Abstract
Advanced database systems offer similarity queries on complex data. Searching by similarity on complex data is accelerated through the use of metric access methods (MAM). These access methods organize data in order to reduce the number of comparison between elements when answering queries. MAM can be categorized in two types: disk-based and memory-based. The disk-based structures limit the partitioning of space forcing nodes to have multiple elements according to disk page sizes. However, memory-based trees allows more flexibility, producing trees faster to build and to perform queries. Although recent developments target disk-based methods on tree structures, several applications benefits from a faster way to build indexes on main memory. This paper presents a memory-based metric tree, the MM-tree, which successively partitions the space into non-overlapping regions. We present experiments comparing MM-tree with existing high performance MAM, including the disk-based Slim-tree. The experiments reveal that MM-tree requires up to one fifth of the number of distance calculations to be constructed when compared with Slim-tree, performs range queries requiring 64% less distance calculations and KNN queries requiring 74% less distance calculations.
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Pola, I.R.V., Traina, C., Traina, A.J.M. (2007). The MM-Tree: A Memory-Based Metric Tree Without Overlap Between Nodes. In: Ioannidis, Y., Novikov, B., Rachev, B. (eds) Advances in Databases and Information Systems. ADBIS 2007. Lecture Notes in Computer Science, vol 4690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75185-4_13
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DOI: https://doi.org/10.1007/978-3-540-75185-4_13
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