Indexing and querying dataspaces

• Ano de defesa: 2011
• Autor(a) principal: Mergen, Sérgio Luis Sardi
• Orientador(a): Heuser, Carlos Alberto
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Palavras-chave em Português: Recuperacao : Informacao; Banco : Dados
• Palavras-chave em Inglês: Dataspaces; Data integration; Search engine; Indexing; Query rewriting
• Link de acesso: http://hdl.handle.net/10183/31134

Over theWeb, distributed and heterogeneous sources with structured and related content form rich repositories of information commonly referred to as dataspaces. To provide access to this heterogeneous data, information integration systems have traditionally relied on the availability of a mediated schema, along with mappings between this schema and the schema of the source schemas. On dataspaces, where sources are plentiful, autonomous and extremely volatile, a system based on the existence of a pre-defined mediated schema and mapping information presents several drawbacks. Notably, the cost of keeping the mappings up to date as new sources are found or existing sources change can be prohibitively high. We propose a novel querying architecture that requires neither a mediated schema nor source mappings, which is based mainly on indexing mechanisms and on-the-fly rewriting algorithms. Our indexes are designed for data that is represented as relations, and are able to capture the structure of the sources, their instances and the connections between them. In the absence of a mediated schema, the user formulates structured queries based on what she expects to find. These queries are rewritten using a best-effort approach: the proposed rewriting algorithms compare a user query against the source schemas and produces a set of rewritings based on the matches found. Based on this architecture, two different querying approaches are tested. Experiments show that the indexing and rewriting algorithms are scalable, i.e., able to handle a very large number of structured Web sources; and that support simple, yet expressive queries that exploit the inherent structure of the data.