Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
How to improve the pruning ability of dynamic metric access methods
Proceedings of the eleventh international conference on Information and knowledge management
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
Similarity Search without Tears: The OMNI Family of All-purpose Access Methods
Proceedings of the 17th International Conference on Data Engineering
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
The Onion-Tree: Quick Indexing of Complex Data in the Main Memory
ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
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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.