Advertisement

Efficient Data Aggregation and Management in Integrated Network Control Environments

  • Patrick-Benjamin Bök
  • Michael Patalas
  • Dennis Pielken
  • York Tüchelmann
Conference paper
  • 377 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 63)

Abstract

Due to the emerging growth of computer networks, broadly based measurements, monitoring and management become necessary, for example, to solve occurring problems. Lots of different concepts exist for each of the mentioned functionality. Therefore, distributed network control architectures integrating all of these functionalities are in the focus of current research. To take advantage of this architectures, advanced data aggregation and management schemes are required because an efficient access to the distributed data is critical in this case. In this paper, we present a data aggregation and management scheme that improves the performance of data handling in Integrated Network Control environments. The Integrated Network Control concept is enhanced by an multidimensional on-line analytical processing (OLAP) scheme. A performance analysis shows that the proposed scheme noticeably improves the overall performance of the Integrated Network Control environment.

Keywords

Business Intelligence Data Aggregation Hierarchical Communication Network Control Network Measurement OLAP 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bök, P.-B., Pielken, D., Tüchelmann, Y.: Towards an integrated network control architecture. In: Parallel and Distributed Computing and Networks Conference (2010)Google Scholar
  2. 2.
    Thomas, M.S., Nanda, D., Ali, I.: Development of a data warehouse for non-operational data in power utilities. In: Power India Conference, p. 7. IEEE, Los Alamitos (2006)Google Scholar
  3. 3.
    Xiangdong, Z., Xiao, J.: Process control analysis system based on data warehouse. In: International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, November 2009, vol. 1, pp. 283–287 (2009)Google Scholar
  4. 4.
    Hamm, C.K., Kennedy, M.R., Wu, T.T., Phillips, J.S.: An operational data store for reporting clinical practice guideline adherence in chronic disease patients. In: Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, CBMS 2004, June 2004, pp. 78–83 (2004)Google Scholar
  5. 5.
    Albrecht, J., Bauer, A., Deyerling, O., Gunzel, H., Hummer, Lehner, W., Schlesinger, L.: Management of multidimensional aggregates for efficient online analytical processing. In: International Symposium Proceedings Database Engineering and Applications, IDEAS 1999, August 1999, pp. 156–164 (1999)Google Scholar
  6. 6.
    Chen, Q., Dayal, U., Hsu, M.: A distributed olap infrastructure for e-commerce. In: Proceedings of the 1999 IFCIS International Conference on Cooperative Information Systems, CoopIS 1999, pp. 209–220 (1999)Google Scholar
  7. 7.
    Jianzhong, L., Hong, G.: Range sum query processing in parallel data warehouses. In: Proceedings of the Fourth International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2003, August 2003, pp. 877–881 (2003)Google Scholar
  8. 8.
    Akinde, M., Bohlen, M., Johnson, T., Lakshmanan, L.V., Srivastava, D.: Efficient olap query processing in distributed data warehouses. In: Proceedings of the 18th International Conference on Data Engineering 2002, p. 262 (2002)Google Scholar
  9. 9.
    He, H., Zhang, Y., Li, L., Ren, J., Hu, C.: An improved storage algorithm for multidimensional data cube. In: Fourth International Conference on Innovative Computing, Information and Control (ICICIC), December 2009, pp. 841–844 (2009)Google Scholar
  10. 10.
    Shimada, T., Tsuji, T., Higuchi, K.: A storage scheme for multidimensional data alleviating dimension dependency. In: Third International Conference on Digital Information Management, ICDIM 2008, November 2008, pp. 662–668 (2008)Google Scholar
  11. 11.
    Yeung, G.C.H., Gruver, W.A., Kotak, D.B.: A multi-agent approach to immediate view maintenance for an operational data store. In: IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, July 2001, vol. 4, pp. 1869–1874 (2001)Google Scholar
  12. 12.
    Xu, W., Li, M., Wu, S., Zhu, S., Wang, Z., Miao, K., Wang, Y.: Incremental data feed maintenance of a data warehouse system derived from multiple autonomous data sources. In: International Conference on Control and Automation, ICCA 2005, June 2005, vol. 2, pp. 1108–1113 (2005)Google Scholar
  13. 13.
    Hose, K., Klan, D., Sattler, K.-U.: Online tuning of aggregation tables for olap. In: IEEE 25th International Conference on Data Engineering, ICDE 2009, March 2009, pp. 1679–1686 (2009)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2011

Authors and Affiliations

  • Patrick-Benjamin Bök
    • 1
  • Michael Patalas
    • 1
  • Dennis Pielken
    • 1
  • York Tüchelmann
    • 1
  1. 1.Ruhr-University BochumBochumGermany

Personalised recommendations