Personal Event Management among Multiple Devices Based on User Intention Recognition Using Dynamic Bayesian Networks

• Published in: IEICE Transactions on Information and Systems Vol. E94.D; no. 7; pp. 1440 - 1448
• Main Authors: JEON, Hocheol, KIM, Taehwan, CHOI, Joongmin
• Format: Journal Article
• Language: English
• Published: Oxford The Institute of Electronics, Information and Communication Engineers 2011; Oxford University Press
• Subjects: Applied sciences; Bayesian analysis; Computers, microcomputers; Devices; dynamic Bayesian networks (DBN); Dynamical systems; Dynamics; Electronics; Exact sciences and technology; Hardware; Monitors; Networks; personal event management service (PEMS); proactive task execution; Recognition; Servers; Services and terminals of telecommunications; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Telephone. Videophone; user intention recognition; Performance evaluation; Laptop computer; proactive task execution; Customization; Reuse; personal event management service (PEMS); Implementation; Networked system; Accuracy; Monitor; dynamic Bayesian networks (DBN); user intention recognition; Bayes network; Smartphone
• ISSN: 0916-8532; 1745-1361; 1745-1361
• DOI: 10.1587/transinf.E94.D.1440

This paper proposes a proactive management system for the events that occur across multiple personal user devices, including desktop PCs, laptops, and smart phones. We implemented the Personal Event Management Service using Dynamic Bayesian Networks (PEMS-DBN) system that proactively executes appropriate tasks across multiple devices without explicit user requests by recognizing the user's device reuse intention, based on the observed actions of the user for specific devices. The client module of PEMS-DBN installed on each device monitors the user actions and recognizes user intention by using dynamic Bayesian networks. The server provides data sharing and maintenance for the clients. A series of experiments were performed to evaluate user satisfaction and system accuracy, and also the amounts of resource consumption during intention recognition and proactive execution are measured to ensure the system efficiency. The experimental results showed that the PEMS-DBN system can proactively provide appropriate, personalized services with a high degree of satisfaction to the user in an effective and efficient manner.