Slim-Trees: High Performance Metric Trees Minimizing Overlap between Nodes
In this paper we present the Slim-tree, a dynamic tree for organizing metric datasets in pages of fixed size. The Slim-tree uses the “fat-factor” which provides a simple way to quantify the degree of overlap between the nodes in a metric tree. It is well-known that the degree of overlap directly affects the query performance of index structures. There are many suggestions to reduce overlap in multidimensional index structures, but the Slim-tree is the first metric structure explicitly designed to reduce the degree of overlap.
Moreover, we present new algorithms for inserting objects and splitting nodes. The new insertion algorithm leads to a tree with high storage utilization and improved query performance, whereas the new split algorithm runs considerably faster than previous ones, generally without sacrificing search performance. Results obtained from experiments with real-world data sets show that the new algorithms of the Slim-tree consistently lead to performance improvements. After performing the Slim-down algorithm, we observed improvements up to a factor of 35% for range queries.
KeywordsMinimal Span Tree Index Structure Distance Calculation Range Query Point Query
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- 2.Ciaccia, P., Patella, M., Zezula, P.: M-tree: An Efficient Access Method for Similarity Search in Metric Spaces, VLDB (1997) 426–435.Google Scholar
- 5.Yianilos, P. N.: Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces. ACM SODA (1993) 311–321.Google Scholar
- 6.Baeza-Yates, R.A., Cunto, W., Manber, U., Wu S.: Proximity Matching Using Fixed-Queries Trees. CPM, (1994) 198–212.Google Scholar
- 7.Bozkaya, T., Özsoyoglu, Z.M. Distance-Based Indexing for High-Dimensional Metric Spaces, ACM-SIGMOD (1997) 357–368.Google Scholar
- 8.Brin S.: Near Neighbor Search in Large Metric Spaces, VLDB (1995) 574–584.Google Scholar
- 9.Guttman A.: R-Tree: Adynamic Index Structure for Spatial Searching. ACMSIGMOD (1984) 47–57.Google Scholar
- 10.Ciaccia, P., Patella, M.: Bulk Loading the M-tree. ADC’98 (1998) 15–26.Google Scholar
- 12.Ciaccia, P., Patella, M., Rabitti, F., Zezula, P.: Indexing Metric Spaces with M-tree. Proc. Quinto convegno Nazionale SEBD (1997).Google Scholar
- 13.Faloutsos, C., Kamel, L.: Beyond Uniformity and Independence: Analysis of R-tree Using the Concept of Fractal Dimension. ACM-PODS (1994) 4–13.Google Scholar
- 14.Traina Jr., C., Traina, A., Faloutsos, C.: Distance Exponent: A New Concept for Selectivity Estimation in Metric Trees. CMU-CS-99-110 Technical Report (1999).Google Scholar
- 15.Sellis, T., Roussopoulos, N., Faloutsos, C.: The R+-tree: A Dynamic Index for Multidimensional Objects. VLDB (1987) 507–518.Google Scholar
- 16.Beckmann, N., Kriegel, H.-P., Schneider R., Seeger, B.: The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. ACM-SIGMOD (1990) 322–331.Google Scholar
- 17.Berchtold, S., Böhm, C., Keim, D.A., Kriegel, H.-P.: A Cost Model For Nearest Neighbor Search in High-Dimensional Data Space. ACM-PODS (1997) 78–86.Google Scholar
- 18.Wactlar, H.D., Kanade, T., Smith, M.A., Stevens, S.M.: Intelligent Access to Digital Video: Informedia Project. IEEE Computer, 29(3) (1996) 46–52.Google Scholar
- 19.Visionics Corp.-Available at http://www.visionics.com/live/frameset.html (12-Feb-1999).