Improving text collection selection with coverage and overlap statistics

In an environment of distributed text collections, the first step in the information retrieval process is to identify which of all available collections are more relevant to a given query and which should thus be accessed to answer the query. We address the challenge of collection selection when there is full or partial overlap between the available text collections, a scenario which has not been examined previously despite its real-world applications. To that end, we present COSCO, a collection selection approach which uses collection-specific coverage and overlap statistics. We describe our experimental results which show that the presented approach displays the desired behavior of retrieving more new results early on in the collection order, and performs consistently and significantly better than CORI, previously considered to be one of the best collection selection systems.