Coding and information theory (2nd ed.)
Coding and information theory (2nd ed.)
Improved techniques for processing queries in full-text systems
SIGIR '87 Proceedings of the 10th annual international ACM SIGIR conference on Research and development in information retrieval
Design and analysis of dynamic Huffman codes
Journal of the ACM (JACM)
Data compression with finite windows
Communications of the ACM
Elements of information theory
Elements of information theory
A systematic approach to compressing a full-text retrieval system
Information Processing and Management: an International Journal - Special issue on data compression for images and texts
Modeling word occurrences for the compression of concordances
ACM Transactions on Information Systems (TOIS)
Managing Gigabytes: Compressing and Indexing Documents and Images
Managing Gigabytes: Compressing and Indexing Documents and Images
Exploiting clustering in inverted file Compression
DCC '96 Proceedings of the Conference on Data Compression
Inverted files for text search engines
ACM Computing Surveys (CSUR)
Analyses of multi-level and multi-component compressed bitmap indexes
ACM Transactions on Database Systems (TODS)
Minimizing index size by reordering rows and columns
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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Bitmaps are a useful, but storage voracious, component of manyinformation retrieval systems. Earlier efforts to compress bitmapswere based on models of bit generation, particularly Markov models.While these permitted considerable reduction in storage, the shortmemory of Markov models may limit their compression efficiency. Inthis paper we accept the state orientation of Markov models, butintroduce a Bayesian approach to assess the state; the analysis isbased on data accumulating in a growing window. The paper describesthe details of the probabilistic assumptions governing the Bayesiananalysis, as well as the protocol for controlling the window thatreceives the data. We find slight improvement over the bestperforming strictly Markov models.