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Using a Time Granularity Table for Gradual Granular Data Aggregation

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Języki publikacji
EN
Abstrakty
EN
The majority of today’s systems increasingly require sophisticated data management as they need to store and to query large amounts of data for analysis and reporting purposes. In order to keep more “detailed” data available for longer periods, “old” data has to be reduced gradually to save space and improve query performance, especially on resource-constrained systems with limited storage and query processing capabilities. A number of data reduction solutions have been developed, however an effective solution particularly based on gradual data reduction is missing. This paper presents an effective solution for data reduction based on gradual granular data aggregation. With the gradual granular data aggregationmechanism, older data can be made coarse-grainedwhile keeping the newest data fine-grained. For instance, when data is 3 months old aggregate to 1 minute level from 1 second level, when data is 6 months old aggregate to 2 minutes level from 1 minute level and so on. The proposed solution introduces a time granularity based data structure, namely a relational time granularity table that enables long term storage of old data by maintaining it at different levels of granularity and effective query processing due to a reduction in data volume. In addition, the paper describes the implementation strategy derived from a farming case study using standard database technologies.
Wydawca
Rocznik
Strony
153--176
Opis fizyczny
Bibliogr. 23 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Computer Science, Aalborg University, Selma Lagerlöfs Vej 300, 9220 Aalborg Ø, Denmark
  • Department of Computer Science, Aalborg University, Selma Lagerlöfs Vej 300, 9220 Aalborg Ø, Denmark
Bibliografia
  • [1] Boly, A., Hébrail, G., Goutier, S.: Forgetting data intelligently in data warehouses, Proc. 5th IEEE International Conference on Research, Innovation and Vision for the Future, Hanoi, Vietnam, 2007, 220–227.
  • [2] Cuzzocrea, A., Furfaro, F., Mazzeo, G. M., Saccà, D.: A grid framework for approximate aggregate query answering on summarized sensor network readings, Proc. OTM 2004 Workshops (R. Meersman, Z. Tari, A. Corsaro, Eds.), LNCS 3292, Springer-Verlag, Berlin, 2004, 144–153.
  • [3] Cuzzocrea, A.: Cams: OLAPing multidimensional data streams efficiently, Proc. 11th International Conference on Data Warehousing and Knowledge Discovery (T. B. Pedersen, M. K. Mohania, A. M. Tjoa, Eds.), LNCS 5691, Springer-Verlag, Berlin, 2009, 48–62.
  • [4] Cuzzocrea, A.: Retrieving accurate estimates to OLAP queries over uncertain and imprecise multidimentional data streams, Proc. 23rd Scientific and Statistical Database Management Conference (J. B. Cushing, J. French, S. Bowers, Eds.), LNCS 6809, Springer-Verlag, Berlin, 2011, 575–576.
  • [5] Han, J., Chen, Y., Dong, G., Pei, J., Wah, B. W., Wang, J., Cai. Y. D.: Stream cube: an architecture for multi-dimensional analysis of data streams, Distributed and Parallel Databases, 18(2), 2005, 173–197.
  • [6] Iftikhar, N., Pedersen, T. B.: A rule-based tool for gradual granular data aggregation, Proc. 14th ACM International Workshop on Data Warehousing and OLAP, Glasgow, Scotland, UK, 2011, 1–8.
  • [7] Iftikhar, N., Pedersen, T. B.: Gradual data aggregation in multi-granular fact tables on resource-constrained systems, Proc. 14th International Conference on Knowledge-based Intelligent Information & Engineering Systems (R. Setchi, I. Jordanov, R. J. Howlett, L. C. Jain, Eds.), LNCS 6278, Springer-Verlag, Berlin, 2010, 349–358.
  • [8] Iftikhar, N.: Integration, aggregation and exchange of farming device data: a high level perspective, Proc. 2nd IEEE Conference on the Applications of Digital Information and Web Technologies, London, United Kingdom, 2009, 14–19.
  • [9] Iftikhar, N., Pedersen, T. B.: LandIT database : a case study. Proc. 16th European Conference on Information Systems in Agriculture and Forestry, Prague, Czech Republic, 2010, 51–59.
  • [10] Iftikhar, N., Pedersen, T. B.: Schema design alternatives for multi-granular data warehousing, Proc. 21st International Conference on Database and Expert Systems Applications (P. G. Bringas, A. Hameurlain, A. Quirchmayr, Eds.), LNCS 6262, Springer-Verlag, Berlin, 2010, 111–125.
  • [11] Iftikhar, N., Pedersen, T. B.: Using a time granularity table for gradual granular data aggregation, Proc. 14th East-European Conference on Advances in Databases and Information Systems, (B. Catania, M. Ivanović, B. Thalheim, Eds.), LNCS 6295, Springer-Verlag, Berlin, 2010, 219–233.
  • [12] LandI T, www.tekkva.dk/page326.aspx as of 08-02-2012.
  • [13] Li, J., Srivastava, J.: Efficient aggregation algorithms for compressed data warehouses, IEEE Transactions on Knowledge and Data Engineering, 14(3), 2002, 515–529.
  • [14] Lopez, I. F. V., Moon, B., Snodgrass, R. T.: Spatiotemporal aggregate computation: a survey, IEEE Transactions on Knowledge and Data Engineering, 17(2), 2005, 271–286.
  • [15] Molina, H. G., Ullman, J. D., Widom, J.: Database Systems: The Complete Book, Prentice Hall, 2002.
  • [16] MYSQL 5.0 Manual, http://dev.mysql.com/doc/refman/5.0/en/innodb-physical-record.html as of 21-02-2012.
  • [17] Osborne, K., Johnson, R., Pöder, T.: Expert Oracle Exadata, Apress, 2011.
  • [18] Pitarch, Y., Laurent, A., Plantevit, M., Poncelet, P.: Multidimensional data stream summarization using extended tilted-timewindows, Proc. IEEE International Conference on Advanced Information Networking and Applications Workshops, Bradford, United Kingdom, 2009, 250–254.
  • [19] Pitarch, Y., Laurent, A., Poncelet, P.: Summarizing multidimensional data streams: a hierarchy-graphbased approach, Proc. 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (M. J. Zaki, J. X. Yu, B. Ravindran, V. Pudi, Eds.), LNCS 6119, Springer-Verlag, Berlin, 2010, 335–342.
  • [20] Rasheed, F., Lee, Y. K., Lee, S.: Towards using data aggregation techniques in ubiquitous computing environments, Proc. 4th IEEE International Conference on Pervasive Computing and Communication Workshops, Pisa, Italy, 2006, 369–392.
  • [21] 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), 2007, 39–55.
  • [22] Skyt, J., Jensen, C. S., Pedersen, T. B.: Specification-based data reduction in dimensional data warehouses, Information Systems, 33(1), 2008, 36–63.
  • [23] Zhang, D., Gunopulos, D., Tsotras, V. J., Seeger, B.: Temporal and spatio-temporal aggregations over data streams using multiple time granularities, Information Systems, 28(1–2), 2003, 61–84.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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