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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14294
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| Title: | Discovering Unexpected Patterns in Temporal Data Using Temporal Logic |
| Authors: | Berger, Gideon
Tuzhilin, Alexander |
| Issue Date: | 1998 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-98-07 |
| Abstract: | There has been much attention given recently to the task of finding
interesting patterns in temporal databases. Since there are so many
different approaches to the problem of discovering temporal patterns, we
first present a characterization of different discovery tasks and then
focus on one task of discovering interesting patterns of events in
temporal sequences. Given an (infinite) temporal database or a sequence
of events one can, in general, discover an infinite number of temporal
patterns in this data. Therefore, it is important to specify some
measure of interestingness for discovered patterns and then select only
the patterns interesting according to this measure. We present a
probabilistic measure of interestingness based on unexpectedness,
whereby a pattern P is deemed interesting if the ratio of the actual
number of occurrences of P exceeds the expected number of occurrences of
P by some user defined threshold. We then make use of a subset of the
propositional, linear temporal logic and present an efficient algorithm
that discovers unexpected patterns in temporal data. Finally, we apply
this algorithm to synthetic data, UNIX operating system calls, and Web
logfiles and present the results of these experiments. |
| URI: | http://hdl.handle.net/2451/14294 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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