A locally adaptive data compression scheme
Communications of the ACM
Software—Practice & Experience
Overview of the second text retrieval conference (TREC-2)
TREC-2 Proceedings of the second conference on Text retrieval conference
XMill: an efficient compressor for XML data
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast and flexible word searching on compressed text
ACM Transactions on Information Systems (TOIS)
Information Retrieval: Computational and Theoretical Aspects
Information Retrieval: Computational and Theoretical Aspects
Adding Compression to Block Addressing Inverted Indexes
Information Retrieval
Compressing XML with Multiplexed Hierarchical PPM Models
DCC '01 Proceedings of the Data Compression Conference
Combining Structural and Textual Contexts for Compressing Semistructured Databases
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
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We describe a compression model for semistructured documents, called Structural Contexts Model, which takes advantage of the context information usually implicit in the structure of the text. The idea is to use a separate semiadaptive model to compress the text that lies inside each different structure type (e.g., different XML tag). The intuition behind the idea is that the distribution of all the texts that belong to a given structure type should be similar, and different from that of other structure types. We test our idea using a word-based Huffman coding, which is the standard for compressing large natural language textual databases, and showt hat our compression method obtains significant improvements in compression ratios. We also analyze the possibility that storing separate models may not pay off if the distribution of different structure types is not different enough, and present a heuristic to merge models with the aim of minimizing the total size of the compressed database. This technique gives an additional improvement over the plain technique. The comparison against existing prototypes shows that our method is a competitive choice for compressed text databases.