Collection selection for managed distributed document databases

In a distributed document database system, a query is processed by passing it to a set of individual collections and collating the responses. For a system with many such collections, it is attractive to first identify a small subset of collections as likely to hold documents of interest before interrogating only this small subset in more detail. A method for choosing collections that has been widely investigated is the use of a selection index, which captures broad information about each collection and its documents. In this paper, we re-evaluate several techniques for collection selection. We have constructed new sets of test data that reflect one way in which distributed collections would be used in practice, in contrast to the more artificial division into collections reported in much previous work. Using these managed collections, collection ranking based on document surrogates is more effective than techniques such as CORI that are based on collection lexicons. Moreover, these experiments demonstrate that conclusions drawn from artificial collections are of questionable validity.