mentioning

confidence: 99%

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

Milani

Bertossi

2016

Web Reasoning and Rule Systems

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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.

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“…Actually, they belong to the class of weakly-sticky Datalog ± programs [4], for which conjunctive query answering (CQA) can be done in polynomial time in data. Algorithms for CQA have been proposed [8,9], so as optimizations thereof [8] with magic-sets techniques [1].…”

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confidence: 99%

The Ontological Multidimensional Data Model

Bertossi,

Milani

2017

Preprint

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A Hybrid Approach to Query Answering Under Expressive Datalog $$^\pm $$

Cited by 4 publications

References 13 publications

Ontological Multidimensional Data Models and Contextual Data Quality

Ontological Multidimensional Data Models and Contextual Data Quality

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

The Ontological Multidimensional Data Model

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