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Comparing the Performance of Collection Selection Algorithms

机译:比较集合选择算法的性能

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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.
机译:在线信息资源的激增增加了在多馆藏环境中进行有效信息检索的重要性。多集合搜索分为三个部分:集合选择(也称为数据库选择),查询处理和结果合并。在这项工作中,我们将注意力集中在第一步的评估上,即馆藏选择。在本文中,我们将详细讨论用于评估和比较馆藏选择方法的方法,涵盖测试环境和评估措施。我们使用六个测试环境和三个文档测试台,比较了CORI,CVV和gGlOSS集合选择方法。我们注意到,在馆藏选择方法中,性能有相似的趋势,但是CORI方法始终优于其他方法,这表明可以使用每个馆藏的有限信息来实现有效的馆藏选择。这项工作的贡献既是组合评估方法,也是该方法在标准化环境中比较馆藏选择方法的应用。

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