Faster temporal range queries over versioned text
|Faster temporal range queries over versioned text|
|Author(s)||He J., Suel T.|
|Published in||SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Keyword(s)||Inverted index, Query processing, Range queries, Temporal search, Versioned documents (Extra: Disk-based, Index compression, Index partitioning, Index structure, Internet archive, Inverted index compression, Inverted indices, Range query, Search queries, Simple approach, Temporal constraints, Temporal search, Time range, Versioned documents, Web collections, Wikipedia, User interfaces, Information retrieval)|
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Faster temporal range queries over versioned text is a 2011 conference paper written in English by He J., Suel T. and published in SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.
Versioned textual collections are collections that retain multiple versions of a document as it evolves over time. Important large-scale examples are Wikipedia and the web collection of the Internet Archive. Search queries over such collections often use keywords as well as temporal constraints, most commonly a time range of interest. In this paper, we study how to support such temporal range queries over versioned text. Our goal is to process these queries faster than the corresponding keyword-only queries, by exploiting the additional constraint. A simple approach might partition the index into different time ranges, and then access only the relevant parts. However, specialized inverted index compression techniques are crucial for large versioned collections, and a naive partitioning can negatively affect index compression and query throughput. We show how to achieve high query throughput by using smart index partitioning techniques that take index compression into account. Experiments on over 85 million versions of Wikipedia articles show that queries can be executed in a few milliseconds on memory-based index structures, and only slightly more time on disk-based structures. We also show how to efficiently support the recently proposed stable top-k search primitive on top of our schemes.
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