First-Order Query Rewriting for Inconsistent Databases

Fuxman, Ariel; Miller, Renée J.

doi:10.1007/978-3-540-30570-5_23

Cited by 80 publications

(105 citation statements)

References 8 publications

Supporting

Mentioning

105

Contrasting

Order By: Relevance

“…it does not have free variables. All these queries fall in the class of Tree-queries for which CQA is tractable under key constraints [39]. However, since we are also considering RICs, which are repaired by inserting tuples with null values, it is not possible to use the polynomial time algorithm for CQA presented in [39].…”

Section: Methodsmentioning

confidence: 99%

“…For them CQA can be solved in polynomial time in data complexity. Also coNP-complete cases have been identified [9,27,28,39].…”

Section: Student Id Name 1 Peter 2 Jonesmentioning

confidence: 99%

“…In these cases, the first-order (FO) query can be rewritten into a new FO query that posed and answered as usual to given instance, obtains the consistent answers to the original query. Other FO query rewriting methods for CQA were presented in [27,39,46]. However, they are still limited in their applicability, which is due to the intrinsic higher data complexity of CQA.…”

Section: Student Id Name 1 Peter 2 Jonesmentioning

confidence: 99%

See 2 more Smart Citations

The consistency extractor system: Answer set programs for consistent query answering in databases

Caniupan

Bertossi

2010

Data & Knowledge Engineering

View full text Add to dashboard Cite

We describe the Consistency Extractor System (ConsEx) that computes consistent answers to Datalog queries with negation posed to relational databases that may be inconsistent with respect to certain integrity constraints. In order to solve this task, ConsEx uses answers set programming. More precisely, ConsEx uses disjunctive logic programs with stable models semantics to specify and reason with the repairs, i.e. with the consistent virtual instances that minimally depart from the original database. The consistent information is invariant under all repairs. ConsEx achieves efficient query evaluation by implementing magic sets techniques. We describe the general methodology, its optimizations for query answering, and the architecture of the system. We also present encouraging experimental results.

show abstract

Section: Methodsmentioning

confidence: 99%

“…For them CQA can be solved in polynomial time in data complexity. Also coNP-complete cases have been identified [9,27,28,39].…”

Section: Student Id Name 1 Peter 2 Jonesmentioning

confidence: 99%

Section: Student Id Name 1 Peter 2 Jonesmentioning

confidence: 99%

See 1 more Smart Citation

The consistency extractor system: Answer set programs for consistent query answering in databases

Caniupan

Bertossi

2010

Data & Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…For the given target query and target constraints, the latter is known to be coNPhard in data complexity [16,21]. Theorem 3.6 implies that the algorithmic properties of exchange-repairs are quite different from those of ⊕-repairs.…”

Section: Mmentioning

confidence: 99%

Exchange-Repairs: Managing Inconsistency in Data Exchange

Cate

Halpert

Kolaitis

2014

Web Reasoning and Rule Systems

View full text Add to dashboard Cite

In a data exchange setting with target constraints, it is often the case that a given source instance has no solutions. Intuitively, this happens when data sources contain inconsistent or conflicting information that is exposed by the target constraints at hand. In such cases, the semantics of target queries trivialize, because the certain answers of every target query over the given source instance evaluate to "true". The aim of this paper is to introduce and explore a new framework that gives meaningful semantics in such cases by using the notion of exchange-repairs. Informally, an exchange-repair of a source instance is another source instance that differs minimally from the first, but has a solution. In turn, exchange-repairs give rise to a natural notion of exchange-repair certain answers (in short, XR-certain answers) for target queries in the context of data exchange with target constraints.After exploring the structural properties of exchange-repairs, we focus on the problem of computing the XR-certain answers of conjunctive queries. We show that for schema mappings specified by source-to-target GAV dependencies and target equality-generating dependencies (egds), the XRcertain answers of a target conjunctive query can be rewritten as the consistent answers (in the sense of standard database repairs) of a union of conjunctive queries over the source schema with respect to a set of egds over the source schema, thus making it possible to use a consistent query-answering system to compute XR-certain answers in data exchange. In contrast, we show that this type of rewriting is not possible for schema mappings specified by source-to-target LAV dependencies and target egds, nor for schema mappings specified by both source-to-target and target GAV dependencies. We then examine the general case of schema mappings specified by source-to-target GLAV constraints, a weakly acyclic set of target tgds and a set of target egds. The main result asserts that, for such settings, the XR-certain answers of conjunctive queries can be rewritten as the certain answers of a union of conjunctive queries with respect to the stable models of a disjunctive logic program over a suitable expansion of the source schema.

show abstract

“…An approach to remedy to this problem that has lately received a lot of interest in the literature (see, e.g., [3,11,12,14,[16][17][18][19]25,26]) is based on the notion of repair for an inconsistent database as introduced in [4]. Roughly speaking, a repair of a database is a new database that satisfies the constraints in the schema, and minimally differs from the original one.…”

Section: Data Integration Systems In a Nutshellmentioning

confidence: 99%

The H $\imath$ L ε X System for Semantic Information Extraction

Manna

Oro

Ruffolo

et al. 2012

Transactions on Large-Scale Data- And Knowledge-Centered Systems V

View full text Add to dashboard Cite

In this paper, a new technique for the optimization of (partially) bound queries over disjunctive Datalog programs with stratified negation is presented. The technique exploits the propagation of query bindings and extends the Magic Set optimization technique (originally defined for non-disjunctive programs).An important feature of disjunctive Datalog programs is nonmonotonicity, which calls for nondeterministic implementations, such as backtracking search. A distinguishing characteristic of the new method is that the optimization can be exploited also during the nondeterministic phase. In particular, after some assumptions have been made during the computation, parts of the program may become irrelevant to a query under these assumptions. This allows for dynamic pruning of the search space. In contrast, the effect of the previously defined Magic Set methods for disjunctive Datalog is limited to the deterministic portion of the process. In this way, the potential performance gain by using the proposed method can be exponential, as could be observed empirically.The correctness of the method is established and proved in a formal way thanks to a strong relationship between Magic Sets and unfounded sets that has not been studied in the literature before. This knowledge allows for extending the method and the correctness proof also to programs with stratified negation in a natural way.The proposed method has been implemented in the DLV system and various experiments on synthetic as well as on real-world data have been conducted. The experimental results on synthetic data confirm the utility of Magic Sets for disjunctive Datalog, and they highlight the computational gain that may be obtained by the new method with respect to the previously proposed Magic Set method for disjunctive Datalog programs. Further experiments on data taken from a real-life application show the benefits of the Magic Set method within an application scenario that has received considerable attention in recent years, the problem of answering user queries over possibly inconsistent databases originating from integration of autonomous sources of information.

show abstract

First-Order Query Rewriting for Inconsistent Databases

Cited by 80 publications

References 8 publications

The consistency extractor system: Answer set programs for consistent query answering in databases

The consistency extractor system: Answer set programs for consistent query answering in databases

Exchange-Repairs: Managing Inconsistency in Data Exchange

The H $\imath$ L ε X System for Semantic Information Extraction

Contact Info

Product

Resources

About