Abstract
Different indexing techniques have been proposed to index either the continuous data space (CDS) or the non-ordered discrete data space (NDDS). However, modern database applications sometimes require indexing the hybrid data space (HDS), which involves both continuous and non-ordered discrete subspaces. In this paper, the structure and heuristics of the ND-tree, which is a recently-proposed indexing technique for NDDSs, are first extended to the HDS. A novel power value adjustment strategy is then used to make the continuous and discrete dimensions comparable and controllable in the HDS. An estimation model is developed to predict the box query performance of the hybrid indexing. Our experimental results show that the original ND-tree’s heuristics are effective in supporting efficient box queries in the hybrid data space, and could be further improved with our proposed strategies to address the unique characteristics of the HDS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. In: Proceedings of ACM SIGMOD, pp. 322–331 (1990)
Catlett, J.: On changing continuous attributes into ordered discrete attributes. In: Proceedings of the European Working Session on Machine Learning, pp. 164–178 (1991)
Chakrabarti, K., Mehrotra, S.: The hybrid tree: an index structure for high dimensional feature spaces. In: Proceedings of the 15th International Conference on Data Engineering, pp. 440–447 (1999)
Chen, C., Pramanik, S., Watve, A., Zhu, Q., Qiang, G.: The C-ND Tree: A Multidimensional Index for Hybrid Continuous and Non-ordered Discrete Data Spaces. In: Proceedings of the 12th International Conference on Extending Database Technology (2009)
Freitas, A.A.: A survey of evolutionary algorithms for data mining and knowledge discovery. In: Advances in Evolutionary Computing: Theory and Applications, pp. 819–845 (2003)
Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proceedings of ACM SIGMOD, pp. 47–57 (1984)
Henrich, A.: The LSDh-tree: an access structure for feature vectors. In: Proceedings of the 14th International Conference on Data Engineering, pp. 362–369 (1998)
Macskassy, S.A., Hirsh, H., Banerjee, A., Dayanik, A.A.: Converting numerical classification into text classification. Artificial Intelligence 143(1), 51–77 (2003)
Qian, G., Zhu, Q., Xue, Q., Pramanik, S.: The ND-tree: a dynamic indexing technique for multidimensional non-ordered discrete data spaces. In: Proceedings of the 29th International Conference on VLDB, pp. 620–631 (2003)
Qian, G., Zhu, Q., Xue, Q., Pramanik, S.: Dynamic indexing for multidimensional non-ordered discrete data spaces using a data-partitioning approach. Proceedings of ACM Transactions on Database Systems 31(2), 439–484 (2006)
Qian, G., Zhu, Q., Xue, Q., Pramanik, S.: A space-partitioning-based indexing method for multidimensional non-ordered discrete data spaces. ACM Trans. on Information Syst. 23(1), 79–110 (2006)
Robinson, J.T.: The K-D-B-tree: a search structure for large multidimensional dynamic indexes. In: Proceedings of ACM SIGMOD, pp. 10–18 (1981)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, C., Pramanik, S., Zhu, Q., Qian, G. (2009). A Study of Indexing Strategies for Hybrid Data Spaces. In: Filipe, J., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2009. Lecture Notes in Business Information Processing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01347-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-01347-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01346-1
Online ISBN: 978-3-642-01347-8
eBook Packages: Computer ScienceComputer Science (R0)