Synonyms
Query, nearest neighbor; Scan, sequential
Definition
The iDistance is an indexing and query processing technique for k nearest neighbor (kNN) queries on point data in multi-dimensional metric spaces. The kNN query is one of the hardest problems on multi‐dimensional data. It has been shown analytically and experimentally that any algorithm using hierarchical index structure based on either space- or data‐partitioning is less efficient than the naive method of sequentially checking every data record (called the sequential scan) in high‐dimensional spaces [4]. Some data distributions including the uniform distribution are particularly hard cases [1]. The iDistance is designed to process kNN queries in high‐dimensional spaces efficiently and it is especially good for skewed data distributions, which usually occur in real-life data sets. For uniform data, the iDistance beats the sequential scan up to 30 dimensions as reported in [3]. Building the iDistance index has two steps....
Recommended Reading
Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is nearest neighbors meaningful? In: Proceedings of International Conference on Database Theory (1999)
Faloutsos, C., Equitz, W., Flickner, M., Niblack, W., Petkovic, D., Barber, R.: Efficient and Effective Querying by Image Content. J. Intell. Inf. Syst. 3(3), 231–262 (1994)
Jagadish, H.V., Ooi, B.C., Yu, C., Tan, K.-L., Zhang, R.: iDistance: An Adaptive B+‑tree Based Indexing Method for Nearest Neighbor Search. ACM Trans. Data Base Syst. 30(2), 364–397 (2005)
Weber, R., Schek, H., Blott, S.: A quantitative analysis and performance study for similarity‐search methods in high‐dimensional spaces. In: Proceedings of International Conference on Very Large Data Bases (1998)
Yu, C., Ooi, B.C., Tan, K.-L., Jagadish, H.: Indexing the distance: an efficient method to KNN processing. In: Proceedings of International Conference on Very Large Data Bases (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag
About this entry
Cite this entry
Jagadish, H., Ooi, B., Zhang, R. (2008). iDistance Techniques. In: Shekhar, S., Xiong, H. (eds) Encyclopedia of GIS. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35973-1_580
Download citation
DOI: https://doi.org/10.1007/978-0-387-35973-1_580
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30858-6
Online ISBN: 978-0-387-35973-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering