Silvia Stefanova

Scalable long-term preservation of relational data through SPARQL queries

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 an RDB is specified using an extension of SPARQL, A-SPARQL. SAQ automatically generates an RDF view of the 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 and recreation, SAQ uses special query rewriting optimizations for the archival queries. It was experimentally shown that they improve query execution and archival time compared with naive processing. The performance of SAQ was compared with that of other systems supporting SPARQL queries to views of existing RDBs.When an archived RDB is to be recreated, the reloader module of SAQ 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 recreation 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 naive Insert Attribute Value (IAV) approach, despite the added sorting and post-processing.To view and query semi-structured Topic Maps data as RDF the prototype system TM-Viewer 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 execution time.

Scalable reconstruction of RDF-archived relational databases

We have investigated approaches for scalable reconstruction of relational databases (RDBs) archived as RDF files. An archived RDB is reconstructed from a data archive file and a schema archive file, both in N-Triples formats. The archives contain RDF triples representing the archived relational data content and the relational schema describing the content, respectively. When an archived RDB is to be reconstructed, the schema archive is first read to automatically create the RDB schema using a schema reconstruction algorithm which identifies RDB elements by queries to the schema archive. The RDB thus created is then populated by reading the data archive. To populate the RDB we have developed two approaches, the naive Insert Attribute Value (IAV) and Triple Bulk Load (TBL). With the IAV approach the data is populated by stored procedures that execute SQL INSERT or UPDATE statements to insert attribute values in the RDB tables. In the more complex TBL approach the database is populated by bulk loading CSV files generated by sorting the data archive triples joined with schema information. Our experiments show that the TBL approach is substantially faster than the IAV approach.

SPARQL queries to RDFS views of Topic Maps

Both Topic Maps and RDF are popular semantic web standards designed for machine processing of web documents. Since these representations were originally created for different purposes, they have differences both in their concepts and in their data models. The Topic Map data model can be seen as an ontology for building indices over web documents, whereas RDF is a language to define arbitrary properties of web resources. Ontologies can be defined using the RDF vocabulary description language RDF-Schema (RDFS) language. RDF(S) repositories can be searched using the query language SPARQL. To make Topic Maps exposed to RDF-based tools and searchable with SPARQL, our approach is to map Topic Maps to a view expressed in RDFS. The view can be queried using SPARQL. The problem concerning efficient query processing of SPARQL queries to the rather complex RDF view of Topic Maps has been studied in detail. Our approach has been applied on searching indices to e-government services.