Qualitative data modeling: application of a mechanism for interpreting graphical data
Computational Intelligence
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Maintaining knowledge about temporal intervals
Communications of the ACM
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Automated mathematical modeling from experimental data: anapplication to material science
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning Patterns in Multidimensional Space Using Interval Algebra
AIMSA '02 Proceedings of the 10th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Handling Feature Ambiguity in Knowledge Discovery from Time Series
DS '02 Proceedings of the 5th International Conference on Discovery Science
Discovery of Core Episodes from Sequences
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Partial Elastic Matching of Time Series
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Deriving and Mining Spatiotemporal Event Schemas in In-Situ Sensor Data
ICCSA '08 Proceeding sof the international conference on Computational Science and Its Applications, Part I
Margin-closed frequent sequential pattern mining
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
ARTEMIS: assessing the similarity of event-interval sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Elastic partial matching of time series
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Learning rules with complex temporal patterns in biomedical domains
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Connecting the dots: constructing spatiotemporal episodes from events schemas
Transactions on Computational Science VI
Mining first-order temporal interval patterns with regular expression constraints
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Learning pattern graphs for multivariate temporal pattern retrieval
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
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Recently, association rule mining has been generalized to the discovery of episodes in event sequences. In this paper, we additionally take durations into account and thus present a generalization to time intervals. We discover frequent temporal patterns in a single series of such labeled intervals, which we call a state sequence. A temporal pattern is defined as a set of states together with their interval relationships described in terms of Allen's interval logic, for instance "A before B, A overlaps C, C overlaps B" or equivalently "state A ends before state B starts, the gap is covered by state C". As an example we consider the problem of deriving local weather forecasting rules that allow us to conclude from the qualitative behaviour of the air-pressure curve to the wind-strength. Here, the states have been extracted automatically from (multivariate) time series and characterize the trend of the time series locally within the assigned time interval.