Building a robust and compact search index

Vladislav Savchuk, Stanislav Protasov
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Abstract

With exponential data growth search engines require more memory for storage and time for search. The data is indexed to increase search speed, which requires additional memory. In this study we develop a fully functional search engine for Wikipedia articles and compare different indexing techniques. Using vector quantization for compression we fit an index into a single machine’s RAM. Moreover, we show that by using metadata and additional search for the out-of-vocabulary words we improve the overall system’s quality.
构建健壮且紧凑的搜索索引
随着数据呈指数级增长,搜索引擎需要更多的存储内存和搜索时间。对数据进行索引以提高搜索速度,这需要额外的内存。在本研究中,我们为维基百科文章开发了一个功能齐全的搜索引擎,并比较了不同的索引技术。使用矢量量化进行压缩,我们将索引放入单个机器的RAM中。此外,我们表明,通过使用元数据和额外搜索词汇表外的单词,我们提高了整个系统的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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