Automating Management of Resources on Desktop Machines

This source preferred by Nan Jiang

Authors: Lim, M.G., Frank, W. and Jiang, N.

Start date: 25 March 2015

DOI: 10.1109/UKSim.2015.98

This data was imported from Scopus:

Authors: Lim, M.G., Wang, F. and Jiang, N.

Journal: Proceedings - UKSim-AMSS 17th International Conference on Computer Modelling and Simulation, UKSim 2015

Pages: 188-191

ISBN: 9781479987122

DOI: 10.1109/UKSim.2015.98

© 2015 IEEE. Two challenges that affect retrieval tasks in a typical computer desktop user environment are the speed and accuracy of accessing information such as documents and media files. Generating a common model to fit patterns of use with a reasonable level of prediction is challenging. This is as the individual usage patterns on the desktop is assumed to vary largely depending on the context of task. In this paper, we propose a lightweight learning framework, Usage Provenance and Prediction (UPP) model, which traces the user's mouse input and predicts the associated application with the media content. We disseminate the architecture of our tracing framework that is embedded on modern operating systems and show that this model adapts and improves data seeking experience at a user level. Our UPP framework achieves 96.70% accuracy with average application speed improvements of up to 32.92% over standard application launches. The results are encouraging and supports the potential of automating resources through the application of tracing usage patterns.

This data was imported from Web of Science (Lite):

Authors: Lim, M.G., Wang, F. and Jiang, N.

Journal: 2015 17TH UKSIM-AMSS INTERNATIONAL CONFERENCE ON COMPUTER MODELLING AND SIMULATION (UKSIM)

Pages: 188-191

ISSN: 2381-4772

DOI: 10.1109/UKSim.2015.98

The data on this page was last updated at 05:18 on March 30, 2020.