The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 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
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Hi-index | 0.00 |
Nowadays large volumes of data with high dimensionality are being generated in many fields. Most existing indexing techniques degrade rapidly when dimensionality goes higher. A large amount of data sets are time related, and the existence of the obsolete data in the data sets may seriously degrade the data processing. In our previous work[7], we proposed ClusterTree+, a new indexing approach representing clusters generated by any existing clustering approach. It is a hierarchy of clusters and subclusters which incorporates the cluster representation into the index structure to achieve effective and efficient retrieval. It also has features from the time perspective. Each new data item is added to the ClusterTree+ with the time information which can be used later in the data update process for the acquisition of the new cluster structure. To improve the performance of this index structure, we propose a dynamic insertion approach for time-related multi-dimensional data based on a modified ClusterTree+, keeping the index structure always in the most updated status which can further promote the efficiency and effectiveness of data query, data update, etc. This approach is highly adaptive to any kind of clusters.