Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Maintaining knowledge about temporal intervals
Communications of the ACM
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Extracting usability information from user interface events
ACM Computing Surveys (CSUR)
Mining sequential patterns with constraints in large databases
Proceedings of the eleventh international conference on Information and knowledge management
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Interactive Constraint-Based Sequential Pattern Mining
ADBIS '01 Proceedings of the 5th East European Conference on Advances in Databases and Information Systems
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Analyzing Behaviorial Data for Refining Cognitive Models of Operator
DEXA '06 Proceedings of the 17th International Conference on Database and Expert Systems Applications
A fast algorithm for finding frequent episodes in event streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Situation recognition: representation and algorithms
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Chronicle recognition improvement using temporal focusing and hierarchization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning
IEEE Transactions on Intelligent Transportation Systems
On mining clinical pathway patterns from medical behaviors
Artificial Intelligence in Medicine
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Discovering temporal patterns hidden in a sequence of events has applications in numerous areas like network failure analysis, customer behaviour analysis, web navigation pattern discovery, etc. In this article, we present an approach to the discovery of chronicles hidden in the interaction traces of a human activity with the intention of characterizing some interesting tasks. Chronicles are a special type of temporal patterns, where temporal orders of events are quantified with numerical bounds. The algorithm we present is the first existing chronicle discovery algorithm that is complete. It is a chronicle discovery framework that can be configured to behave exactly as non-complete algorithms existing in litterature with no reduction of performance, but it can also be extended to other useful chronicle discovery problems like hybrid episode discovery. We show that the complete chronicle discovery problem has a very high complexity but we argue and illustrate that this high complexity is acceptable when the knowledge discovery process in which our algorithm takes part is real time and interactive. The platform Scheme Emerger, also presented in this paper, has been developed in order to implement the algorithm and to support graphically the real time and interactive chronicle discovery process. © 2012 Wiley Periodicals, Inc.