초록 ▼

A method, system, and computer program product for associating events. A provided event dataset includes events occurring in each of N successive time intervals (N≧3). Association rules pertaining to successive events in the event dataset are deduced. Sequences of events are generated from th

A method, system, and computer program product for associating events. A provided event dataset includes events occurring in each of N successive time intervals (N≧3). Association rules pertaining to successive events in the event dataset are deduced. Sequences of events are generated from the association rules. Clusters of the sequences of events are formed. Sequences of the clusters are created. The clusters of the sequences may be used: to identify at least one event occurring in a time interval of the N time intervals as being a probable cause of at least one event occurring in a later-occurring time interval of the N time intervals; or to predict an occurrence of at least one event in a time interval occurring after the N time intervals, wherein the at least one event had occurred within the N time intervals.

대표청구항 ▼

What is claimed is: 1. A method for associating events, comprising the steps of: providing an event dataset that includes a plurality of events occurring in each of N successive time intervals, said N≧3, said event dataset being stored on a computer readable medium; deducing from the event d

What is claimed is: 1. A method for associating events, comprising the steps of: providing an event dataset that includes a plurality of events occurring in each of N successive time intervals, said N≧3, said event dataset being stored on a computer readable medium; deducing from the event dataset a plurality of association rules, each association rule EKEL of the plurality of association rules expressing an association between events EK and EL respectively occurring in two successive time intervals of the N time intervals, said events EK and EL being in the event dataset; generating a plurality of sequences of events, each sequence of the plurality of sequences being generated from at least two sequentially ordered association rules of the plurality of association rules; forming a plurality of clusters from the plurality of sequences in accordance with a clustering algorithm, each cluster of the plurality of clusters including at least two sequences of the plurality of sequences; creating SC sequences of clusters from the plurality of clusters, said SC≧1, each sequence of the SC sequences including at least two clusters of the plurality of clusters; and after the creating step, at least one of: identifying at least one event occurring in a first time interval of the N time intervals as being a probable cause of at least one event occurring in a later-occurring time interval of the N time intervals; and predicting an occurrence of at least one event in a time interval occurring after the N time intervals, wherein the at least one event had occurred within the N time intervals. 2. The method of claim 1, wherein each pair of sequentially ordered association rules of the plurality of association rules consists of a first association rule and a second association rule, and wherein the first association rule and the second association rule are independent of each other. 3. The method of claim 1, wherein the association rules from which a first sequence of the plurality of sequences has been generated include a pair of consecutively ordered first and second association rules which are not independent of each other. 4. The method of claim 1, wherein the N time intervals are contiguously sequenced. 5. The method of claim 1, wherein the N time intervals are not contiguously sequenced. 6. The method of claim 1, wherein each sequence of the plurality of sequences is of the form E1→E2 →E3→ . . . E1-1→E1 in relation to sequentially ordered association rules E1 E2, E3, . . . , EI-1EI of the plurality of association rules, and wherein I≧3. 7. The method of claim 1, wherein said each association rule satisfies a condition of α≦PKL<1, wherein PKL is the probability that EK and EL respectively occur in the two successive time intervals, and wherein α is a predetermined positive real number satisfying α<1. 8. The method of claim 7, wherein α is within a range selected from the group consisting of a first range of 0.50≦ α<1, a second range of 0.60≦α<1, a third range of 0.70≦α<1, a fourth range of 0. 80≦α<1, a fifth range of 0.90≦α <1, and a sixth range of 0.95≦α<1. 9. The method of claim 7, wherein each sequence of the plurality of sequences has a probability of occurrence no less than β , and wherein β is a predetermined positive real number satisfying β<α. 10. The method of claim 9, wherein β is within a range selected from the group consisting of a first range of 0.20≦ β<α, a second range of 0.30≦β<α, a third range of 0.40≦β<α, a fourth range of 0.50≦β<α, a fifth range of 0.60β ≦α, and a sixth range of 0.70≦β< α. 11. A system for associating events, comprising the steps of: means for providing an event dataset that includes a plurality of events occurring in each of N successive time intervals, said N≧3, said event dataset being stored on a computer readable medium; means for deducing, from the event dataset, a plurality of association rules, each association rule EK EL of the plurality of association rules expressing an association between events EK and EL respectively occurring in two successive time intervals of the N time intervals, said events EK and EL being in the event dataset; means for generating a plurality of sequences of events, each sequence of the plurality of sequences being generated from at least two sequentially ordered association rules of the plurality of association rules; means for forming a plurality of clusters from the plurality of sequences in accordance with a clustering algorithm, each cluster of the plurality of clusters including at least two sequences of the plurality of sequences; means for creating SC sequences of clusters from the plurality of clusters, said SC≧1, each sequence of the SC sequences including at least two clusters of the plurality of; and at least one of: means for using at least one sequence of the SC sequences to identify at least one event occurring in a first time interval of the N time intervals as being a probable cause of at least one event occurring in a later-occurring time interval of the N time intervals; and means for using a first sequence of the SC sequences to predict an occurrence of at least one event in a time interval occurring after the N time intervals, wherein the at least one event had occurred within the N time intervals. 12. The system of claim 11, wherein each pair of sequentially ordered association rules of the plurality of association rules consists of a first association rule and a second association rule, and wherein the first association rule and the second association rule are independent of each other. 13. The system of claim 11, wherein the association rules from which a first sequence of the plurality of sequences is generated by said means for generating include a pair of consecutively ordered first and second association rules which are not independent of each other. 14. The system of claim 11, wherein the N time intervals are contiguously sequenced. 15. The system of claim 11, wherein the N time intervals are not contiguously sequenced. 16. The system of claim 11, wherein each sequence of the plurality of sequences is of the form E1→E2 →E3→ . . . EI-1→E1 in relation to sequentially ordered association rules E1 E2, E2E3, . . . , EI-1E1 of the plurality of association rules, and wherein I≧3. 17. The system of claim 11, wherein said each association rule satisfies a condition of α≦PKL<1, wherein PKL is the probability that EK and EL respectively occur in the two successive time intervals, and wherein α is a predetermined positive real number satisfying α<1. 18. The system of claim 17, wherein α is within a range selected from the group consisting of a first range of 0.50≦ α<1, a second range of 0.60≦α<1, a third range of 0.70≦α<1, a fourth range of 0. 80≦α<1, a fifth range of 0.90≦α <1, and a sixth range of 0.95≦α<1. 19. The system of claim 17, wherein each sequence of the plurality of sequences has a probability of occurrence no less than β , and wherein β is a predetermined positive real number satisfying β<α. 20. The system of claim 19, wherein β is within a range selected from the group consisting of a first range of 0.20≦ β<α, a second range of 0.30≦β<α, a third range of 0.40≦β<α, a fourth range of 0.50≦β<α, a fifth range of 0.60β ≦α, and a sixth range of 0.70≦β< α. 21. A computer program product comprising a computer usable medium having a computer readable program embodied therein, said computer readable program adapted to access an event dataset that includes a plurality of events occurring in each of N successive time intervals, said event dataset being stored on a computer readable medium, said N≧3, said computer readable program further adapted execute a method for associating events, said method comprising the steps of: deducing from the event dataset a plurality of association rules, each association rule EKEL of the plurality of association rules expressing an association between events EK and EL respectively occurring in two successive time intervals of the N time intervals, said events EK and EL being in the event dataset; generating a plurality of sequences of events, each sequence of the plurality of sequences being generated from at least two sequentially ordered association rules of the plurality of association rules; forming a plurality of clusters from the plurality of sequences in accordance with a clustering algorithm, each cluster of the plurality of clusters including at least two sequences of the plurality of sequences; creating SC sequences of clusters from the plurality of clusters, said SC≧1, each sequence of the SC sequences including at least two clusters of the plurality of clusters; and after the creating step, at least one of: identifying at least one event occurring in a first time interval of the N time intervals as being a probable cause of at least one event occurring in a later-occurring time interval of the N time intervals; and predicting an occurrence of at least one event in a time interval occurring after the N time intervals, wherein the at least one event had occurred within the N time intervals. 22. The computer program product of claim 21, wherein each pair of sequentially ordered association rules of the plurality of association rules consists of a first association rule and a second association rule, and wherein the first association rule and the second association rule are independent of each other. 23. The computer program product of claim 21, wherein the association rules from which a first sequence of the plurality of sequences has been generated include a pair of consecutively ordered first and second association rules which are not independent of each other. 24. The computer program product of claim 21, wherein the N time intervals are contiguously sequenced. 25. The computer program product of claim 21, wherein the N time intervals are not contiguously sequenced. 26. The computer program product of claim 21, wherein each sequence of the plurality of sequences is of the form E1 →E2→E3→ . . . . EI-1→E1 in relation to sequentially ordered association rules E1 E2, E 2E3, . . . , EI-1 E1 of the plurality of association rules, and wherein I≧3. 27. The computer program product of claim 21, wherein said each association rule satisfies a condition of α≦P KL<1, wherein PKL is the probability that EK and EL respectively occur in the two successive time intervals, and wherein α is a predetermined positive real number satisfying α<1. 28. The computer program product of claim 27, wherein α is within a range selected from the group consisting of a first range of 0.50≦α<1, a second range of 0.60≦ α<1, a third range of 0.70≦α<1, a fourth range of 0.80≦α<1, a fifth range of 0. 90≦α<1, and a sixth range of 0.95 ≦ α<1. 29. The computer program product of claim 27, wherein each sequence of the plurality of sequences has a probability of occurrence no less than β, and wherein β is a predetermined positive real number satisfying β<α. 30. The computer program product of claim 29, wherein β is within a range selected from the group consisting of a first range of 0.20≦β<α, a second range of 0. 30≦β<α, a third range of 0.40≦ β<α, a fourth range of 0.50≦β<α, a fifth range of 0.60β≦α, and a sixth range of 0.70≦β<α.