Abstract
Mining data streams is a critical task of actual Big Data applications. Usually, data stream mining algorithms work on resource-constrained environments, which call for novel requirements like availability of resources and adaptivity. Following this main trend, in this paper we propose a distributed data stream classification technique that has been tested on a real sensor network platform, namely, Sun SPOT. The proposed technique shows several points of research innovation, with are also confirmed by its effectiveness and efficiency assessed in our experimental campaign.
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
Similar content being viewed by others
References
Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. IEEE Trans. Syst. Man Cybern. Part B 38, 393422 (2002)
Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science (2007). http://www.ics.uci.edu/~mlearn/MLRepository.html
Bonifati, A., Cuzzocrea, A.: Efficient fragmentation of large XML documents. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 539–550. Springer, Heidelberg (2007)
Cameron J.J., Cuzzocrea A., Jiang F., Leung C.K.-S.: Mining frequent itemsets from sparse data streams in limited memory environments. In: Proceedings of the 14th International Conference on Web-Age Information Management, pp. 51–578 (2013)
Cuzzocrea, A.: Analytics over big data: exploring the convergence of data warehousing, OLAP and data-intensive cloud infrastructures. In: Proceedings of COMPSAC 2013, pp. 481–483 (2013)
Cuzzocrea, A., Chakravarthy, S.: Event-based lossy compression for effective and efficient OLAP over data streams. Data Knowl. Eng. 69(7), 678–708 (2010)
Cuzzocrea, A., Darmont, J., Mahboubi, H.: Fragmenting very large XML data warehouses via K-means clustering algorithm. Int. J. Bus. Intell. Data Min. 4(3/4), 301–328 (2009)
Cuzzocrea, A., Furfaro, F., Mazzeo, G.M., Saccá, D.: A grid framework for approximate aggregate query answering on summarized sensor network readings. In: Meersman, R., Tari, Z., Corsaro, A. (eds.) OTM-WS 2004. LNCS, vol. 3292, pp. 144–153. Springer, Heidelberg (2004)
Cuzzocrea, A., Furfaro, F., Masciari, E., Sacca’, D., Sirangelo, C.: Approximate query answering on sensor network data streams. In: Stefanidis, A., Nittel, S. (eds.) GeoSensor Networks, pp. 53–72. CRC Press, Boca Raton (2004)
Cuzzocrea, A., Gaber, M.M., Shiddiqi, A.M.: Adaptive data stream mining for wireless sensor networks. In: Proceedings of IDEAS 2014, pp. 284–287 (2014)
Cuzzocrea, A., Russo, V., Saccà, D.: A robust sampling-based framework for privacy preserving OLAP. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 97–114. Springer, Heidelberg (2008)
Cuzzocrea, A., Sacc, D.: Balancing accuracy and privacy of OLAP aggregations on data cubes. In: Proceedings of DOLAP 2010, pp. 93–98 (2010)
Cuzzocrea, A., Sacc, D., Ullman, J.D.: Big data: a research agenda. In: Proceedings of IDEAS 2013, pp. 198–203 (2013)
Gaber, M.M.: Data stream mining using granularity-based approach. In: Abraham, A., Hassanien, A.E., de Leon, F., de Carvalho, A.P., Snášel, V. (eds.) Foundations of Computational, IntelligenceVolume 6. Studies in Computational Intelligence, vol. 206, pp. 47–66. Springer, Berlin (2009)
Gaber, M.M.: Advances in data stream mining. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 2(1), 79–85 (2012)
Iordache, O.: Methods. In: Iordache, O. (ed.) Polystochastic Models for Complexity. UCS, vol. 4, pp. 17–61. Springer, Heidelberg (2010)
Gaber, M.M., Yu, P.S.: A holistic approach for resource-aware adaptive data stream mining. J. New Gener. Comput. 25(1), 95–115 (2006)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: A survey of classification methods in data streams. In: Aggarwal, C.C. (ed.) Data Streams Models and Algorithms. Advances in Database Systems, pp. 39–59. Springer, Heidelberg (2007)
Gama, J., Gaber, M.M.: Learning from Data Streams: Processing Techniques in Sensor Networks. Springer, Berlin (2007). ISBN 1420082329, 9781420082326
Ganguly, A., Gama, J., Omitaomu, O., Gaber, M.M., Vatsavai, R.R.: Knowledge Discovery from Sensor Data. CRC Press, Boca Raton (2008). ISBN 1420082329, 9781420082326
Krishnaswamy S., Gama J., Gaber M.M.: Advances in data stream mining for mobile and ubiquitous environments. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2607–2608 (2011)
Leung, C.K.-S., Cuzzocrea, A., Jiang, F.: Discovering frequent patterns from uncertain data streams with time-fading and landmark models. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds.) TLDKS VIII. LNCS, vol. 7790, pp. 174–196. Springer, Heidelberg (2013)
Phung N.D., Gaber M.M., Rohm, U.: Resource-aware online data mining in wireless sensor networks. In: Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, pp. 139–146 (2007)
Rodrigues, P.P., Gama, J., Lopes, L.: Clustering distributed sensor data streams. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 282–297. Springer, Heidelberg (2008)
Shah R., Krishnaswamy S., Gaber M.M.: Resource-aware very fast k-means for ubiquitous data stream mining. In: Proceedings of Second International Workshop on Knowledge Discovery in Data Streams, held in conjunction with the ECML/PKDD 2005, Porto, Portugal (2005)
Sheng, B., Li, Q., Mao, W., Jin, W.: Outlier detection in sensor networks. In: Proceedings of the 8th ACM International Symposium on Mobile and Ad Hoc Networking and Computing, pp. 219–228 (2007)
Stahl, F., Gaber, M.M., Bramer, M.: Scaling up data mining techniques to large datasets using parallel and distributed processing. In: Rausch, P., Sheta, A.F., Ayesh, A. (eds.) Business Intelligence and Performance Management. Advanced Information and Knowledge Processing, pp. 243–259. Springer, London (2013)
Subramaniam S., Palpanas T., Papadopoulos D., Kalogeraki V., Gunopulos D.: Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd International Conference on Very Large Databases, pp. 187–198 (2006)
Yin, J., Gaber, M.M.: Clustering distributed time series in sensor networks. In: Proceedings of the Eighth IEEE International Conference on Data Mining, pp. 678–687, Pisa, Italy, 15–19 December 2008
Yu, B., Cuzzocrea, A., Jeong, D.H., Maydebura, S.: On managing very large sensor-network data using bigtable. In: Proceedings of CCGRID 2012, pp. 918–922 (2012)
Zhuang, Y., Chen, L.: In-network outlier cleaning for data collection in sensor networks. In: Proceedings of the 1st International VLDB Workshop on Clean Databases, pp. 678–687 (2006)
Zhuang, Y., Chen, L., Wang, X., Lian, J.: A weighted average-based approach for cleaning sensor data. In: Proceedings of the 27th International Conference on Distributed Computing Systems, pp. 678–687 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Cuzzocrea, A., Gaber, M.M., Shiddiqi, A.M. (2015). Distributed Classification of Data Streams: An Adaptive Technique. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-22729-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22728-3
Online ISBN: 978-3-319-22729-0
eBook Packages: Computer ScienceComputer Science (R0)