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Distributed search based on self-indexed compressed text

机译：基于自索引压缩文本的分布式搜索

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摘要

Query response times within a fraction of a second in Web search engines are feasible due to the use of indexing and caching techniques, which are devised for large text collections partitioned and replicated into a set of distributed-memory processors. This paper proposes an alternative query processing method for this setting, which is based on a combination of self-indexed compressed text and posting lists caching. We show that a text self-index (i.e., an index that compresses the text and is able to extract arbitrary parts of it) can be competitive with an inverted index if we consider the whole query process, which includes index decompression, ranking and snippet extraction time. The advantage is that within the space of the compressed document collection, one can carry out the posting lists generation, document ranking and snippet extraction. This significantly reduces the total number of processors involved in the solution of queries. Alternatively, for the same amount of hardware, the performance of the proposed strategy is better than that of the classical approach based on treating inverted indexes and corresponding documents as two separate entities in terms of processors and memory space.
机译：由于使用了索引和缓存技术，因此在Web搜索引擎中不到一秒钟的查询响应时间是可行的，该技术是为分区和复制到一组分布式内存处理器中的大型文本集合设计的。本文提出了一种针对此设置的替代查询处理方法，该方法基于自索引压缩文本和发布列表缓存的组合。我们表明，如果考虑整个查询过程（包括索引解压缩，排名和代码段），则文本自索引（即压缩文本并能够提取文本的任意部分的索引）可以与倒排索引竞争。提取时间。优点是，在压缩文档集合的空间内，可以执行过帐列表生成，文档排名和摘录。这大大减少了查询解决方案中涉及的处理器总数。或者，对于相同数量的硬件，在处理器和内存空间方面，所提出策略的性能优于经典方法的性能，该经典方法基于将倒排索引和相应文档视为两个单独的实体。

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