Abstract
This paper complements our line of research on effectively and efficiently processing massive high − rate data streams via intelligent compression techniques. In particular, here we provide approximation algorithms adhering to the so-called non − linear data stream compression paradigm. This paradigm demonstrates its feasibility and reliability in the context of emerging data stream applications, such as environmental sensor networks.
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References
Abadi, D.J., Lindner, W., Madden, S., Schuler, J.: An integration framework for sensor networks and data stream management systems. In: VLDB, pp. 1361–1364 (2004)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: ACM PODS (2002)
Cai, Y.D., Clutterx, D., Papex, G., Han, J., Welgex, M., Auvilx, L.: MAIDS: Mining Alarming Incidents from Data Streams. In: ACM SIGMOD (2004)
Cormode, G., Garofalakis, M.N.: Sketching probabilistic data streams. In: SIGMOD Conference, pp. 281–292 (2007)
Cuzzocrea, A.: Synopsis Data Structures for Representing, Querying, and Mining Data Streams. In: Ferragine, V.E., Doorn, J.H., Rivero, L.C. (eds.) Encyclopedia of Database Technologies and Applications (2008)
Cuzzocrea, A.: CAMS: OLAPing Multidimensional Data Streams Efficiently. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 48–62. Springer, Heidelberg (2009)
Cuzzocrea, A.: Intelligent Techniques for Warehousing and Mining Sensor Network Data. IGI Global (2009)
Cuzzocrea, A.: A top-down approach for compressing data cubes under the simultaneous evaluation of multiple hierarchical range queries. J. Intell. Inf. Syst. 34(3), 305–343 (2010)
Cuzzocrea, A., Chakravarthy, S.: Event-Based Compression and Mining of Data Streams. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 670–681. Springer, Heidelberg (2008)
Cuzzocrea, A., Chakravarthy, S.: Event-based lossy compression for effective and efficient olap over data streams. Data Knowl. Eng. 69(7) (2010)
Cuzzocrea, A., Decker, H.: Non-linear Data Stream Compression: Foundations and Theoretical Results. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part I. LNCS, vol. 7208, pp. 622–634. Springer, Heidelberg (2012)
Cuzzocrea, A., Furfaro, F., Masciari, E., Saccà, D., Sirangelo, C.: Approximate Query Answering on Sensor Network Data Streams. In: Stefanidis, A., Nittel, S. (eds.) GeoSensor Networks (2004)
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., Gabriele, S., Saccà, D.: High-performance data management and efficient aggregate query answering on environmental sensor networks by computational grids. In: DEXA Workshops, pp. 359–364 (2008)
Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: ACM SIGKDD (2000)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining Data Streams: A Review. ACM SIGMOD Record 34(2) (2005)
Garofalakis, M.N.: Distributed data streams. In: Encyclopedia of Database Systems, pp. 883–890 (2009)
Garofalakis, M.N., Gehrke, J., Rastogi, R.: Querying and mining data streams: you only get one look a tutorial. In: SIGMOD Conference, p. 635 (2002)
Gilbert, A., Guha, S., Indyk, P., Kotidis, Y., Muthukrishnan, S., Strauss, M.: Fast, Small-Space Algorithms for Approximate Histogram Maintenance. In: ACM STOC (2002)
Gilbert, A., Kotidis, Y., Muthukrishnan, S., Strauss, M.: One-Pass Wavelet Decompositions of Data Streams. IEEE Trans. on Knowledge and Data Engineering 15(3) (2003)
Guha, S., Koudas, N., Shim, K.: Data Streams and Histograms. In: ACM STOC (2001)
Ho, C.-T., Agrawal, R., Megiddo, N., Srikant, R.: Range Queries in OLAP Data Cubes. In: ACM SIGMOD (1997)
Knuth, D.E.: The Art of Computer Programming. Sorting and Searching, vol. 3. Addison-Wesley (1998)
Koudas, N., Srivastava, D.: Data stream query processing. In: ICDE, p. 1145 (2005)
Muthukrishnan, S.: Data Streams: Algorithms and Applications. In: ACM-SIAM SODA (2003)
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Cuzzocrea, A. (2013). Approximation Algorithms for Massive High-Rate Data Streams. In: Pechenizkiy, M., Wojciechowski, M. (eds) New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32518-2_6
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DOI: https://doi.org/10.1007/978-3-642-32518-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32517-5
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