Browsing by Author "Hui, Lin"

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A Study of Integrating Social Networking Service into The Virtual Pet Web Game System
(2013)
Hui, Lin
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Lin, Wan-Chi
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Li, Xiwen
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
As the World Wide Web developed and the utilization rate of the mobile device grew up, a lot of computer game companies chose to expand their product platform to the web games or the smart phone apps. These two kinds of game categories can become as popular as nowadays, because these are the ways that users can easily have access to and to play the games with. Based on the above and the fact that social networking service has become a part of our daily life, we decided to merge all social network features into our web game to make our games more compatible. This research is focused on virtual pet game with ability of allowing interaction between friends\' pets and sharing messages among friends and the system are accessible through the web browser. In the future, we will combine social network system and the virtual pet game and also make a smart phone app version out of the combination, so that the users can take care of their own virtual pets anytime and anywhere as long as they have access to the Internet.
Incremental mining of temporal patterns in interval-based database
(2016-02-01)
Hui, Lin
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Chen, Yi-Cheng
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Weng, Julia Tzu-Ya
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Lee, Suh-Yin
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資訊工程學系
In several real-life applications, sequence databases, in general, are updated incrementally with time. Some discovered sequential patterns may be invalidated and some new ones may be introduced by the evolution of the database. When a small set of sequences grow, or when some new sequences are added into the database, re-mining sequential patterns from scratch each time is usually inefficient and thus not feasible. Although there have been several recent studies on the maintenance of sequential patterns in an incremental manner, these works only consider the patterns extracted from time point-based data. Few research efforts have been elaborated on maintaining time interval-based sequential patterns, also called temporal patterns, where each datum persists for a period of time. In this paper, an efficient algorithm, Inc_TPMiner (Incremental Temporal Pattern Miner) is developed to incrementally discover temporal patterns from interval-based data. Moreover, the algorithm employs some optimization techniques to reduce the search space effectively. The experimental results on both synthetic and real datasets indicate that Inc_TPMiner significantly outperforms re-mining with static algorithms in execution time and possesses graceful scalability. Furthermore, we also apply Inc_TPMiner on a real dataset to show the practicability of incremental mining of temporal patterns.