TY - GEN
T1 - Episodic memory for ubiquitous multimedia contents management system
AU - Kim, Kyung Joong
AU - Jung, Myung Chul
AU - Cho, Sung Bae
PY - 2007
Y1 - 2007
N2 - Recently, mobile devices are regarded as a content storage with their functions such as camera, camcorder, and music player. It creates massive new data and downloads contents from desktop or wireless internet. Because of the massive size of digital contents in the mobile devices, user feels difficulty to recall or find information from the personal storage. If it is possible to organize the storage in a style of human-memory management, it could reduce user's effort in contents management. Based on the evidence that human memory is organized as an episodic-style, we propose a KeyGraph-based reorganization method of mobile device storage for better accessibility to the data. It can help user not only find useful information from the storage but also expand his/her memory by adding user's contexts such as location, SMS, call, and device status. User can recall his/her memory from the contents and contexts. KeyGraph finds rare but relevant events that can be used as a memory landmark in the episodic memory. Using artificially generated logs from a pre-defined scenario, the proposed method is tested and analyzed to check the possibility.
AB - Recently, mobile devices are regarded as a content storage with their functions such as camera, camcorder, and music player. It creates massive new data and downloads contents from desktop or wireless internet. Because of the massive size of digital contents in the mobile devices, user feels difficulty to recall or find information from the personal storage. If it is possible to organize the storage in a style of human-memory management, it could reduce user's effort in contents management. Based on the evidence that human memory is organized as an episodic-style, we propose a KeyGraph-based reorganization method of mobile device storage for better accessibility to the data. It can help user not only find useful information from the storage but also expand his/her memory by adding user's contexts such as location, SMS, call, and device status. User can recall his/her memory from the contents and contexts. KeyGraph finds rare but relevant events that can be used as a memory landmark in the episodic memory. Using artificially generated logs from a pre-defined scenario, the proposed method is tested and analyzed to check the possibility.
UR - http://www.scopus.com/inward/record.url?scp=37249068449&partnerID=8YFLogxK
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U2 - 10.1007/978-3-540-73325-6_79
DO - 10.1007/978-3-540-73325-6_79
M3 - Conference contribution
AN - SCOPUS:37249068449
SN - 9783540733225
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 796
EP - 805
BT - New Trends in Applied Artificial Intelligence - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE 2007, Proceedings
PB - Springer Verlag
T2 - 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, lEA/AlE-2007
Y2 - 26 June 2007 through 29 June 2007
ER -