Discovering sequential relationships in a time sequence is often important to many application domains, including financial, manufacturing, etc. Indeed, a lot of work has been do e in this area (see [1, 6] for an overview). An important aspect for such a discovery process is, however, l argely missing from the literature: namely discovering temporal patterns or relationships that involve multiple t ime granularities. This paper reports the progress in this front. A more detailed study can be found in [4]. In this paper, we focus on algorithms for discovering sequen tial relationships when a rough pattern of relationships is given. The rough pattern (which we term “event s tructure”) specifies what sort of relationships a user is interested in. For example, a user may be interested in “wh ich pairs of events occur frequently one week after another”. The algorithms will find the instances that fit the e v nt structure. They can also be used when more information is filled in the structure, as in in the following example from the stock market domain: “what kind of events frequently follow within 5 trading-days from the o ccurrence of a rise in IBM stocks, when this is preceded, one week before, by the fall of another hi-tech compan y stock”. These algorithms form a core component for a data mining environment. We view the actual data mining process as being done interact ively through a user interface. Data mining requests (in terms of event structures) are issued through t he user interface and processed using the algorithms. Initially, the user will issue a request with a simple struct ure. Complicated structures may be given by the user
[1]
Philip Laird,et al.
Identifying and Using Patterns in Sequential Data
,
1993,
ALT.
[2]
Heikki Mannila,et al.
Discovering frequent episodes in sequences extended abstract
,
1995,
KDD 1995.
[3]
Heikki Mannila,et al.
Discovering Frequent Episodes in Sequences
,
1995,
KDD.
[4]
Kaizhong Zhang,et al.
Combinatorial pattern discovery for scientific data: some preliminary results
,
1994,
SIGMOD '94.
[5]
Ke Wang,et al.
Incremental Discovery of Sequential Patterns
,
1996
.
[6]
Ramakrishnan Srikant,et al.
Mining sequential patterns
,
1995,
Proceedings of the Eleventh International Conference on Data Engineering.
[7]
Tomasz Imielinski,et al.
Database Mining: A Performance Perspective
,
1993,
IEEE Trans. Knowl. Data Eng..
[8]
Rajeev Alur,et al.
A Theory of Timed Automata
,
1994,
Theor. Comput. Sci..
[9]
Sushil Jajodia,et al.
Logical design for temporal databases with multiple granularities
,
1997,
TODS.
[10]
X.S. Wang,et al.
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
,
1998,
IEEE Trans. Knowl. Data Eng..