Solicited review(s): Name Surname, University, Country

Abstract. We present an approach for scalable long-term archival of data stored in relational databases (RDBs) as RDF, im-plemented in the SAQ (Semantic Archive and Query) system. The proposed approach is suitable for archiving scientific data used in scientific publications where it is desirable to preserve only parts of an RDB, e.g. only data about a specific set of ex-perimental artefacts in the database. With the approach, long-term preservation as RDF of selected parts of a database is speci-fied as an archival query in an extended SPARQL dialect, A-SPARQL. The query processing is based on automatically gener-ating an RDF view of a relational database to archive, called the RD-view. A-SPARQL provides flexible selection of data to be archived in terms of a SPARQL-like query to the RD-view. The result of an archival query is a data archive file containing the RDF-triples representing the relational data content to be preserved. The system also generates a schema archive file where sufficient meta-data are saved to allow the archived database to be fully reconstructed. An archival query usually selects both properties and their values for sets of subjects, which makes the property p in some triple patterns unknown. We call such que-ries where properties are unknown unbound-property queries. To achieve scalable data preservation and recreation, we pro-pose some query transformation strategies suitable for optimizing unbound-property queries. These query rewriting strategies were implemented and evaluated in a new benchmark for archival queries called ABench. ABench is defined as set of typical A-SPARQL queries archiving selected parts of databases generated by the Berlin benchmark data generator. In experiments, the SAQ optimization strategies were evaluated by measuring the performance of A-SPARQL queries selecting triples fo