Scalable Preservation, Reconstruction, and Querying of Databases in terms of Semantic Web Representations

Stefanova, S. 2013. Scalable Preservation, Reconstruction, and Querying of Databases in terms of Semantic Web Representations. Acta Universitatis Upsaliensis. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1052. 59 pp. Uppsala. ISBN 978-91-554-8690-7. This Thesis addresses how Semantic Web representations, in particular RDF, can enable flexible and scalable preservation, recreation, and querying of databases. An approach has been developed for selective scalable long-term archival of relational databases (RDBs) as RDF, implemented in the SAQ (Semantic Archive and Query) system. The archival of user-specified parts of a RDB is specified using an extension of SPARQL, ASPARQL. SAQ automatically generates an RDF view of an RDB, the RD-view. The result of an archival query is RDF triples stored in: i)a data archive file containing the preserved RDB content, and ii)a schema archive file containing sufficient meta-data to reconstruct the archived database. To achieve scalable data preservation SAQ uses special query rewriting optimizations for the archival queries. It was experimentally shown that they improve query execution time compared with naïve processing. The performance of SAQ was compared with that of other systems supporting SPARQL queries to views of existing RDBs. To reconstruct an RDF-archived RDB an approach was developed and implemented in the reloader module of SAQ. When an archived RDB is to be reconstructed, the reloader first reads the schema archive file and executes a schema reconstruction algorithm to automatically construct the RDB schema. The thus created RDB is populated by reading the data archive and converting the read data into relational attribute values. For scalable reconstruction of RDF archived data we have developed the Triple Bulk Load (TBL) approach where the relational data is reconstructed by using the bulk load facility of the RDBMS. Our experiments show that the TBL approach is substantially faster than the naïve Insert Attribute Value (IAV) approach, despite the added sorting and post-processing. To view and query the semi-structured data Topic Maps as RDF the prototype system, TMViewer was implemented. A declarative RDF view of Topic Maps, the TM-view is automatically generated by the TM-viewer using a developed conceptual schema for the Topic Maps data model. To achieve efficient query processing of SPARQL queries to the TM-view query rewrite transformations were developed and evaluated. It was shown that they significantly improve the query processing time.