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.
@cite_19 uses ranking functions on facts to resolve conflicts by taking only the fact with highest rank and removing others. This approach constructs a unique repair under the assumption that no two different facts are of equal rank (satisfaction of @math ). If this assumption is not satisfied and the facts contain numeric values, a new value, called the fusion, can be calculated from the conflicting facts (then, however, the constructed instance is not necessarily a repair in the sense of Definition which means a possible loss of information).
@cite_19
2167333415 2159296364 1972827298 2062180302
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. We consider a set of views stating possibly conflicting facts. Negative facts in the views may come, e.g., from functional dependencies in the underlying database schema. We want to predict the truth values of the facts. Beyond simple methods such as voting (typically rather accurate), we explore techniques based on "corroboration", i.e., taking into account trust in the views. We introduce three fixpoint algorithms corresponding to different levels of complexity of an underlying probabilistic model. They all estimate both truth values of facts and trust in the views. We present experimental studies on synthetic and real-world data. This analysis illustrates how and in which context these methods improve corroboration results over baseline methods. We believe that corroboration can serve in a wide range of applications such as source selection in the semantic Web, data quality assessment or semantic annotation cleaning in social networks. This work sets the bases for a wide range of techniques for solving these more complex problems. Many situations exist in which n objects are ranked by two or more independent sources, where interest centers primarily on agreement in the top rankings and disagreements on items at the bottom of the rankings are of little or no importance. A problem with Spearman's rho or Kendall's coefftcient of concordance in this setting is that they are equally influenced by disagreement on the assignment of rankings at all levels. In this article, a concordance measure is provided that is more sensitive to agreement on the top rankings. The statistics used in this setting are functions of the ordinary correlation coeRicient computed on Savage (1956) scores. The asymptotic distributions of these statistics are presented, and a summary of the quantiles of the exact distribution for the two sample case are provided for n = 3(1)14. The statistic for the two-sample case is shown to provide a locally most powerful rank test for a model given by Hajek and Sidak (1967). This article deals with the computation of consistent answers to queries on relational databases that violate primary key constraints. A repair of such inconsistent database is obtained by selecting a maximal number of tuples from each relation without ever selecting two distinct tuples that agree on the primary key. We are interested in the following problem: Given a Boolean conjunctive query q, compute a Boolean first-order (FO) query @j such that for every database db, @j evaluates to true on db if and only if q evaluates to true on every repair of db. Such @j is called a consistent FO rewriting of q. We use novel techniques to characterize classes of queries that have a consistent FO rewriting. In this way, we are able to extend previously known classes and discover new ones. Finally, we use an Ehrenfeucht-Fraisse game to show the non-existence of a consistent FO rewriting for @[email protected]?y(R([email protected]?,y)@?R([email protected]?,c)), where c is a constant and the first coordinate of R is the primary key.