Abstract
Modern business appliications require huge volumes of highdimensioinal data to be Stored. explorative queries, typically used in these applications, select groups of objects with similar attributes or attribute combinations. In contrast to multidimensional index structures designed for spatial data that assume dimension independence and very often a uniform distribution, we have developed a new database indexing concept that discovers correlation patterns and takes the nonuniform distribution into consideration. The corresponding analysis is done on the subsymbolic level by applying a hierarchical artificial neural network. The trained neural network organises the data into a hierarchy of clusters. The clusters can be interpreted as groups of similar objects on the symbolic level. The hierarchy is finally used to derive the Intelligent Cluster Index (ICIx). In this paper we present a description of the Intelligent Cluster Index, it’s creation and application as multidimensional index and as heuristic for a logical distribution schema. We describe first experimental results, showing that this new approach can significantly speed up the system performance.
This work is supported by the Deutsche Forschungsgemeinschaft (DFG) under contract reference SFB 457-00.
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
N. Beckmann et al., The R*-tree: An Efficient and Robust Access Method for Points and Rectangles, in: SIGMOD Record Vol. 19(2), ACM Press, 1990
W. Benn, O. Görlitz, Semantic Navigation Maps for Information Agents, 2nd Int’l. Workshop on Cooperative Information Agents CIA-98. Paris,.July 1998.
S. Berclitold et al., The X-tree: An Index Structure for High-Dimensional Data, in: Proc. of the 22nd VLDB Conf., Mumbay, India, Morgan Kaufmann 1996
S. Berclitold et al., Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces, in: Proc. of the 16th Int’l. Conf. on Data Engineering (ICDE), San Diego, USA, IEEE Computer Society, 2000
E. Bertino et al., Indexing Techniques for Advanced Database Systems, Kluwer Academic Publishers Boston, Dordrecht, London 1997
K. Beyer et al., When Is “(Nearest Neighbor“ Meaningful? in: Lecture Notes in Computer Science 1540, pp. 217–235, Springer 1999
J. P. Bigus, Data Mining with Neural Networks, The McGraw-Hill Companies, 1996
V. Burzevski, C. K. Mohan, Hierarchical Growing Cell Structures, Proc. IEEE Int’l. Conf. on Neural Networks, pp. 1658–63, 1996
P. Dadam Verteilte Datenbanken und Client/Server-Systeme: Grundlagen, Konzepte, Rcalisierungsfoimen, 1996, Springer Verlag Berlin
B. Fritzke, A growing neural gas network learns topologies, 1994, Proc. 1994 NIPS Conf., Denver
B. Fritzke, Vektorbasierte Neuronale Netze, 1998, Shaker Verlag
S. Gilg, R. Neubert, Schemavergleich mit Hilfe Neuronaler Netze, 1998, Studienarbeit, TU Chemnitz
O. Görlitz, R. Neubert, W. Benn, Access to Distributed Environmental Databases with ICIx Technology, Online Information Review Journal, Vol. 24 Issue 5, 2000 MCB University Press
A. Guttman, R-Trees: A Dynamic Index Structure For Spatial Searching, in: SIGMOD Record 14(2), pp. 47–57, ACM Press 1984
A. Henrich, Der LSD-Baum: eine mehrdimensionale Zugriffsstruktur und ihre Einsatzmöglichkeiten in Datenbanksystemen, 1990, Dissertation, FernUniversität-Gesamthochschule—Hagen
A. Henrich, H.-W. Six, How to split buckets in spatial data structures, 1991, Proc. Int’l. Conf. on Geographic Database Management Systems, Esprit Basic Research Series DG XIII, Springer Verlag
A. Henrich, A. Hilbert, P. Widmayer, Anbindung einer räumlich clustemden Zugriffsstruktur für geometrische Attribute an ein Standard-Datenbanksystem am Bsp. Oracle, 1991, Proc. Gl-Fachtagung BTW 91, Springer Informatik Fachbericht 270, pp. 161–177
N. Katayama, S. Satoh, SR-Tree: an index structure for nearest neighbor searching of highdimensional point data, 1998, Journal: Systems and Computers in Japan, Vol. 29, Iss. 5, pp. 59–73
T. Kohoncn, Self-Organization of Very Large Document Collections: State of the Art, 1998, Proc. Int’l. Conf. on Arlifical Neural Networks 1CANN-98
S. Krumbiegel, Performanzvergleich künstlicher Neuronaler Netze bei unterschiedlicher Hardwareunterstützung, 1999, Studienarbeit, TU Chemnitz
W. Li, C. Clifton, Semantic Integration in Heterogeneous Databases Using Neural Networks, Proc. of the 20th VLDB Conf., Santiago, Chile, 1994
T. Martinetz, K. Schulten, A “neural gas“ network learns topologies, 1991, Artifical Neural Networks, North-Holland, Amsterdam, pp. 397–402
D. Merkl, Exploration of Text Collections with Hierarchical Feature Maps, 1997, Proc. ACM SIGIR 97, Philadelphia, USA
R. Neubert, O. Görlitz, W. Benn, Incorporating Knowledge Technology in Databases-the Intelligent Cluster index, KnowTech 2000 Conference and Exihibition, http://www.knowtech.net
J. Nievergelt, II. Hinterberger, K. C. Sevcik, The grid file: An adaptable, symmetric multikey file structure, 1984, ACM Transactions on Database Systems, Vol 9(1), pp. 38–71
OASIS: Open Architecture Server for Information Search, http://www.oasis-europe.org/
J. Rahmel, SplitNet: Learning of Tree Structured Kohonen Chains, 1996, Proc. ICNN 96, Washington
J. Rahmel, On the Role of Topology for Neural Network Interpretation, 1996 Proc. ECAI 96, John Wiley&Sons Ltd.
H. Ritter, T. Kohonen, Self-Organizing Semantic Maps, 1989, Biological Cybernetics 61:(4), pp. 241–254, Springer Verlag
Y. Sakurai et al., The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation, Proc. of the 26th VLDB Conf., Cairo, Egypt, 2000
P. Schcucnnann, Wen-Syan Li, C. Clifton, Multidatabase Query Processing with Uncertainly in Global Keys and Attribute Values, 1998, Journal of the American Society for Information Science Vol. 49(3), pp. 283–301
J. Zavrel, Neural Information Retrieval, 1995, Thesis, University of Amsterdam
J. Zavrel, Neural Navigation Interfaces for Information Retrieval: Are they more than an Appealing Idea, 1996, Artificial Intelligence Review 10, pp. 477–504
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Neubert, R., Görlitz, O., Benn, W. (2001). Towards Content-Related Indexing in Databases. In: Heuer, A., Leymann, F., Priebe, D. (eds) Datenbanksysteme in Büro, Technik und Wissenschaft. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56687-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-56687-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41707-1
Online ISBN: 978-3-642-56687-5
eBook Packages: Springer Book Archive