Evolving Mashup Interfaces Using a Distributed Machine Learning and Model Transformation Methodology

  • Antonio Jesus Fernandez-GarciaEmail author
  • Luis Iribarne
  • Antonio Corral
  • James Z. Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9416)


Nowadays users access information services at any time and in any place. Providing an intelligent user interface which adapts dynamically to the users’ requirements is essential in information systems. Conventionally, systems are constructed at the design time according to an initial structure and requirements. The effect of the passage of time and changes in users, applications and environment is that the systems cannot always satisfy the user’s requirements. In this paper a methodology is proposed to allow mashup user interfaces to be intelligent and evolve over time by using computational techniques like machine learning over huge amounts of heterogeneous data, known as big data, and model-driven engineering techniques as model transformations. The aim is to generate new ways of adapting the interface to the user’s needs, using information about user’s interaction and the environment.


Machine learning Mashup interfaces Smart interfaces Distributed systems Big data Model transformation 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Antonio Jesus Fernandez-Garcia
    • 1
    Email author
  • Luis Iribarne
    • 1
  • Antonio Corral
    • 1
  • James Z. Wang
    • 2
  1. 1.Applied Computing GroupUniversity of AlmeriaAlmeríaSpain
  2. 2.The Pennsylvania State UniversityState CollegeUSA

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