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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14382
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| Title: | A BELIEF-DRIVEN DISCOVERY FRAMEWORK BASED ON DATA MONITORING AND TRIGGERING |
| Authors: | Silberschatz, Avi
Tuzhilin, Alexander |
| Issue Date: | Dec-1996 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-96-26 |
| Abstract: | A new knowledge-discovery framework, called Data Monitoring and
Discovery Triggering (DMDT), is defined, where the user specifies
monitors that âwatch" for significant changes to the data
and changes to the user-defined system of beliefs. Once these changes
are detected, knowledge discovery processes, in the form of data mining
queries, are triggered. The proposed framework is the result of an
observation, made in the previous work of the authors, that when changes
to the user-defined beliefs occur, this means that, there are
interesting patterns in the data. In this paper, we present an approach
for finding these interesting patterns using data monitoring and
belief-driven discovery techniques. Our approach is especially useful in
those applications where data changes rapidly with time, as in some of
the On-Line Transaction Processing (OLTP) systems. The proposed approach
integrates active databases, data mining queries and subjective measures
of interestingness based on user-defined systems of beliefs in a novel
and synergetic way to yield a new type of data mining systems. |
| URI: | http://hdl.handle.net/2451/14382 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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