Matching Dependencies (MDs) are a recent proposal for declarative entity resolution. They are rules that specify, given the similarities satisfied by values in a database, what values should be considered duplicates, and have to be matched. On the basis of a chase-like procedure for MD enforcement, we can obtain clean (duplicate-free) instances; actually possibly several of them. The clean answers to queries (which we call the resolved answers) are invariant under the resulting class of instances. In this paper, we investigate a query rewriting approach to obtaining the resolved answers (for certain classes of queries and MDs). The rewritten queries are specified in stratified Datalog not,s with aggregation. In addition to the rewriting algorithm, we discuss the semantics of the rewritten queries, and how they could be implemented by means of a DBMS.
We motivate, formalize and investigate the notions of data quality assessment and data quality query answering as context dependent activities. Contexts for the assessment and usage of a data source at hand are modeled as collections of external databases, that can be materialized or virtual, and mappings within the collections and with the data source at hand. In this way, the context becomes "the complement" of the data source wrt a data integration system. The proposed model allows for natural extensions, like considering data quality predicates, and even more expressive ontologies for data quality assessment.
A correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints has already been established. In this work, answer-set programs that specify repairs of databases are used as a basis for solving computational and reasoning problems about causes. Here, causes are also introduced at the attribute level by appealing to a both null-based and attribute-based repair semantics. The corresponding repair programs are presented, and they are used as a basis for computation and reasoning about attribute-level causes.