TY - GEN
T1 - Similarity score for information filtering thresholds in business processes
AU - Lai, Jun
AU - Son, Ben
AU - Ali, Saqib
N1 - Publisher Copyright:
© 2004 IEEE.
PY - 2004
Y1 - 2004
N2 - The tremendous growth in the amount of information available poses some key challenges for information filtering and retrieval. Users not only expect high quality and relevant information, but also wish that the information be presented in an as efficient way as possible. The traditional filtering methods, however, only consider the relevant values of document. These conventional methods fail to consider the efficiency of documents retrieval. In this paper, we propose a new algorithm to calculate pn index called document similarity score based on elements of the document. Using the index, document profile will be derived. Any documents with the similarity score above a given threshold wilt be clustered. Using these pre-clusiered documents, information filtering and retrieval can be made more efficient. Experimental results clearly show our proposed method tremendously improves the efficiency of information filtering and retrieval. We also give an example application of our proposed method in business processes.
AB - The tremendous growth in the amount of information available poses some key challenges for information filtering and retrieval. Users not only expect high quality and relevant information, but also wish that the information be presented in an as efficient way as possible. The traditional filtering methods, however, only consider the relevant values of document. These conventional methods fail to consider the efficiency of documents retrieval. In this paper, we propose a new algorithm to calculate pn index called document similarity score based on elements of the document. Using the index, document profile will be derived. Any documents with the similarity score above a given threshold wilt be clustered. Using these pre-clusiered documents, information filtering and retrieval can be made more efficient. Experimental results clearly show our proposed method tremendously improves the efficiency of information filtering and retrieval. We also give an example application of our proposed method in business processes.
KW - Business process
KW - Clustering
KW - Elements
KW - Information filtering
KW - Information retrieval
KW - Search engine
KW - Web crawlers
KW - World Wide Web
UR - http://www.scopus.com/inward/record.url?scp=84935115300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84935115300&partnerID=8YFLogxK
U2 - 10.1109/INMIC.2004.1492988
DO - 10.1109/INMIC.2004.1492988
M3 - Conference contribution
AN - SCOPUS:84935115300
T3 - Proceedings of INMIC 2004 - 8th International Multitopic Conference
SP - 743
EP - 748
BT - Proceedings of INMIC 2004 - 8th International Multitopic Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th International Multitopic Conference, INMIC 2004
Y2 - 24 December 2004 through 26 December 2004
ER -