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Finding Frequent and Maximal Periodic Patterns in Spatiotemporal Databases for Shifted Instances

O. Obulesu, A. Rama Mohan Reddy


Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the event-types whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types.  Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences.  In this paper, a new framework is proposed to find spatiotemporal patterns from Big Data. Existing algorithms are well in computation of necessary patterns, but more problematic when they are applied to Big Data. Big Data is a new trend used to analyse the datasets that due to their large size and complexity, Developers cannot manage them with traditional current algorithms or data mining software tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variety, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. This Paper focuses on a broad overview of pattern mining algorithms and significance in Spatiotemporal Databases, its current status, trade-offs, and forecast to the big data pattern mining future.


Periodicity Detection, Spatial Patterns, Big Data, Cascading Spatiotemporal Pattern Discovery, MapR

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Rakesh Agrawal Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. Proceedings of the 20th VLDB Conference Santiago, Chile, 1994.

R. Srikant, R. Agrawal: "Mining Sequential Patterns: Generalizations and Performance Improvements", Proc. of the Fifth Int'l Conference on Extending Database Technology (EDBT), Avignon, France, March 1996. Expanded version available as IBM Research Report RJ 9994, December 1995.

Roberto J. Bayardo Jr. Efficiently Mining Long Patterns from Databases. Proc. of the 1998 ACM-SIGMOD Int’l Conf. on Management of Data, 85- 93.

J. Pei, J. Han, and R. Mao. "CLOSET: An efficient algorithm for mining frequent closed itemsets". Proceedings of the 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Dallas, TX, May, 2000.

Yves Bastide, Rafik Taouil, Nicolas Pasquier, Gerd Stumme. Mining Frequent Patterns with Counting Inference. SIGKDD Explorations. ACM SIGKDD, December 2000. Volume 2, Issue 2 - page 66-75.

J. Han, J. Pei, Y. Yin and R. Mao, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach", Data Mining and Knowledge Discovery, an International Journal, Volume 8, Issue 1, pages 53-87, January 2004, Kluwer Academic Publishers.

Doug Burdick, Manuel Calimlim, Johannes Gehrke. MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases. ICDE, 2001.

Ilias Tsoukatos and Dimitrios Gunopulos. Efficient Mining of Spatiotemporal Patterns. C.S. Jensen et al. (Eds.):SSTD 2001, LNCS 2121, pp. 425−442, 2001. Springer-Verlag Berlin Heidelberg 2001.

Mohammed J. Zaki and Ching-Jui Hsiao, CHARM: An Efficient Algorithm for Closed Itemset Mining. 2nd {SIAM} International Conference on Data Mining. April 2002.

J. Wang, J. Han, and J. Pei, "CLOSET+: Searching for the Best Strategies for Mining Frequent Closed Itemsets", Proc. 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'03), Washington, D.C., August, 2003.

Guimei Liu Hongjun Lu Wenwu Lou and Jeffrey Xu Yu. On Computing, Storing and Querying Frequent Patterns. SIGKDD’03, August24-27, 2003, Washington, DC, USA. 2003 ACM 1­58113­737­0/03/0008 ...$5.00.

J. Wang, J. Han, Y. Lu, and P. Tzvetkov, "TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets", IEEE Transactions on Knowledge and Data Engineering, 17(5):652-664, 2005.

H. Cao, N. Mamoulis, and D. W. Cheung, "Mining Frequent Spatio-temporal Sequential Patterns," Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), pp. 82-89, Houston, Texas, November 2005.

Zhenhui Li Bolin Ding, Jiawei Han, Roland Kays and Peter Nye. Mining Periodic Behaviors for Moving Objects. KDD’10, July 25–28, 2010, Washington, DC, USA. 2010 ACM 978-1-4503-0055-1/10/07 ...$10.00.

Juyoung Kang and Hwan-Seung Yong. Mining Spatio-Temporal Patterns in Trajectory Data. Journal of Information Processing Systems, Vol.6, No.4, December 2010 DOI : 10.3745/JIPS.2010.6.4.521.

Mabroukeh, N. R. and Ezeife, C. I. 2010. A Taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43, 1, Article 3, November 2010, 41 pages.

Rakesh Agrawal Sakti Ghosh Tomasz Imielinski, Bala Iyer, Arun Swami. An Interval Classier for Database Mining Applications. Proceedings of the 18th VLDB Conference Vancouver, British Columbia, Canada 1992.

Zhenhui Li, Ming Ji, Jae-Gil Lee, Lu-An Tang, Yintao Yu, Jiawei Han and Roland Kays, MoveMine: Mining Moving Object Databases. SIGMOD’10, June 6–11, 2010, Indianapolis, Indiana, USA. 2010 ACM 978-1-4503-0032-2/10/06 ...$10.00.

Qiankun Zhao and Sourav S. Bhowmick. Sequential Pattern Mining: A Survey. Technical Report, CAIS, Nanyang Technological University, Singapore, No. 2003118 , 2003.

Ho Jin Woo and Won Suk Lee. estMax: Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams. IEEE Transactions on Knowledge and Data Engineering, VOL. 21, NO. 10, OCTOBER 2009.

Jinlin Chen. An UpDown Directed Acyclic Graph Approach for Sequential Pattern Mining. IEEE Transactions on Knowledge and Data Engineering, VOL. 22, NO. 7, JULY 2010.

Faraz Rasheed, Mohammed Alshalalfa, and Reda Alhajj. Efficient Periodicity Mining in Time Series Databases Using Suffix Trees. IEEE Transactions on Knowledge and Data Engineering, VOL. 23, NO. 1, JANUARY 2011.

Osman Abul, Francesco Bonchi, and Fosca Giannotti, Hiding Sequential and Spatiotemporal Patterns. IEEE Transactions on Knowledge and Data Engineering, VOL. 22, NO. 12, DECEMBER 2010.

Pradeep Mohan, Shashi Shekhar, James A. Shine, and James P. Rogers. Cascading Spatio-Temporal Pattern Discovery. IEEE Transactions on Knowledge and Data Engineering, VOL. 24, NO. 11, NOVEMBER 2012.

Xixian Han, Jianzhong Li, Donghua Yang, and Jinbao Wang. Efficient Skyline Computation on Big Data. IEEE Transactions on Knowledge and Data Engineering, VOL. 25, NO. 11, November 2013.

Manish Bhide and Krithi Ramamritham. Category-Based Infidelity Bounded Queries over Unstructured Data Streams. IEEE Transactions on Knowledge and Data Engineering, VoL. 25, No. 11, November 2013.

J.Han, M.Kamber and J.Pei. Data Mining: Concepts and Techniques. Morgan Kauffman Series. 3rd Edition, 2012.

Wei Fan, Albert Bifet. Mining Big Data: Current Status, and Forecast to the Future. SIGKDD Explorations Volume 14, Issue 2, Pno.s 1-5.

M.N.Garofalakis, Rajeev Rastogi and Kyuseok Shim. SPIRIT: Sequential Pattern Mining with Regular Expression Constraints. Proceedings of VLDB '99 Proceedings of the 25th International Conference on Very Large Databases, Page Nos. 223-234.

Mohammed J. Zaki, SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning Journal, 42(1/2):31-60, 2001.

Ming-Yen Lin and Suh-Yin Lee. Fast Discovery of Sequential Patterns through Memory Indexing and Database Partitioning. Springer Berlin Heidelberg, 2002.

Huiping Cao, Nikos Mamoulis, David Wai-lok Cheung. Discovery of Periodic Patterns in Spatiotemporal Sequences, IEEE TKDE, Vol. 19, No. 4, April 2007 pp. 453-467.

O.Obulesu, A. Rama Mohan Reddy and K. Suresh. Finding Maximal Periodic Patterns and Pruning Strategy in Spatiotemporal Databases. IJARCSSE, Vol.2, Issue No.4, April 2012. ISSN: 2277 128X Page No.s 423-426.


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