A complete chronicle discovery approach: application to activity analysis

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.

[1]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[2]  M S Magnusson,et al.  Discovering hidden time patterns in behavior: T-patterns and their detection , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[3]  Sourav S. Bhowmick,et al.  Sequential Pattern Mining: A Survey , 2003 .

[4]  Gemma Casas-Garriga.  Discovering Unbounded Episodes in Sequential Data , 2003 .

[5]  Alain Mille,et al.  Creating Cognitive Models from Activity Analysis: A Knowledge Engineering Approach to Car Driver Modeling , 2007 .

[6]  Alain Mille,et al.  Using object memory patterns to make plan-driven help systems more flexible , 2009, Intelligent Environments.

[7]  James F. Allen.  Maintaining knowledge about temporal intervals , 1983, CACM.

[8]  M. Schneider.  The semantic cookbook: sharing cooking experiences in the smart kitchen , 2007 .

[9]  Christophe Dousson,et al.  Discovering Chronicles with Numerical Time Constraints from Alarm Logs for Monitoring Dynamic Systems , 1999, IJCAI.

[10]  P. S. Sastry,et al.  A fast algorithm for finding frequent episodes in event streams , 2007, KDD '07.

[11]  P. S. Sastry,et al.  A survey of temporal data mining , 2006 .

[12]  Dimitrios Gunopulos,et al.  Mining Process Models from Workflow Logs , 1998, EDBT.

[13]  Mohammed J. Zaki,et al.  SPADE: An Efficient Algorithm for Mining Frequent Sequences , 2004, Machine Learning.

[14]  Josef F. Krems,et al.  The quality of behavioral and environmental indicators used to infer the intention to change lanes , 2007 .

[15]  Evert W. Beth,et al.  Mathematical Epistemology And Psychology , 1966 .

[16]  Boudewijn F. van Dongen,et al.  Workflow mining: A survey of issues and approaches , 2003, Data Knowl. Eng..

[17]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

[18]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[19]  Malik Ghallab,et al.  Situation Recognition: Representation and Algorithms , 1993, IJCAI.

[20]  Christophe Dousson,et al.  Chronicle Recognition Improvement Using Temporal Focusing and Hierarchization , 2007, IJCAI.

[21]  David F. Redmiles,et al.  Extracting usability information from user interface events , 2000, CSUR.

[22]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[23]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[24]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[25]  Yannick Prié,et al.  An approach to User-Centric Context-Aware Assistance based on Interaction Traces , 2008 .

[26]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[27]  Marek Wojciechowski.  Interactive Constraint-Based Sequential Pattern Mining , 2001, ADBIS.

[28]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[29]  Howard J. Hamilton,et al.  Interestingness measures for data mining: A survey , 2006, CSUR.

[30]  Jian Pei,et al.  Mining sequential patterns with constraints in large databases , 2002, CIKM '02.

[31]  Alain Mille,et al.  Analyzing Behaviorial Data for Refining Cognitive Models of Operator , 2006, 17th International Workshop on Database and Expert Systems Applications (DEXA'06).

[32]  Dario D. Salvucci.  Inferring Driver Intent: A Case Study in Lane-Change Detection , 2004 .

[33]  Penelope M. Sanderson,et al.  Exploratory sequential data analysis: exploring continuous observational data , 1996, INTR.