document: LCA-based selection for XML document collections In this paper , we address the problem of database selection for XML document collections , that is , given a set of collections and a user query , how to rank the collections based on their goodness to the query . Goodness is determined by the relevance of the documents in the collection to the query . We consider keyword queries and support Lowest Common Ancestor ( LCA ) semantics for defining query results , where the relevance of each document to a query is determined by properties of the LCA of those nodes in the XML document that contain the query keywords . To avoid evaluating queries against each document in a collection , we propose maintaining in a preprocessing phase , information about the LCAs of all pairs of keywords in a document and use it to approximate the properties of the LCA-based results of a query . To improve storage and processing efficiency , we use appropriate summaries of the LCA information based on Bloom filters . We address both a boolean and a weighted version of the database selection problem . Our experimental results show that our approach incurs low errors in the estimation of the goodness of a collection and provides rankings that are very close to the actual ones .
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