Biztool: A Flexible Cloud-Centric Framework for Business Analytics

  • Amarnath Ayyadurai
  • Chitra Babu
Part of the Communications in Computer and Information Science book series (CCIS, volume 141)


Business analytics provide large enterprises an edge over their competitors, depending upon the size of the data analyzed and the time needed to generate business models. This requires an infrastructure model that meets these huge demands on large scale data processing. Cloud computing provides low cost storage space of virtually any size on demand which can host the data perpetually. Similarly, the processing power can also be commissioned as and when needed. Enterprises are constantly in search of simple and inexpensive systems which transform the available raw data to useful information. In this context, we propose a new framework named Biztool, where a collection of data analytic operators based on Gridbatch is provided as web services that process the data remotely. Biztool is flexible and customizable through user-defined functions. Since various clients can reuse the existing operators for their needs, the development and maintenance cost are reduced.


Business Analytics Data Analysis Cloud Computing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: Proceedings of Sixth Symposium on Operating System Design and Implementation, pp. 137–150 (2004)Google Scholar
  2. 2.
    Borthakur, D.: The Hadoop Distributed File System: Architecture and Design (2007), Retrieved from
  3. 3.
    Liu, H., Orban, D.: GridBatch: Cloud Computing for Large-Scale Data-Intensive Batch Applications. In: Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid, May 19-22, pp. 295–305 (2008)Google Scholar
  4. 4.
    Graves, S., Ramachandran, R., Lynnes, C., Maskey, M., Keiser, K., Pham, L.: Mining Scientific Data using the Internet as the Computer. In: Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), July 7-11, pp. 283–286 (2008)Google Scholar
  5. 5.
    Li, T., Bollinger, T., Breuer, N., Wehle, H.-D.: Grid-based Data Mining in Real-life Business Scenario. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence (WI 2004), September 20-24, pp. 611–614 (2004)Google Scholar
  6. 6.
    Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., Zagorodnov, D.: The Eucalyptus Open-source Cloud-computing System. In: Proceedings of 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, May 18-21, pp. 124–131 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Amarnath Ayyadurai
    • 1
  • Chitra Babu
    • 1
  1. 1.SSN College of EngineeringKalavakkamIndia

Personalised recommendations