Querying Semistructured Heterogeneous Information

Semistructured data has no absolute schema xed in advance and its structure may be irregular or incomplete. Such data commonly arises in sources that do not impose a rigid structure (such as the World-Wide Web) and when data is combined from several heterogeneous sources. Data models and query languages designed for well structured data are inappropriate in such environments. Starting with a \lightweight" object model adopted for the TSIMMIS project at Stanford, in this paper we describe a query language and object repository designed speci cally for semistructured data. Our language provides meaningful query results in cases where conventional models and languages do not: when some data is absent, when data does not have regular structure, when similar concepts are represented using di erent types, when heterogeneous sets are present, and when object structure is not fully known. This paper motivates the key concepts behind our approach, describes the language through a series of examples (a complete semantics is available in an accompanying technical report [QRS94]), and describes the basic architecture and query processing strategy of the \lightweight" object repository we have developed.