Querying Transaction–Time Databases under Branched Schema Evolution

Abstract

Abstract. Transaction-time databases have been proposed for storing and querying the history of a database. While past work concentrated on managing the data evolution assuming a static schema, recent research has considered data changes under a linearly evolving schema. An ordered sequence of schema versions is maintained and the database can restore/query its data under the appropriate past schema. There are however many applications leading to a branched schema evolution where data can evolve in parallel, under different concurrent schemas. In this work, we consider the issues involved in managing the history of a database that follows a branched schema evolution. To maintain easy access to any past schema, we use an XML-based approach with an optimized sharing strategy. As for accessing the data, we explore branched temporal indexing techniques and present efficient algorithms for evaluating two important queries made possible by our novel branching environment: the vertical historical query and the horizontal historical query. Moreover, we show that our methods can support branched schema evolution which allows version merging. Experimental evaluations show the efficiency of our storing, indexing, and query processing methodologies. IntroductionDue to the collaborative nature of web applications, information systems experience evolution not only on their data content but also under different schema versions. For example, Wikipedia has experienced more than 170 schema changes in its 4.5 years of lifetime [5]. Schema evolution has been addressed for traditional (single-state) database systems and issues on how data is efficiently transferred to the latest schema have been examined [4]. Consider however the case where the application maintains its past data (typically for archiving, auditing reasons etc.) which may have followed different schemas. A temporal database can be facilitated to manage the historical data, but issues related to how data can be queried under different schemas arise. The pioneering work in PRIMA system [8] addresses the issues of maintaining a transaction-time database under schema evolution by introducing: (i) an XML-based model for archiving historical data with evolving schemas, (ii) a language of atomic schema modification operators (SMOs), and (iii) query answering and rewriting algorithms for complex temporal queries spanning over multiple schema versions. Nevertheless,