Data Sharing and Exchange: Semantics and Query Answering - Awada, Rana (Université d'Ottawa / University of Ottawa, 2015)
Exchanging and integrating data that belong to worlds of different vocabularies are two prominent problems in the database literature. While data coordination deals with managing and integrating data between autonomous yet related sources with possibly distinct vocabularies, data exchange is deﬁned as the problem of extracting data from a source and materializing it in an independent target to conform to the target schema. These two problems, however, have never been studied in a uniﬁed setting which allows both the exchange of the data as well as the coordination of different vocabularies between different sources. Our thesis shows that such a uniﬁed setting exhibits data integration capabilities that are beyond the ones provided by data exchange and data coordination separately. In this thesis, we propose a new setting – called DSE, for Data Sharing and Exchange – which allows the exchange of data between independent source and target applications that possess independent schemas, as well as independent yet related domains of constants. To facilitate this type of exchange, we extend the source-to-target dependencies used in the ordinary data exchange setting which allow the association between the source and the target at the schema level, with the mapping table construct introduced in the classical data coordination setting which deﬁnes the association between the source and the target at the instance level. A mapping table construct deﬁnes for each source element, the set of associated (or corresponding) elements in the domain of the target. The semantics of this association relationship between source and target elements change with different requirements of different applications. Ordinary DE settings can represent DSE settings; however, we show that there exist DSE settings with particular semantics of related values in mapping tables where DE is not the best exchange solution to adopt. The thesis introduces two DSE settings with such a property. We call the ﬁrst DSE with unique identity semantics. The semantics of a mapping table in this DSE setting speciﬁes that each source element should be uniquely mapped to at least one target element that is associated with it in the mapping table. ii In this setting, classical DE is one method to perform a data exchange; however, it is not the best method to adopt, since it can not represent exchange applications, that require – as DC applications – to compute both portions as well as complete sets of certain answers for conjunctive queries. In addition, we show that adopting known DE universal solutions as semantics for such DSE settings is not the best in terms of efﬁciency when computing certain answers for conjunctive queries. The second DSE setting that the thesis introduces with the same property is called DSE with equality semantics. This setting captures interesting meaning of related data in a mapping table. Such semantics impose that each source element in a mapping table is related to a target element only if both elements are equivalent (i.e they have the same meaning). We show in our thesis that this DSE setting differs from ordinary DE settings in the sense that additional information could be entailed under such interpretation of related data. Also, this added information needs to be augmented to both the source instance and the mapping table in order to generate target instances that correctly reﬂect both in a DSE scenario. In other words, we can say that in such a DSE setting, a source instance and a mapping table can be incomplete with respect to the semantics of the mapping table. We formally deﬁne the two aforementioned semantics of a DSE setting and we distinguish between two types of solutions for this setting, named,universal DSE solutions, which contain the complete set of exchanged information, and universal DSE KB-Solutions, which store a portion of the exchanged information with implicit information in the form of a set of rules over the target. DSEKB-Solutions allow applications to compute on demand both a portion and the complete set of certain answers for conjunctive queries. In addition,we deﬁne the semantics of conjunctive query answering, and we distinguish between sound and complete certain answers for conjunctive queries and we deﬁne the algorithms to compute these efﬁciently. Finally, we provide experimental results which compare the run times to generate DSE solutions versus DSE KB-solutions, and compare the performance of computing sound and complete certain answers for conjunctive queries using both types of solutions
Keywords: Data Exchange; Data Integration