Activity-Based Proactive Data Management in Mobile Environments

• Published in: IEEE transactions on mobile computing 2010-03, Vol.9 (3), p.390-404
• Main Authors: Wu, Shiow-yang, Fan, Hsiu-Hao
• Format: Magazinearticle
• Language: English
• Subjects: Activity mining; Allocations; Applied sciences; Behavior; Computer science; control theory; systems; Computer simulation; Cost engineering; Costs; Data management; Data mining; Data processing. List processing. Character string processing; Delay; Environmental management; Exact sciences and technology; Information theory; Information, signal and communications theory; Interleaved codes; Maintenance; Mathematical models; Memory organisation. Data processing; mobile environments; Motion pictures; Navigation; Personalized; Position (location); Prediction algorithms; Prefetching; proactive data management; pushing; Software; Studies; Telecommunications and information theory
• ISSN: 1536-1233; 1558-0660
• DOI: 10.1109/TMC.2009.139

Most users in a mobile environment are moving and accessing wireless services for the activities they are currently engaged in. We propose the idea of complex activity for characterizing the continuously changing complex behavior patterns of mobile users. For the purpose of data management, a complex activity is modeled as a sequence of location movement, service requests, the cooccurrence of location and service, or the interleaving of all above. An activity may be composed of subactivities. Different activities may exhibit dependencies that affect user behaviors. We argue that the complex activity concept provides a more precise, rich, and detail description of user behavioral patterns which are invaluable for data management in mobile environments. Proper exploration of user activities has the potential of providing much higher quality and personalized services to individual user at the right place on the right time. We, therefore, propose new methods for complex activity mining, incremental maintenance, online detection and proactive data management based on user activities. In particular, we devise prefetching and pushing techniques with cost-sensitive control to facilitate predictive data allocation. Preliminary implementation and simulation results demonstrate that the proposed framework and techniques can significantly increase local availability, conserve execution cost, reduce response time, and improve cache utilization.