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Demand on Computational Intelligence Paradigms Synergy

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Next Generation Data Technologies for Collective Computational Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 352))

Abstract

Enterprises are migrating towards SOA-based models in order to meet the greater than ever needs for integration and consolidation. Besides, driven by the dissemination of more refined mobile devices in the enterprise, and the rapid growth of wireless networks based on IEEE 802.11 WiFi Standards, mobile applications have been increasingly used in mission-critical business applications. The SOA-based next generation mobility management model analyzed here provides a baseline framework for the successful architecting, deployment and maintenance of mobile applications. We introduce and analyze the requirements to the architecture design needed to comply with new mobility management concept development. We also examine the architecture planning and design issues for the successful implementation of mobility management solutions. Furthermore, we provide a scenario example of the framework for SOA (Service-oriented Architecture) mobile appliances implementation, namely, a model that demonstrates “the customer search” mobile application. Finally, we present a practical case, e.g., a “mobile messaging” application, to show how applying a SOA approach can make the writing of mobile clients using remote services simple and intuitive, which in turn can increase the number of services available on the market, as well as their functionalities and features.

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Authors
  1. N. Kryvinska
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  2. C. Strauss
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  3. L. Auer
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Editor information

Editors and Affiliations

  1. School of Computing & Maths, University of Derby, DE22 1GB, Derby, United Kingdom (UK)

    Nik Bessis

  2. Dept de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya, Barcelona, Spain

    Fatos Xhafa

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Kryvinska, N., Strauss, C., Auer, L. (2011). Demand on Computational Intelligence Paradigms Synergy. In: Bessis, N., Xhafa, F. (eds) Next Generation Data Technologies for Collective Computational Intelligence. Studies in Computational Intelligence, vol 352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20344-2_13

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  • DOI: https://doi.org/10.1007/978-3-642-20344-2_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20343-5

  • Online ISBN: 978-3-642-20344-2

  • eBook Packages: EngineeringEngineering (R0)

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