Temporal reasoning based on semi-intervals
Artificial Intelligence
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintaining knowledge about temporal intervals
Communications of the ACM
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Managing Interesting Rules in Sequence Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships
IEEE Transactions on Knowledge and Data Engineering
ACM SIGMOD Record
ACM Computing Surveys (CSUR)
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
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Sequence mining is often conducted over static and temporal datasets as well as over collections of events (episodes). More recently, there has also been a focus on the mining of streaming data. However, while many sequences are associated with absolute time values, most sequence mining routines treat time in a relative sense, only returning patterns that can be described in terms of Allen-style relationships (or simpler). In this work we investigate the accommodation of timing marks within the sequence mining process. The paper discusses the opportunities presented and the problems that may be encountered and presents a novel algorithm, INTEM™, that provides support for timing marks. This enables sequences to be examined not only in respect of the order and occurrence of tokens but also in terms of pace. Algorithmic considerations are discussed and an example provided for the case of polled sensor data.