Modeling word occurrences for the compression of concordances
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Managing Gigabytes: Compressing and Indexing Documents and Images
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Inverted file compression through document identifier reassignment
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Index Compression through Document Reordering
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Assigning identifiers to documents to enhance the clustering property of fulltext indexes
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Inverted Index Compression Using Word-Aligned Binary Codes
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Simplified similarity scoring using term ranks
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
The automatic creation of literature abstracts
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Document identifier reassignment through dimensionality reduction
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Entry Pairing in Inverted File
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ACM Transactions on Information Systems (TOIS)
Scalable techniques for document identifier assignment in inverted indexes
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VSEncoding: efficient coding and fast decoding of integer lists via dynamic programming
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Improved index compression techniques for versioned document collections
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Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Optimized top-k processing with global page scores on block-max indexes
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Reordering an index to speed query processing without loss of effectiveness
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Optimizing top-k document retrieval strategies for block-max indexes
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A candidate filtering mechanism for fast top-k query processing on modern cpus
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Document vector representations for feature extraction in multi-stage document ranking
Information Retrieval
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Information Retrieval
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The compression of Inverted File indexes in Web Search Engines has received a lot of attention in these last years. Compressing the index not only reduces space occupancy but also improves the overall retrieval performance since it allows a better exploitation of the memory hierarchy. In this paper we are going to empirically show that in the case of collections of Web Documents we can enhance the performance of compression algorithms by simply assigning identifiers to documents according to the lexicographical ordering of the URLs. We will validate this assumption by comparing several assignment techniques and several compression algorithms on a quite large document collection composed by about six million documents. The results are very encouraging since we can improve the compression ratio up to 40% using an algorithm that takes about ninety seconds to finish using only 100 MB of main memory.