Abstract
The number of information sources and the volumes of data in these information sources have greatly increased, which may be attributed to the ever-increasing complexity of real-world applications. The enormous amount of information available in the information sources in a distributed information-providing environment has created a need to provide users with tools to effectively and efficiently navigate and retrieve information. Queries in such an environment often access information from multiple information sources. This may be attributed to navigational characteristics. Clusters provide a structure for organizing the large number of information sources for efficient browsing, searching, and retrieval. This paper presents a stochastically-based clustering mechanism, called the Markov model mediator (MMM), to group the information sources into a set of useful clusters. Each information source cluster groups those information sources that show similarities in their data access behavior. Information sources within the same cluster are expected to be able to provide most of the required information among themselves for user queries that are closely related with respect to a particular, application. This can significantly improve system response time, query performance, and result in an overall improvement in decision support. Empirical studies on real databases are performed and the results demonstrate that our proposed mechanism leads to a better set of clusters in comparison with other clustering methods. This serves to illustrate the effectiveness of our proposed MMM mechanism.
| Original language | English |
|---|---|
| Pages (from-to) | 2035-2047 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |
| Volume | 34 |
| Issue number | 5 |
| DOIs | |
| State | Published - Oct 1 2004 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
- Human-Computer Interaction
Cite this
Stochastic clustering for organizing distributed information sources. / Shyu, Mei-Ling; Chen, Shu Ching; Rubin, Stuart H.
In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 34, No. 5, 01.10.2004, p. 2035-2047.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Stochastic clustering for organizing distributed information sources
AU - Shyu, Mei-Ling
AU - Chen, Shu Ching
AU - Rubin, Stuart H.
PY - 2004/10/1
Y1 - 2004/10/1
N2 - The number of information sources and the volumes of data in these information sources have greatly increased, which may be attributed to the ever-increasing complexity of real-world applications. The enormous amount of information available in the information sources in a distributed information-providing environment has created a need to provide users with tools to effectively and efficiently navigate and retrieve information. Queries in such an environment often access information from multiple information sources. This may be attributed to navigational characteristics. Clusters provide a structure for organizing the large number of information sources for efficient browsing, searching, and retrieval. This paper presents a stochastically-based clustering mechanism, called the Markov model mediator (MMM), to group the information sources into a set of useful clusters. Each information source cluster groups those information sources that show similarities in their data access behavior. Information sources within the same cluster are expected to be able to provide most of the required information among themselves for user queries that are closely related with respect to a particular, application. This can significantly improve system response time, query performance, and result in an overall improvement in decision support. Empirical studies on real databases are performed and the results demonstrate that our proposed mechanism leads to a better set of clusters in comparison with other clustering methods. This serves to illustrate the effectiveness of our proposed MMM mechanism.
AB - The number of information sources and the volumes of data in these information sources have greatly increased, which may be attributed to the ever-increasing complexity of real-world applications. The enormous amount of information available in the information sources in a distributed information-providing environment has created a need to provide users with tools to effectively and efficiently navigate and retrieve information. Queries in such an environment often access information from multiple information sources. This may be attributed to navigational characteristics. Clusters provide a structure for organizing the large number of information sources for efficient browsing, searching, and retrieval. This paper presents a stochastically-based clustering mechanism, called the Markov model mediator (MMM), to group the information sources into a set of useful clusters. Each information source cluster groups those information sources that show similarities in their data access behavior. Information sources within the same cluster are expected to be able to provide most of the required information among themselves for user queries that are closely related with respect to a particular, application. This can significantly improve system response time, query performance, and result in an overall improvement in decision support. Empirical studies on real databases are performed and the results demonstrate that our proposed mechanism leads to a better set of clusters in comparison with other clustering methods. This serves to illustrate the effectiveness of our proposed MMM mechanism.
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U2 - 10.1109/TSMCB.2004.833599
DO - 10.1109/TSMCB.2004.833599
M3 - Article
C2 - 15503499
AN - SCOPUS:4844220829
VL - 34
SP - 2035
EP - 2047
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SN - 1083-4419
IS - 5
ER -