A consistent query answer in an inconsistent database is an answer obtained in every (minimal) repair. The repairs are obtained by resolving all conflicts in all possible ways. Often, however, the user is able to provide a preference on how conflicts should be resolved. We investigate here the framework of preferred consistent query answers, in which user preferences are used to narrow down the set of repairs to a set of preferred repairs. We axiomatize desirable properties of preferred repairs. We present three different families of preferred repairs and study their mutual relationships. Finally, we investigate the complexity of preferred repairing and computing preferred consistent query answers.
An approach where the user has a certain degree of control over the way the conflicts are resolved is presented in @cite_24 . Using repair constraints the user can restrict considered repairs to those where facts from one relation have been removed only if similar facts have been removed from some other relation. This approach satisfies @math but not @math . A method of weakening the repair constraints is proposed to get @math , however this comes at the price of losing @math .
@cite_24
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Data integrated from multiple sources may contain inconsistencies that violate integrity constraints. The constraint repair problem attempts to find "low cost" changes that, when applied, will cause the constraints to be satisfied. While in most previous work repair cost is stated in terms of tuple insertions and deletions, we follow recent work to define a database repair as a set of value modifications. In this context, we introduce a novel cost framework that allows for the application of techniques from record-linkage to the search for good repairs. We prove that finding minimal-cost repairs in this model is NP-complete in the size of the database, and introduce an approach to heuristic repair-construction based on equivalence classes of attribute values. Following this approach, we define two greedy algorithms. While these simple algorithms take time cubic in the size of the database, we develop optimizations inspired by algorithms for duplicate-record detection that greatly improve scalability. We evaluate our framework and algorithms on synthetic and real data, and show that our proposed optimizations greatly improve performance at little or no cost in repair quality. In this paper we describe a new approach to repairing violations of integrity constraints in relational databases with null values. By adopting basic concepts from model-based diagnosis, we show how simultaneous reasons for violations of (different) constraints can be determined. These reasons, represented as sets of facts, directly indicate possible repair actions that guarantee to remove the observed violations. An @math maximum distance separable (MDS) array code of length @math , dimension @math , and sub-packetization @math is formed of @math matrices over a finite field @math , with every column of the matrix stored on a separate node in the distributed storage system and viewed as a coordinate of the codeword. Repair of a failed node (recovery of one erased column) can be performed by accessing a set of @math surviving (helper) nodes. The code is said to have the optimal access property if the amount of data accessed at each of the helper nodes meets a lower bound on this quantity. For optimal-access MDS codes with @math , the sub-packetization @math satisfies the bound @math . In our previous work (IEEE Trans. Inf. Theory, vol. 63, no. 4, 2017), for any @math and @math , we presented an explicit construction of optimal-access MDS codes with sub-packetization @math . In this paper, we take up the question of reducing the sub-packetization value @math to make it to approach the lower bound. We construct an explicit family of optimal-access codes with @math , which differs from the optimal value by at most a factor of @math . These codes can be constructed over any finite field @math as long as @math , and afford low-complexity encoding and decoding procedures. We also define a version of the repair problem that bridges the context of regenerating codes and codes with locality constraints (LRC codes), which we call group repair with optimal access . In this variation, we assume that the set of @math nodes is partitioned into @math repair groups of size @math , and require that the amount of accessed data for repair is the smallest possible whenever the @math helper nodes include all the other @math nodes from the same group as the failed node. For this problem, we construct a family of codes with the group optimal access property. These codes can be constructed over any field @math of size @math , and also afford low-complexity encoding and decoding procedures. Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related entities is an expression of the given relation. Here we argue that this leads to noisy patterns that hurt precision, in particular if the knowledge base is not directly related to the text we are working with. We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decision whether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB. We apply our approach to extract relations from the New York Times corpus and use Freebase as knowledge base. When compared to a state-of-the-art approach for relation extraction under distant supervision, we achieve 31 error reduction.