Storing semistructured data with STORED

TLDR

Systems for managing and querying semistructured data often rely on proprietary object repositories or tagged‑text formats. The study proposes using relational database management systems to store and manage semistructured data, with a focus on applying the STORED technique to XML. STORED maps the semistructured data model to the relational model via a query language, and automatically generates this mapping from data instances using data‑mining techniques. When a document‑type descriptor is available, it can be leveraged by STORED to further improve performance.

Abstract

Systems for managing and querying semistructured-data sources often store data in proprietary object repositories or in a tagged-text format. We describe a technique that can use relational database management systems to store and manage semistructured data. Our technique relies on a mapping between the semistructured data model and the relational data model, expressed in a query language called STORED. When a semistructured data instance is given, a STORED mapping can be generated automatically using data-mining techniques. We are interested in applying STORED to XML data, which is an instance of semistructured data. We show how a document-type-descriptor (DTD), when present, can be exploited to further improve performance.