Scalar Aggregation in FD-Inconsistent Databases

Arenas, Marcelo; Bertossi, Leopoldo; Chomicki, Jan

doi:10.1007/3-540-44503-x_3

Cited by 31 publications

(46 citation statements)

References 14 publications

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“…In particular, F 1 contains the atom edge(1, 3), which leads us to map Z to 3. Eventually, this inferred binding information might be propagated to the remaining body atom path(Z, Y), which hence becomes path (3,5).…”

Section: Sideways Information Passing For Datalog Rulesmentioning

confidence: 99%

“…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%

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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

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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.

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Section: Sideways Information Passing For Datalog Rulesmentioning

confidence: 99%

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

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“…This is a generalization of the conflict graph defined by Arenas, Bertossi, and Chomicki [6] for FDs only.…”

Section: Conflict Hypergraphmentioning

confidence: 99%

“…In the framework of Arenas, Bertossi and Chomicki [4], which was adapted by the follow-up papers [5,6,7,8,9,11,12,19,21,25,26,34,41,45,46,49,62], the smallest unit to be deleted from (or added to) a database in the process of repairing is a tuple. A different choice is made by Wijsen [64] where tuples themselves are being repaired.…”

Section: Repairing By Attribute Modificationmentioning

confidence: 99%

“…The problem of computing consistent query answers is the second computational problem studied in the present paper. The notion of consistent query answer proposed by Arenas, Bertossi and Chomicki [4] has been used and extended in many papers [5,6,7,8,9,11,12,16,19,20,21,25,26,27,34,41,45,46,49,62,64]. This research has been surveyed by Bertossi and Chomicki [13].…”

Section: Introductionmentioning

confidence: 99%

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On the Computational Complexity of Minimal-Change Integrity Maintenance in Relational Databases

Chomicki

Marcinkowski

2005

Inconsistency Tolerance

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Abstract. We address the problem of minimal-change integrity maintenance in the context of integrity constraints in relational databases. Using the framework proposed by Arenas, Bertossi, and Chomicki [4], we focus on two basic computational issues: repair checking (is a database instance a repair of a given database?) and consistent query answers (is a tuple an answer to a given query in every repair of a given database?). We study the computational complexity of both problems, delineating the boundary between the tractable and the intractable. We review relevant semantical issues and survey different computational mechanisms proposed in this context. Our analysis sheds light on the computational feasibility of minimal-change integrity maintenance. The tractable cases should lead to practical implementations. The intractability results highlight the inherent limitations of any integrity enforcement mechanism, e.g., triggers or referential constraint actions, as a way of performing minimal-change integrity maintenance.

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First order theory for literal‐paraconsistent and literal‐paracomplete matrices

Lewin¹,

Mikenberg²

2010

Mathematical Logic Qtrly

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In this paper a first order theory for the logics defined through literal paraconsistent-paracomplete matrices is developed. These logics are intended to model situations in which the ground level information may be contradictory or incomplete, but it is treated within a classical framework. This means that literal formulas, i.e. atomic formulas and their iterated negations, may behave poorly specially regarding their negations, but more complex formulas, i.e. formulas that include a binary connective are well behaved. This situation may and does appear for instance in data bases.

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Scalar Aggregation in FD-Inconsistent Databases

Cited by 31 publications

References 14 publications

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

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

On the Computational Complexity of Minimal-Change Integrity Maintenance in Relational Databases

First order theory for literal‐paraconsistent and literal‐paracomplete matrices

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