Information Retrieval: Computational and Theoretical Aspects
Information Retrieval: Computational and Theoretical Aspects
Searching large text collections
Handbook of massive data sets
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Lightweight natural language text compression
Information Retrieval
Efficient document retrieval in main memory
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Search Engines: Information Retrieval in Practice
Search Engines: Information Retrieval in Practice
Modern Information Retrieval
Efficient set intersection for inverted indexing
ACM Transactions on Information Systems (TOIS)
Engineering basic algorithms of an in-memory text search engine
ACM Transactions on Information Systems (TOIS)
Compressed self-indices supporting conjunctive queries on document collections
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Implicit indexing of natural language text by reorganizing bytecodes
Information Retrieval
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Ranked document retrieval is a fundamental task in search engines. Such queries are solved with inverted indexes that require additional 45%-80% of the compressed text space, and take tens to hundreds of microseconds per query. In this paper we show how ranked document retrieval queries can be solved within tens of milliseconds using essentially no extra space over an in-memory compressed representation of the document collection. More precisely, we enhance wavelet trees on bytecodes (WTBCs), a data structure that rearranges the bytes of the compressed collection, so that they support ranked conjunctive and disjunctive queries, using just 6%---18% of the compressed text space.