Interactive and Adaptive Visualisation For Mobile Data Mining

visualisation of Data Mining results
visualisation of Data Mining results


There is an emerging focus on real-time data stream analysis and visualisation on mobile devices. The recent growth of smart phones capabilities has attracted many fields to port their applications to the mobile context. Examples of such mobile data analysis applications are patient monitoring, traffic monitoring, an stock market visualisation systems. Those types of applications require real-time analysis and visualisation of continuous, and voluminous datasets. Users of those systems need to make well-informed decisions in real-time based on the analysis of constant monitoring
on different sensors or other data sources. Thus, mining data streams on mobile devices is becoming increasingly an important field that is termed as Mobile/Ubiquitous Data Mining (UDM).
UDM refers to the process of data analysis that is performed on portable and embedded devices in distributed environments where data is transmitted in form of a continuous stream. Users, who are in a need for data stream analysis and visualisation in time critical scenarios, are the main target of UDM visualisation since it allows data mining to be accessible anywhere at anytime. However, visualising the outcome of UDM analysis on mobile devices faces many challenges due to the resources constraints and the characteristics of display screens of mobile devices as well as the distinctive characteristics of data streams. While limited computational resources, small screen space, and limited power supply are some of the limitations of mobile devices, data
streams also have challenging characterises such as being dynamic, changing, and continuous.

Several resource-aware and adaptive strategies and algorithms have recently been developed for UDM. This trend has shaded the lights on UDM visualisation as it is essential to quickly and appropriately understand the data analysis outcome. Clutter has been identified as a key issue in UDM visualisation, and clutter-adaptiv techniques have also been developed. However, visualising the continuous analysis of real-time data streams also requires adaptive, interactive, and user-driven style of presentation to ensure accuracy and appropriate comprehension of the data visualisation for mobile

Therefore, in this thesis, we propose, develop, and evaluate interactive, adaptive, generic, and user-driven/controlled UDM visualisation strategies that extend the state-of-the-art clutter-aware UDM visualisation strategies. We have implemented and
experimentally evaluated the proposed framework to examine its effectiveness in terms of clutter reduction, visualisation enhancement, and improving resource management.
The system was evaluated using publically available stock market data as well as geolocations dataset that was obtained by geo-coding addresses from the web. Finally, we have provided demonstrations of the application, which has been made available on the web and on the Google Android App-Store for free download to show the operation of our system. The outcomes of this dissertation have resulted in a Demo paper in ICDM 2010, and a conference paper in ICTAI 2010.

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2 Replies to “Interactive and Adaptive Visualisation For Mobile Data Mining”

  1. Dear Hasnain AlTaiar,
    I was very interested when I read your paper entitled “Interactive self-adaptive clutter-aware visualization for mobile data mining” that is already in the publish in the Journal of Computer and System Sciences 79 (2013) 369-382, Elsevier. Currently I’m undergoing a Ph.D at Kumamoto University, Japan. I wanted to continue with the next study as you write related to Future work includes developing visualization techniques that are based on clutter-awareness and adaptation for other mobile data analysis techniques (i.e. beyond clustering) Reviews such as classification, change detection and analysis of frequent items. However, I find it difficult to understand more deeply than the paper when not know your application has been developed. If you do not mind, allow me to access or download an application in this paper? I still put your name as a developer and I maintain the academic ethics.

    Thank you.


    1. Hi Risnandar,
      Thanks for your comment, glad that you find my research paper useful 🙂
      I am not sure if I can share the application code with you as it was part of a bigger research project in Monash University and as so the University maintain the Intellectual Property (IP), so I am sorry that I cannot share the application with you.
      However, I am more than happy to explain any concept or parts of the implementation that you are finding it hard to comprehend.
      Hope that could help and all the best.

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