Book Chapter10.1007/BFB0101003
Mining Process Models from Workflow Logs
Rakesh Agrawal,Dimitrios Gunopulos,Frank Leymann +2 more
- 23 Mar 1998
- pp 469-483
TL;DR: This work presents an approach for a system that constructs process models from logs of past, unstructured executions of the given process, and presents results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation.
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Abstract: Modern enterprises increasingly use the workflow paradigm to prescribe how business processes should be performed. Processes are typically modeled as annotated activity graphs. We present an approach for a system that constructs process models from logs of past, unstructured executions of the given process. The graph so produced conforms to the dependencies and past executions present in the log. By providing models that capture the previous executions of the process, this technique allows easier introduction of a workflow system and evaluation and evolution of existing process models. We also present results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation.
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Citations
Learning from Observing: Vision and POIROT - Using Metareasoning for Self Adaptation
Mark Burstein,Robert J. Bobrow,William Ferguson,Robert Laddaga,Paul Robertson +4 more
- 27 Sep 2010
TL;DR: A cognitive architecture that heavily utilizes metareasoning for self adaptation is presented, derived in part from neuroscience data and theories about the operation of the human vision system.
Mining massive moving object datasets from rfid flow analysis to traffic mining
Jiawei Han,Hector Gonzalez +1 more
- 01 Jan 2008
TL;DR: A multi-dimensional mining framework that can be used to identify a concise set of anomalies from massive traffic monitoring data, and further overlay, contrast, and explore such anomalies in multi- dimensional space is proposed.
Analysis of the Maximal Pattern Mining method and its variants
Dávid Gégény,Sándor Radeleczki +1 more
TL;DR: This paper examines the Maximal Pattern Mining method introduced by Liesaputra et al. in [1], and introduces some new subroutines to handle the loops, parallel and optional sequences.
Process Mining Approach to Promote Business Intelligence in Iranian Detectives’ Police
Mehdi Ghazanfari,Mohammad Fathian,Mostafa Jafari,Saeed Rouhani +3 more
- 07 Jul 2010
TL;DR: This paper focuses on the potential use of process mining techniques to enable Iranian Detectives‘ Police for discovering grounds of crime and explains process mining how can assist the monitoring enterprise systems.
References
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Introduction to Algorithms
Thomas H. Cormen,Charles E. Leiserson,Ronald L. Rivest +2 more
- 01 Jan 1990
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
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Introduction to algorithms: 4. Turtle graphics
TL;DR: In this article, a language similar to logo is used to draw geometric pictures using this language and programs are developed to draw geometrical pictures using it, which is similar to the one we use in this paper.
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Mining sequential patterns
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 06 Mar 1995
TL;DR: Three algorithms are presented to solve the problem of mining sequential patterns over databases of customer transactions, and empirically evaluating their performance using synthetic data shows that two of them have comparable performance.
An overview of workflow management: from process modeling to workflow automation infrastructure
TL;DR: This paper provides a high-level overview of the current workflow management methodologies and software products and discusses how distributed object management and customized transaction management can support further advances in the commercial state of the art in this area.
1.7K
•Book
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Sholom M. Weiss,Casimir A. Kulikowski +1 more
- 01 Jan 1991
TL;DR: In this article, the authors discuss the importance of unbiased error rate estimation and find the right complexity fit to estimate the true performance of a learning system and compare it to the expected patterns of classifier behavior.
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