Download Repairing and Querying Databases under Aggregate Constraints by Sergio Flesca PDF

By Sergio Flesca

Research has deeply investigated a number of concerns with regards to using integrity constraints on relational databases. particularly, loads of cognizance has been dedicated to the matter of extracting "reliable" details from databases containing items of knowledge inconsistent in regards to a few integrity constraints. during this manuscript, the matter of extracting constant details from relational databases violating integrity constraints on numerical facts is addressed. mixture constraints outlined as linear inequalities on aggregate-sum queries on enter information are thought of. The thought of fix as constant set of updates at attribute-value point is exploited, and the characterization of numerous data-complexity concerns relating to repairing info and computing constant question solutions is supplied. furthermore, a style for computing “reasonable” maintenance of inconsistent numerical databases is brought, for a constrained yet expressive type of combination constraints. An extension of this technique for facing the information repairing challenge within the presence of vulnerable combination constraints that are anticipated to be chuffed, yet now not required to, is gifted. moreover, a strategy for computing constant solutions of mixture queries within the presence of a large kind of combination constraints is equipped. ultimately, extensions of the framework in addition to numerous open difficulties are discussed.

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Additional resources for Repairing and Querying Databases under Aggregate Constraints

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A set of steady aggregate constraints A C can be modeled as a MILP 1 (Mixed Integer Linear Programming) problem [33], thus allowing us to adopt standard techniques addressing MILP problems to accomplish the computation of reasonable repairs. We point out that translating the problem of finding a card-minimal repair (as well as that of finding a preferred repair) into an MILP problem (which is NP-hard) is reasonable, since the search problem we are considering is NP-hard too. This easily follows from the fact that there is a straightforward reduction to this problem from the coNP-hard problem STEADYMRC card , the steady version of the card-minimal repair checking problem studied in Chapter 3.

A C and W . t. A C , whereas the value ∑ω∈W ∧θ ∈Θ (ω) s[μω,θ ] represents minimum number of the ground weak constraints which are not satisfied by any preferred repair. 6. The optimization problem OPT (D, A C , W , D) obtained for “Balance Sheet” example, where A C = {κ1 , κ2 , κ3 } and W = {ω1 , ω2 } is shown in Fig. 2. Herein, the substitutions θ1 , . . 20] ⎨ w3 = z3 − 100 w4 = z4 − 1250 σω1 ,θ1 = z2 − 1000 ⎪ ⎪ w σ = z − 1120 ⎪ 5 5 ω1 ,θ2 = z12 − 1000 ⎪ ⎪ ⎪ w6 = z6 − 20 σω2 ,θ3 = 200 − z3 ⎪ ⎪ ⎪ ⎪ σω2 ,θ4 = 200 − z13 w7 = z7 − 80 ⎪ ⎪ ⎪ ⎪ w8 = z8 − 1220 −M · μω1 ,θ1 ≤ σω1 ,θ1 ⎪ ⎪ ⎪ ⎪ = z − 30 −M · μω1 ,θ2 ≤ σω1 ,θ2 w 9 9 ⎪ ⎪ ⎪ ⎪ −M · μω2 ,θ3 ≤ σω2 ,θ3 ⎪ w10 = z10 − 80 ⎪ ⎪ ⎪ w11 = z11 − 80 −M · μω2 ,θ4 ≤ σω2 ,θ4 ⎪ ⎪ ⎪ ⎪ μω1 ,θ1 ∈ {0, 1} ⎪ w12 = z12 − 1110 ⎪ ⎪ ⎪ w13 = z13 − 90 μω1 ,θ2 ∈ {0, 1} ⎪ ⎪ ⎪ μω2 ,θ3 ∈ {0, 1} ⎪ ⎪ w14 = z14 − 1200 ⎩ w15 = z15 − 1130 μω2 ,θ4 ∈ {0, 1} Fig.

Analogously, under the set-minimal semantics, MRC and CQA are coNP- and Π2p - complete, respectively, if the numerical data domain is Z, and their complexity becomes P- and coNP- complete, respectively, if the numerical data domain is Q. 5 Summary of the complexity results 31 It is worth noting that, under the card-minimal semantics, the complexity of MRC and CQA does not depend on the numerical data domain and the class of aggregate constraint considered. This does not hold under the set-minimal semantics.

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