A Model-Based Framework for Developing and Deploying Data Aggregation Services

  • Ramakrishna Soma
  • Amol Bakshi
  • V. K. Prasanna
  • Will Da Sie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4294)


Data aggregation services compose, transform, and analyze data from a variety of sources such as simulators, real-time sensor feeds, etc. This paper proposes a methodology for accelerating the development and deployment of data aggregation modules in a service-oriented architecture. Our framework allows existing semantic web-service techniques to be embedded into a programming language thereby leveraging ease of use and flexibility enabled by the former with the expressiveness and tool support of the latter. In our framework data aggregations are written as regular Java programs where the data inputs to the aggregations are specified as predicates over a rich ontology. Our middleware matches these data specifications to the appropriate web-service, automatically invokes it, and performs the required data serialization-deserialization. Finally the data aggregation program is deployed as yet another web-service. Thus, our programming framework hides the complexity of web-service development from the end-user. We discuss the design and implementation of the framework based on open standards, and using state-of-art tools.


Service Composition Data Aggregation Service Discovery Service Oriented Architecture Domain Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Curbera, F., Goland, Y., Klein, J., Leymann, F., Roller, D., Weerawarana, S.: Business Process Execution Language for Web Services, Version 1.1. Specification, BEA Systems, IBM, Microsoft, SAP, Siebel, May 05 (2003)Google Scholar
  2. 2.
    Blow, M., Goland, Y., Kloppmann, M., Leymann, F., Pfau, G., Roller, D., Rowley, M.: BPELJ: BPEL for Java. Whitepaper, BEA and IBM (2004)Google Scholar
  3. 3.
  4. 4.
    Martin, D., et al.: OWL-S: Semantic markup for web services,
  5. 5.
    Sivashanmugam, K., Miller, J.A., Sheth, A.P., Verma, K.: Framework for Semantic Web Process Composition. International Journal of Electronic Commerce 9(2), 71 (Winter 2004)Google Scholar
  6. 6.
  7. 7.
    Mandell, D.J., McIlraith, S.A.: Adapting BPEL4WS for the Semantic Web: The Bottom-Up Approach to Web Service Interoperation. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 227–241. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Rao, J., Su, X.: A Survey of Automated Web Service Composition Methods. In: Cardoso, J., Sheth, A.P. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 43–54. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Thakkar, S., Ambite, J.L., Knoblock, C.A.: Composing, optimizing, and executing plans for bioinformatics web services. VLDB Journal, Special Issue on Data Management, Analysis and Mining for Life Sciences 14(3), 330–353 (2005)Google Scholar
  10. 10.
    POSC, The Petrotechnical Open Standards Consortium,
  11. 11.
    Sycara, K., Paolucci, M., Ankolekar, A., Srinivasan, N.: Automated Discovery, Interaction and Composition of Semantic Web Services. Journal of Web Semantics (2003)Google Scholar
  12. 12.
    Paolucci, M., Kawamura, T., Payne, T.R., Sycara, K.: Importing the semantic web in UDDI. In: Bussler, C.J., McIlraith, S.A., Orlowska, M.E., Pernici, B., Yang, J. (eds.) CAiSE 2002 and WES 2002. LNCS, vol. 2512, pp. 225–236. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Deelman, E., Singh, G., Atkinson, M.P., Chervenak, A., Hong, N.P.C., Kesselman, C., Patil, S., Pearlman, L., Su, M.-i.: Grid-Based Metadata Services. In: 16th International Conference on Scientific and Statistical Database Management (SSDBM 2004) (June 2004)Google Scholar
  14. 14.
    Zhao, J., Wroe, C., Goble, C., Stevens, R., Quan, D., Greenwood, M.: Using Semantic Web Technologies for Representing e-Science Provenance. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 92–106. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Zhang, C., Prasanna, V., Orangi, A., Da Sie, W., Kwatra, A.: Modeling methodology for application development in petroleum industry. In: IEEE International Conference on Information Reuse and Integration, Las Vegas (2005)Google Scholar
  16. 16.
  17. 17.
    Bowers, S., Ludascher, B.: Actor-oriented design of scientific workflows. In: Delcambre, L.M.L., Kop, C., Mayr, H.C., Mylopoulos, J., Pastor, Ó. (eds.) ER 2005. LNCS, vol. 3716, pp. 369–384. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Foster, I., Voeckler, J., Wilde, M., Zhao, Y.: Chimera: A Virtual Data System for Representing, Querying and Automating Data Derivation. In: 14th Conference on Scientific and Statistical Database Management, Edinburgh, Scotland (July 2002)Google Scholar
  19. 19.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ramakrishna Soma
    • 1
  • Amol Bakshi
    • 2
  • V. K. Prasanna
    • 2
  • Will Da Sie
    • 3
  1. 1.Dept of Computer Science, USCLos Angeles
  2. 2.Dept of Electrical Engineering, USCLos Angeles
  3. 3.Chevron CorporationSan Ramon

Personalised recommendations