III: Medium: Selective Search of Large-Scale Text Collections

该项目为大规模文本搜索开发了另一种架构，在这种架构中，文档语料库被分解为索引碎片，这些索引碎片预计会有倾斜的效用分布，从而使大多数索引分区在大多数查询中被忽略。这种选择性搜索体系结构与传统搜索引擎体系结构一样有效，但计算成本要低得多，并在大规模搜索中揭示了新的挑战和机遇。分解过程创建文本集合，因此需要研究文本集合中需要或避免哪些特征，以实现准确的搜索。为了解决现有算法的效率问题，开发了新的资源选择算法，并根据查询难度动态调整搜索成本。该项目包括与其他大学的三个研究小组合作，以帮助他们的研究，利用他们在设计解决问题的新方法方面的专业知识，并调查我们的研究在更多不同情况下的有效性。其结果是一种“现成的”方法，与当前最先进的方法相比，它的搜索成本降低了一个数量级，特别是在超过10亿个文档的语料库上，并且可以很容易地定制或扩展以支持各种需求。选择性搜索很重要，部分原因是它提供了一种新的视角，可以了解如何组织非常大的文档集合，以便准确有效地进行搜索。这种新的理解揭示了现有算法中新的研究问题和未发现的弱点，这些问题将在科学界产生影响。文本搜索是应用最广泛的计算机科学技术之一；因此，选择性搜索具有重要的现实意义。许多工业和科学领域的最新技术越来越多地与大规模数据集联系在一起，这使得拥有适度计算资源的组织很难与之竞争。这个项目将搜索大规模文本集合的计算成本降低了一个数量级或更多。它有可能减少与大型搜索提供商的数据中心相关的能源和其他成本，这具有重要的经济和社会效益。本项目研究成果通过项目网站（http://www.cs.cmu.edu/~callan/Projects/IIS-1302206/）发布；在研究出版物中；在狐猴项目的开源搜索引擎中，被广泛的国际科学界使用；以及狐猴计划的ClueWeb公共搜索服务，该服务将研究和教育结合起来，使科学家和课堂上的学生能够在大型、最先进的文本语料库上进行实验。

This project develops an alternative architecture for large-scale text search in which the document corpus is decomposed into index shards that are expected to have skewed utility distributions, thus enabling most index partitions to be ignored for most queries. This selective search architecture is as effective as conventional search engine architectures, but has far lower computational costs and reveals new challenges and opportunities in large-scale search. The decomposition process creates text collections, thus inviting research on what characteristics are desired or to be avoided in a text collection to enable accurate search. New resource selection algorithms are developed to address efficiency problems in existing algorithms and dynamically adjust search costs based on query difficulty. The project includes collaboration with three research groups at other universities, to help their research, leverage their expertise in designing new approaches to problems, and investigate the effectiveness of our research in more varied situations. The result is an "off-the-shelf" method that provides an order of magnitude reduction in search costs over the current state-of-the-art, especially on corpora of more than a billion documents, and that can be easily customized or extended to support varied needs.Selective search is significant in part because it provides a new perspective on how to organize a very large collection of documents so that it can be searched accurately and efficiently. This new understanding reveals new research problems and undiscovered weaknesses in existing algorithms that will have impact within the scientific community. Text search is one of the most widely used computer science technologies; hence selective search is of practical significance. The state-of-the-art in many areas of industry and science is increasingly associated with large-scale datasets, which makes it difficult for organizations with modest computational resources to compete. This project reduces the computational costs of searching large-scale text collections by an order of magnitude or more. It has the potential to reduce the energy and other costs associated with the data centers of large search providers, which has important economic and societal benefits. Research results from this project are disseminated via project web site (http://www.cs.cmu.edu/~callan/Projects/IIS-1302206/); in research publications; in the Lemur Project's open-source search engines, which are used by a broad international scientific community; and in the Lemur Project's ClueWeb public search services, which integrate research and education by enabling scientists and classroom students to do experiments on large, state-of-the-art text corpora.