Abstract
Majority of databases contain large amounts of data, gathered over long intervals of time. In most cases, the data is aggregated so that it can be used for analysis and reporting purposes. The other reason of data aggregation is to reduce data volume in order to avoid over-sized databases that may cause data management and data storage issues. However, non-flexible and ineffective means of data aggregation not only reduce performance of database queries but also lead to erroneous reporting. This paper presents flexible and effective ratio-based methods for gradual data aggregation in databases. Gradual data aggregation is a process that reduces data volume by converting the detailed data into multiple levels of summarized data as the data gets older. This paper also describes implementation strategies of the proposed methods based on standard database technology.
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References
Lopez, I.F.V., Moon, B., Snodgrass, R.T.: Spatiotemporal Aggregate Computation: A Survey. IEEE Transactions on Knowledge and Data Engineering 17(2), 271–286 (2005)
Li, J., Srivastava, J.: Efficient Aggregation Algorithms for Compressed Data Warehouses. IEEE Transactions on Knowledge and Data Engineering 14(3), 515–529 (2002)
Rasheed, F., Lee, Y.K., Lee, S.: Towards using Data Aggregation Techniques in Ubiquitous Computing Environments. In: 4th IEEE International Conference on Pervasive Computing and Communication Workshops, pp. 369–392. IEEE Press, New York (2006)
Schulze, C., Spilke, J., Lehner, W.: Data Modeling for Precision Dairy Farming within the Competitive Field of Operational and Analytical Tasks. Computers and Electronics in Agriculture 59(1-2), 39–55 (2007)
Zhang, D., Gunopulos, D., Tsotras, V.J., Seeger, B.: Temporal and Spatio-temporal Aggregations over Data Streams using Multiple Time Granularities. Information System 28(1-2), 61–84 (2003)
Boly, A., Hebrail, G., Goutier, S.: Forgetting Data Intelligently in Data Warehouses. In: IEEE International Conference on Research, Innovation and Vision for the Future, pp. 220–227. IEEE Press, New York (2007)
Skyt, J., Jensen, C.S., Pedersen, T.B.: Specification-based Data Reduction in Dimensional Data Warehouses. Information System 33(1), 36–63 (2008)
Iftikhar, N., Pedersen, T.B.: Using a Time Granularity Table for Gradual Granular Data Aggregation. In: Catania, B., Ivanović, M., Thalheim, B. (eds.) ADBIS 2010. LNCS, vol. 6295, pp. 219–233. Springer, Heidelberg (2010)
Iftikhar, N., Pedersen, T.B.: Gradual Data Aggregation in Multi-granular Fact Tables on Resource-Constrained Systems. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds.) KES 2010. LNCS, vol. 6278, pp. 349–358. Springer, Heidelberg (2010)
Fan, J., Kambhampati, S.: Multi-Objective Query Processing for Data Aggregation. Arizona State University, Computer Science & Engineering, Technical Report (2006)
Montagnuolo, M., Messina, A., Borgotallo, R.: Automatic Segmentation, Aggregation and Indexing of Multimodal News Information from Television and the Internet. Journal of Digital Information Management 8(6), 387–395 (2010)
Cuzzocrea, A.: Multiple-Objective Compression of Data Cubes in Cooperative OLAP Environments. In: Atzeni, P., Caplinskas, A., Jaakkola, H. (eds.) ADBIS 2008. LNCS, vol. 5207, pp. 62–80. Springer, Heidelberg (2008)
CAN-BUS, http://www.canbus.us
Coordinated Universal Time, http://en.wikipedia.org/wiki/Coordinated_Universal_Time
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Iftikhar, N. (2012). Ratio-Based Gradual Aggregation of Data. In: Benlamri, R. (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30507-8_28
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DOI: https://doi.org/10.1007/978-3-642-30507-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30506-1
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