Download Database Repairing and Consistent Query Answering by Leopoldo Bertossi, M. Tamer Ozsu PDF

By Leopoldo Bertossi, M. Tamer Ozsu

Integrity constraints are semantic stipulations database may still fulfill so that it will be a suitable version of exterior fact. In perform, and for plenty of purposes, a database won't fulfill these integrity constraints, and accordingly it truly is acknowledged to be inconsistent. notwithstanding, and probably, a wide component to the database continues to be semantically right, in a feeling that needs to be made targeted. After having supplied a proper characterization of constant information in an inconsistent database, the traditional challenge emerges of extracting that semantically right info, as question solutions. The constant facts in an inconsistent database is mostly characterised because the info that persists throughout the entire database situations which are constant and minimally range from the inconsistent example. these are the so-called upkeep of the database. particularly, the constant solutions to a question posed to the inconsistent database are these solutions that may be at the same time got from the entire database maintenance. As anticipated, the thought of fix calls for an sufficient suggestion of distance that enables for the comparability of databases with recognize to how a lot they fluctuate from the inconsistent example. in this foundation, the minimality situation on upkeep will be competently formulated. during this monograph we current and speak about those primary suggestions, varied fix semantics, algorithms for computing constant solutions to queries, and likewise complexity-theoretic effects concerning the computation of maintenance and doing constant question answering. desk of Contents: creation / The Notions of fix and constant resolution / Tractable CQA and question Rewriting / Logically Specifying upkeep / choice difficulties in CQA: Complexity and Algorithms / upkeep and information cleansing

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The hypergraph-based methodology has also been extended and applied to general universal constraints, of which denial constraints form a subclass [Staworko and Chomicki, 2010] (cf. 3). 4. 4 31 KEYS, TREES, FORESTS AND ROOTS The complexity analysis of CQA for key constraints and conjunctive queries with projection, that was started by Chomicki and Marcinkowski [2005], was extended by Fuxman and Miller [2005, 2007], where a broader tractable class was identified and characterized, and polynomial time algorithms were presented.

Here, | · | is used to denote the cardinality of a set. The set of repairs of an instance D under this semantics is denoted by Reptc(D, IC). / Reptc(D, IC). 6, we have D1 ∈ Reptc(D, IC), but D2 ∈ was briefly considered in Arenas et al. [2003a], and studied in more detail by Arieli et al. [2006], Lopatenko and Bertossi [2007] and Afrati and Kolaitis [2009]. 7. 6 ATTRIBUTE-BASED REPAIRS So far, we have considered repairs that insert or delete full tuples. Attribute-based repairs are obtained by changing attribute values in database tuples.

2003]. 4 and Chapter 5. 4 NULL INSERTIONS-BASED REPAIRS Under this repair semantics, existentially quantified inclusion dependencies are repaired by insertions of null values (cf. 5 for more details). 10, the only repair would be D = {P (a), R(a, null)}. Bravo and Bertossi [2006] use a single null value for restoring consistency, and it behaves as an SQL NULL. This same repair semantics has been used in peer data exchange systems [Bertossi and Bravo, 2007]. Data are moved around at query answering time, and this process is driven by consistency restoration of inter-peer data exchange constraints.

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