Improved Artificial Negative Event Generation to Enhance Process Event Logs

Process mining is the research area that is concerned with knowledge discovery from event logs. Process mining faces notable difficulties. One is that process mining is commonly limited to the harder setting of unsupervised learning, since negative information about state transitions that were prevented from taking place (i.e. negative events) is often unavailable in real-life event logs. We propose a method to enhance process event logs with artificially generated negative events, striving towards the induction of a set of negative examples that is both correct (containing no false negative events) and complete (containing all, non-trivial negative events). Such generated sets of negative events can advantageously be applied for discovery and evaluation purposes, and in auditing and compliance settings.

[1]  Evelina Lamma,et al.  Exploiting Inductive Logic Programming Techniques for Declarative Process Mining , 2009, Trans. Petri Nets Other Model. Concurr..

[2]  Isidro Ramos,et al.  Advances in Database Technology — EDBT'98 , 1998, Lecture Notes in Computer Science.

[3]  Wil M. P. van der Aalst,et al.  Genetic process mining: an experimental evaluation , 2007, Data Mining and Knowledge Discovery.

[4]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[5]  Saso Dzeroski,et al.  Inductive Logic Programming: Techniques and Applications , 1993 .

[6]  U. M. Feyyad.  Data mining and knowledge discovery: making sense out of data , 1996 .

[7]  Bart Baesens,et al.  A robust F-measure for evaluating discovered process models , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[8]  Hendrik Blockeel,et al.  Top-Down Induction of First Order Logical Decision Trees , 1998, AI Commun..

[9]  Anindya Datta,et al.  Automating the Discovery of AS-IS Business Process Models: Probabilistic and Algorithmic Approaches , 1998, Inf. Syst. Res..

[10]  Wil M. P. van der Aalst,et al.  User-guided discovery of declarative process models , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[11]  Wil M. P. van der Aalst,et al.  A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs , 2005, Data Mining and Knowledge Discovery.

[12]  Alexander L. Wolf,et al.  Discovering models of software processes from event-based data , 1998, TSEM.

[13]  Evelina Lamma,et al.  Applying Inductive Logic Programming to Process Mining , 2007, ILP.

[14]  Evelina Lamma,et al.  Inducing Declarative Logic-Based Models from Labeled Traces , 2007, BPM.

[15]  Kalle Lyytinen,et al.  Attention Shaping and Software Risk - A Categorical Analysis of Four Classical Risk Management Approaches , 1998, Inf. Syst. Res..

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

[17]  Bart Baesens,et al.  Robust Process Discovery with Artificial Negative Events , 2009, J. Mach. Learn. Res..

[18]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[19]  Boudewijn F. van Dongen,et al.  ProM: The Process Mining Toolkit , 2009, BPM.

[20]  Wil M.P. van der Aalst,et al.  Process mining with the HeuristicsMiner algorithm , 2006 .

[21]  Wil M. P. van der Aalst,et al.  Transactions on Petri Nets and Other Models of Concurrency II, Special Issue on Concurrency in Process-Aware Information Systems , 2009, Trans. Petri Nets and Other Models of Concurrency.

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

[23]  Boudewijn F. van Dongen,et al.  Business process mining: An industrial application , 2007, Inf. Syst..

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

[25]  Jianmin Wang,et al.  Mining process models with non-free-choice constructs , 2007, Data Mining and Knowledge Discovery.

[26]  Chia-Hui Chang,et al.  Efficient mining of frequent episodes from complex sequences , 2008, Inf. Syst..

[27]  van der Wmp Wil Aalst,et al.  Process Mining , 2005, Process-Aware Information Systems.

[28]  Diogo R. Ferreira,et al.  An Integrated Life Cycle for Workflow Management Based on Learning and Planning , 2006, Int. J. Cooperative Inf. Syst..