This Article 
 Bibliographic References 
 Add to: 
A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns
June 2008 (vol. 20 no. 6)
pp. 825-835
A number of algorithms have been proposed for the discovery of temporal patterns. However, since the number of generated patterns can be large, selecting which patterns to analyze can be non-trivial. There is thus a need for algorithms and tools that can assist in the selection of discovered patterns so that subsequent analysis can be performed in an efficient and, ideally, interactive manner. In this paper, we propose a signature-based indexing method, to optimise the storage and retrieval of a large collection of relative temporal patterns.

[1] T. Imielinski and A. Virmani, “Association Rules $\ldots$ and What's Next? Towards Second Generation Data Mining Systems,” Proc. Second East European Symp. Advances in Databases and Information Systems (ADBIS '98), pp. 6-25, 1998.
[2] L. Geng and H.J. Hamilton, “Interestingness Measures for Data Mining: A Survey,” ACM Computing Surveys, vol. 38, no. 3, 2006.
[3] H. Toivonen, M. Klemettinen, P. Ronkainen, K. Hatonen, and H. Mannila, “Pruning and Grouping of Discovered Association Rules,” Proc. ECML Workshop Statistics, Machine Learning, and Knowledge Discovery in Databases, pp. 47-52, 1995.
[4] B. Lent, A.N. Swami, and J. Widom, “Clustering Association Rules,” Proc. 13th Int'l Conf. Data Eng. (ICDE '97), W.A. Gray and P.-Å. Larson, eds., pp. 220-231, 1997.
[5] B. Liu, W. Hsu, and Y. Ma, “Pruning and Summarizing the Discovered Associations,” Proc. ACM SIGKDD '99, pp. 125-134, 1999.
[6] B. Liu, M. Hu, and W. Hsu, “Multi-Level Organization and Summarization of the Discovered Rules,” Proc. ACM SIGKDD, 2000.
[7] M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, and A. Verkamo, “Finding Interesting Rules from Large Sets of Discovered Association Rules,” Proc. Third Int'l Conf. Information and Knowledge Management (CIKM '94), N. Adam, B. Bhargava, and Y.Yesha, eds., pp. 401-407, 1994.
[8] R. Meo, G. Psaila, and S. Ceri, “A New SQL-Like Operator for Mining Association Rules,” Proc. 22nd Int'l Conf. Very Large Data Bases (VLDB '96), M.T. Vijayaramam, A. Buchmann, C. Mohan, and L.N. Sarda, eds., pp. 122-133, 1996.
[9] J. Han, J.Y. Chiang, S. Chee, J. Chen, Q. Chen, S. Cheng, W. Gong, M. Kamber, K. Koperski, G. Liu, Y. Lu, N. Stefanovic, L. Winstone, B.B. Xia, O.R. Zaiane, S. Zhang, and H. Zhu, “DBMiner: A System for Data Mining in Relational Databases and Data Warehouses,” Proc. ACM SIGKDD, 1996.
[10] A. Netz, S. Chaudhuri, U.M. Fayyad, and J. Bernhardt, “Integrating Data Mining with SQL Databases: OLE DB for Data Mining,” Proc. 17th Int'l Conf. Data Eng. (ICDE'01), pp. 379-387, 2001.
[11] T. Imielinski and A. Virmani, “MSQL: A Query Language for Database Mining,” J. Data Mining and Knowledge Discovery, vol. 3, no. 4, pp. 373-408, 1999.
[12] A. Tuzhilin and B. Liu, “Querying Multiple Sets of Discovered Rules,” Proc. ACM SIGKDD '02, pp. 52-60, 2002.
[13] C.M. Antunes and A.L. Oliveira, “Temporal Data Mining: An Overview,” Proc. ACM SIGKDD Workshop Temporal Data Mining, pp. 1-13, 2001.
[14] X. Chen and I. Petrounias, “A Framework for Temporal Data Mining,” Proc. Ninth Int'l Conf. Database and Expert Systems Applications (DEXA '98), pp. 796-805, 1998.
[15] J.F. Roddick and M. Spiliopoulou, “A Survey of Temporal Knowledge Discovery Paradigms and Methods,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 4, pp. 750-767, Mar./Apr. 2002.
[16] F. Höppner, “Learning Temporal Rules from State Sequences,” Proc. IJCAI Workshop Learning from Temporal and Spatial Data, pp.25-31, 2001.
[17] E. Winarko and J.F. Roddick, “ARMADA—An Algorithm for Discovering Richer Relative Temporal Association Rules from Interval-Based Data,” Data and Knowledge Eng., vol. 63, no. 1, pp.76-90, 2007.
[18] D. Comer, “The Ubiquitous B-Tree,” Computing Surveys, vol. 11, no. 2, pp. 121-137, 1979.
[19] A. Guttman, “R-Trees: A Dynamic Index Structure for Spatial Searching,” Proc. ACM SIGMOD '84, pp. 47-57, 1984.
[20] S. Helmer and G. Moerkotte, “A Performance Study of Four Index Structures for Set-Valued Attributes of Low Cardinality,” VLDB J., vol. 12, no. 3, pp. 244-261, 2003.
[21] Y. Ishikawa, H. Kitagawa, and N. Ohbo, “Evaluation of Signature Files as Set Access Facilities in OODBs,” Proc. ACM SIGMOD '93, P. Buneman and S. Jajodia, eds., pp. 247-256, 1993.
[22] T. Morzy and M. Zakrzewicz, “Group Bitmap Index: A Structure for Association Rules Retrieval,” Proc. ACM SIGKDD '98, R.Agrawal, P. Stolorz, and G. Piatetsky-Shapiro, eds., pp. 284-288, 1998.
[23] U. Deppisch, “S-Tree: A Dynamic Balanced Signature Index for Office Retrieval,” Proc. ACM SIGIR '86, pp. 77-87, 1986.
[24] N. Mamoulis, D.W. Cheung, and W. Lian, “Similarity Search in Sets and Categorical Data Using the Signature Tree,” Proc. 19th Int'l Conf. Data Eng. (ICDE '03), U. Dayal, K. Ramamritham, and T.Vijayaraman, eds., pp. 75-86, 2003.
[25] N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, and D.W. Cheung, “Mining, Indexing, and Querying Historical Spatiotemporal Data,” Proc. ACM SIGKDD '04, pp. 236-245, 2004.
[26] D.L. Lee and C.-W. Leng, “A Partitioned Signature File Structure for Multiattribute and Text Retrieval,” Proc. Sixth Int'l Conf. Data Eng. (ICDE '90), pp. 389-397, 1990.
[27] F. Rabitti and P. Zezula, “A Dynamic Signature Technique for Multimedia Databases,” Proc. ACM SIGIR '90, J.-L. Vidick, ed., pp.193-210, 1990.
[28] P. Zezula, F. Rabitti, and P. Tiberio, “Dynamic Partitioning of Signature Files,” ACM Trans. Information Systems, vol. 9, no. 4, pp.336-367, 1991.
[29] T. Morzy, M. Wojciechowski, and M. Zakrzewicz, “Optimizing Pattern Queries for Web Access Logs,” Proc. Fifth East European Conf. Advances in Databases and Information Systems (ADBIS '01), pp. 141-154, 2001.
[30] A. Nanopoulos, M. Zakrzewicz, T. Morzy, and Y. Manolopoulos, “Efficient Storage and Querying of Sequential Patterns in Database Systems,” Information and Software Technology, vol. 45, pp. 23-34, 2003.
[31] M. Zakrzewicz, “Sequential Index Structure for Content-Based Retrieval,” Proc. Fifth Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD '01), pp. 306-311, 2001.
[32] J. Allen, “Maintaining Knowledge about Temporal Intervals,” Comm. ACM, vol. 26, no. 11, pp. 832-843, 1983.
[33] J. Xiao, Y. Zhang, X. Jia, and T. Li, “Measuring Similarity of Interests for Clustering Web-Users,” Proc. 12th Australasian Database Conf. (ADC '01), M. Orlowska and J. Roddick, eds., pp.107-114, 2001.
[34] C. Faloutsos and S. Christodoulakis, “Signature Files: An Access Method for Documents and Its Analytical Performance Evaluation,” ACM Trans. Office Information Systems, vol. 2, no. 4, pp. 267-288, 1984.
[35] Y. Chen, “On the General Signature Trees,” Proc. 16th Int'l Conf. Database and Expert Systems Applications (DEXA '05), pp. 207-219, 2005.
[36] H. Kitagawa, Y. Fukushima, Y. Ishikawa, and N. Ohbo, “Estimation of False Drops in Set-Valued Object Retrieval with Signature Files,” Proc. Fourth Int'l Conf. Foundations of Data Organization and Algorithms (FODO '93), pp. 146-163, 1993.
[37] Y. Chen, “Building Signature Trees into OODBs,” J. Information Science and Eng., vol. 20, no. 2, pp. 275-304, 2004.
[38] J. Yang and M. Hu, “Trajpattern: Mining Sequential Patterns from Imprecise Trajectories of Mobile Objects,” Proc. 10th Int'l Conf. Extending Database Technology (EDBT '06), pp. 664-681, 2006.

Index Terms:
Data Storage Representations, Indexing methods, Temporal databases
Edi Winarko, John F. Roddick, "A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns," IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 6, pp. 825-835, June 2008, doi:10.1109/TKDE.2008.20
Usage of this product signifies your acceptance of the Terms of Use.