SYSTEM AND METHOD FOR QUERYING MULTIPLE, DISTRIBUTED DATABASES BY SELECTIVE SHARING OF LOCAL RELATIVE SIGNIFICANCE INFORMATION FOR TERMS RELATED TO THE QUERY

A system, method, and various software products provide improved information retrieval performance from multiple document databases by retrieving from the multiple document databases in response to a user query, a set of documents that globally satisfy the query, even though each database maintains independent document indices, term frequency information, and scoring functions. The global search result approximates, to any desired degree of error, the search results that would have been obtained had the multiple document databases been globally indexed. This is done by sharing at the time the query is executed, a small subset of information about the local relative significance of terms related to the user's query, and from this information, determining a global relative significance of such terms. From the global relative significance, the individual document databases determine their query results, which are then merged into a global set of documents satisfying the query. The shared local relative significance information may be the inverse document frequency of each of a number of terms related to the query, or it may be the total frequency of each of such terms. The global relative significance may correspondingly be a global inverse document frequency, or a global term frequency from which the global inverse document frequency is calculated.