Abstract
Chance discovery provides a way to find rare but very important events for future decision making. It can be applied to stock market prediction, earthquake alarm, intrusion detection and social community evolution modeling. Similarly, contents management procedure can be regarded as a new application of chance discovery. By combining context information with contents, user’s trivial behavior at a specific area can result in sudden photo creation, long-time mp3 listening, and going to specific area for content creation. Using chance discovery, such novel situation can be modeled and predictable. 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. We propose a KeyGraph-based reorganization method of mobile device storage for better accessibility to the data based on chance discovery. It can help user not only find useful information from the storage but also refresh his/her memory by using the summary of novel events. User can recall his/her memory from the contents and contexts. Using artificially generated logs from a pre-defined scenario, the proposed method is tested and analyzed to check the possibility.
| Original language | English |
|---|---|
| Pages (from-to) | 313-320 |
| Number of pages | 8 |
| Journal | International Journal of Knowledge-Based and Intelligent Engineering Systems |
| Volume | 11 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2007 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Artificial Intelligence
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}
KeyGraph-based chance discovery for mobile contents management system. / Kim, Kyung Joong; Jung, Myung Chul; Cho, Sung-Bae.
In: International Journal of Knowledge-Based and Intelligent Engineering Systems, Vol. 11, No. 5, 01.01.2007, p. 313-320.Research output: Contribution to journal › Article
TY - JOUR
T1 - KeyGraph-based chance discovery for mobile contents management system
AU - Kim, Kyung Joong
AU - Jung, Myung Chul
AU - Cho, Sung-Bae
PY - 2007/1/1
Y1 - 2007/1/1
N2 - Chance discovery provides a way to find rare but very important events for future decision making. It can be applied to stock market prediction, earthquake alarm, intrusion detection and social community evolution modeling. Similarly, contents management procedure can be regarded as a new application of chance discovery. By combining context information with contents, user’s trivial behavior at a specific area can result in sudden photo creation, long-time mp3 listening, and going to specific area for content creation. Using chance discovery, such novel situation can be modeled and predictable. 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. We propose a KeyGraph-based reorganization method of mobile device storage for better accessibility to the data based on chance discovery. It can help user not only find useful information from the storage but also refresh his/her memory by using the summary of novel events. User can recall his/her memory from the contents and contexts. Using artificially generated logs from a pre-defined scenario, the proposed method is tested and analyzed to check the possibility.
AB - Chance discovery provides a way to find rare but very important events for future decision making. It can be applied to stock market prediction, earthquake alarm, intrusion detection and social community evolution modeling. Similarly, contents management procedure can be regarded as a new application of chance discovery. By combining context information with contents, user’s trivial behavior at a specific area can result in sudden photo creation, long-time mp3 listening, and going to specific area for content creation. Using chance discovery, such novel situation can be modeled and predictable. 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. We propose a KeyGraph-based reorganization method of mobile device storage for better accessibility to the data based on chance discovery. It can help user not only find useful information from the storage but also refresh his/her memory by using the summary of novel events. User can recall his/her memory from the contents and contexts. Using artificially generated logs from a pre-defined scenario, the proposed method is tested and analyzed to check the possibility.
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UR - http://www.scopus.com/inward/citedby.url?scp=85013594981&partnerID=8YFLogxK
U2 - 10.3233/KES-2007-11507
DO - 10.3233/KES-2007-11507
M3 - Article
AN - SCOPUS:85013594981
VL - 11
SP - 313
EP - 320
JO - International Journal of Knowledge-Based and Intelligent Engineering Systems
JF - International Journal of Knowledge-Based and Intelligent Engineering Systems
SN - 1327-2314
IS - 5
ER -