Comparing the Performance of Collection Selection Algorithms

摘要

The proliferation of online information resources increases the importance of effective and efficient information retrieval in a multicollection environment. Multicollection searching is cast in three parts: collection selection (also referred to as database selection), query processing and results merging. In this work, we focus our attention on the evaluation of the first step, collection selection. In this article, we present a detailed discussion of the methodology that we used to evaluate and compare collection selection approaches, covering both test environments and evaluation measures. We compare the CORI, CVV and gGlOSS collection selection approaches using six test environments utilizing three document testbeds. We note similar trends in performance among the collection selection approaches, but the CORI approach consistently outperforms the other approaches, suggesting that effective collection selection can be achieved using limited information about each collection. The contributions of this work are both the assembled evaluation methodology as well as the application of that methodology to compare collection selection approaches in a standardized environment.