Abstract
Recent advances in ubiquitous devices open an opportunity to apply new data stream mining techniques to support intelligent decision making in the next generation of ubiquitous applications. This paper motivates and describes a novel Context-aware Collaborative data stream mining system CC-Stream that allows intelligent mining and classification of time-changing data streams on-board ubiquitous devices. CC-Stream explores the knowledge available in other ubiquitous devices to improve local classification accuracy. Such knowledge is associated with context information that captures the system state for a particular underlying concept. CC-Stream uses an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the instance space and their context similarity in relation to the current context.
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
Brezillon, R., Pomerol, J.C.: Contextual knowledge sharing and cooperation in intelligent assistant systems. Travail Humain 62, 223–246 (1999)
Cortez, P., Lopes, C., Sousa, P., Rocha, M., Rio, M.: Symbiotic Data Mining for Personalized Spam Filtering. In: IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies, WI-IAT 2009, vol. 1, pp. 149–156. IEEE, Los Alamitos (2009)
Datta, S., Bhaduri, K., Giannella, C., Wolff, R., Kargupta, H.: Distributed data mining in peer-to-peer networks. IEEE Internet Computing, 18–26 (2006)
Daumé III, H., Marcu, D.: Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research 26(1), 101–126 (2006)
Dey, A.K., Abowd, G.D., Salber, D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-Computer Interaction 16(2), 97–166 (2001)
Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80. ACM, New York (2000)
Gaber, M.M., Krishnaswamy, S., Zaslavsky, A.: Ubiquitous data stream mining. In: Current Research and Future Directions Workshop Proceedings held in conjunction with The Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining, Sydney, Australia. Citeseer (2004)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004)
Harries, M.B., Sammut, C., Horn, K.: Extracting hidden context. Machine Learning 32(2), 101–126 (1998)
Hotho, A., Pedersen, R., Wurst, M.: Ubiquitous Data. In: May, M., Saitta, L. (eds.) Ubiquitous Knowledge Discovery. LNCS, vol. 6202, pp. 61–74. Springer, Heidelberg (2010)
Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 97–106. ACM, New York (2001)
Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Bushra, S., Dull, J., Sarkar, K., Klein, M., Vasa, M., et al.: VEDAS: A Mobile and Distributed Data Stream Mining System for Real-Time Vehicle Monitoring. In: Proceedings of SIAM International Conference on Data Mining, vol. 334 (2004)
Katakis, I., Tsoumakas, G., Vlahavas, I.: On the utility of incremental feature selection for the classification of textual data streams. In: Bozanis, P., Houstis, E.N. (eds.) PCI 2005. LNCS, vol. 3746, pp. 338–348. Springer, Heidelberg (2005)
Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: An ensemble method for drifting concepts. The Journal of Machine Learning Research 8, 2755–2790 (2007)
Padovitz, A., Loke, S.W., Zaslavsky, A.: Towards a theory of context spaces. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004, pp. 38–42 (2004)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 1345–1359 (2009)
Schmidt, A., Beigl, M., Gellersen, H.W.: There is more to context than location. Computers & Graphics 23(6), 893–901 (1999)
Stahl, F., Gaber, M.M., Bramer, M., Yu, P.S.: Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), vol. 2, pp. 323–330. IEEE, Los Alamitos (2010)
Street, W.N., Kim, Y.S.: A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377–382. ACM, New York (2001)
Tsymbal, A.: The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin (2004)
Tsymbal, A., Pechenizkiy, M., Cunningham, P., Puuronen, S.: Dynamic integration of classifiers for handling concept drift. Inf. Fusion 9, 56–68 (2008)
Turney, P.D.: Exploiting context when learning to classify. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 402–407. Springer, Heidelberg (1993)
Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD international Conference on Knowledge Discovery and Data Mining, pp. 226–235. ACM, New York (2003)
Widmer, G.: Tracking context changes through meta-learning. Machine Learning 27(3), 259–286 (1997)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine learning 23(1), 69–101 (1996)
Wurst, M., Morik, K.: Distributed feature extraction in a p2p setting–a case study. Future Generation Computer Systems 23(1), 69–75 (2007)
Zhu, X., Wu, X., Yang, Y.: Effective classification of noisy data streams with attribute-oriented dynamic classifier selection. Knowl. Inf. Syst. 9, 339–363 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bártolo Gomes, J., Gaber, M.M., Sousa, P.A.C., Menasalvas, E. (2011). Context-Aware Collaborative Data Stream Mining in Ubiquitous Devices. In: Gama, J., Bradley, E., Hollmén, J. (eds) Advances in Intelligent Data Analysis X. IDA 2011. Lecture Notes in Computer Science, vol 7014. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24800-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-24800-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24799-6
Online ISBN: 978-3-642-24800-9
eBook Packages: Computer ScienceComputer Science (R0)