Center Selection Techniques for Metric Indexes
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| Format: | Artículo científico |
| Sprache: | en |
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Universidad Nacional de La Plata
2007
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| _version_ | 1876445412589568000 |
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| author | Cristian Mendoza Alric |
| author_facet | Cristian Mendoza Alric |
| contents | Center Selection Techniques for Metric Indexes Cristian Mendoza Alric Norma Edith Herrera Computación Index Databases Metric Spaces Similarity Search Centers Selection The metric spaces model formalizes the similarity search concept in nontraditional databases. The goal is to build an index designed to save distance computations when answering similarity queries later. A large class of algorithms to build the index are based on partitioning the space in zones as compact as possible. Each zone stores a representative point, called center, and a few extra data that allow to discard the entire zone at query time without measuring the actual distance between the elements of the zone and the query object. The way in which the centers are selected affects the performance of the algorithm. In this paper, we introduce two new center selection techniques for compact partition based indexes. These techniques were evaluated using the Geometric Near-neighbor Access Tree (GNAT). We experimentally showed that they achieve good performance. 2007 artículo científico 1666-6046 https://www.redalyc.org/articulo.oa?id=638067340013 en http://www.redalyc.org/revista.oa?id=6380 Journal of Computer Science and Technology application/pdf Universidad Nacional de La Plata Journal of Computer Science and Technology (Argentina) Num.01 Vol.7 |
| format | Artículo científico |
| id | redalyc_638067340013 |
| institution | Redalyc |
| language | en |
| publishDate | 2007 |
| publisher | Universidad Nacional de La Plata |
| spellingShingle | Center Selection Techniques for Metric Indexes Cristian Mendoza Alric Computación Index Databases Metric Spaces Similarity Search Centers Selection Center Selection Techniques for Metric Indexes Cristian Mendoza Alric Norma Edith Herrera Computación Index Databases Metric Spaces Similarity Search Centers Selection The metric spaces model formalizes the similarity search concept in nontraditional databases. The goal is to build an index designed to save distance computations when answering similarity queries later. A large class of algorithms to build the index are based on partitioning the space in zones as compact as possible. Each zone stores a representative point, called center, and a few extra data that allow to discard the entire zone at query time without measuring the actual distance between the elements of the zone and the query object. The way in which the centers are selected affects the performance of the algorithm. In this paper, we introduce two new center selection techniques for compact partition based indexes. These techniques were evaluated using the Geometric Near-neighbor Access Tree (GNAT). We experimentally showed that they achieve good performance. 2007 artículo científico 1666-6046 https://www.redalyc.org/articulo.oa?id=638067340013 en http://www.redalyc.org/revista.oa?id=6380 Journal of Computer Science and Technology application/pdf Universidad Nacional de La Plata Journal of Computer Science and Technology (Argentina) Num.01 Vol.7 |
| title | Center Selection Techniques for Metric Indexes |
| topic | Computación Index Databases Metric Spaces Similarity Search Centers Selection |
| url | https://www.redalyc.org/articulo.oa?id=638067340013 |