Abstract
In this paper we present a new compression scheme for signature tree structures. Beyond the reduction of storage space, compression attains significant savings in terms of query processing. The latter issue is of critical importance when considering large collections of set valued data, e.g., in object-relational databases, where signature tree structures find important applications. The proposed scheme works on a per node basis, by reorganizing node entries according to their similarity, which results to sparse bit vectors that can be drastically compressed. Experimental results illustrate the efficiency gains due to the proposed scheme, especially for interesting real-world cases, like basket-market data or Web-server logs.
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Kontaki, M., Manolopoulos, Y., Nanopoulos, A. (2003). Compressing Large Signature Trees. In: Kalinichenko, L., Manthey, R., Thalheim, B., Wloka, U. (eds) Advances in Databases and Information Systems. ADBIS 2003. Lecture Notes in Computer Science, vol 2798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39403-7_14
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DOI: https://doi.org/10.1007/978-3-540-39403-7_14
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