An Extensible Framework for Analysing Resource Behaviour Using Event Logs

Business processes depend on human resources and managers must regularly evaluate the performance of their employees based on a number of measures, some of which are subjective in nature. As modern organisations use information systems to automate their business processes and record information about processes’ executions in event logs, it now becomes possible to get objective information about resource behaviours by analysing data recorded in event logs. We present an extensible framework for extracting knowledge from event logs about the behaviour of a human resource and for analysing the dynamics of this behaviour over time. The framework is fully automated and implements a predefined set of behavioural indicators for human resources. It also provides a means for organisations to define their own behavioural indicators, using the conventional Structured Query Language, and a means to analyse the dynamics of these indicators. The framework’s applicability is demonstrated using an event log from a German bank.

[1]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[2]  Huilong Duan,et al.  Resource behavior measure and application in business process management , 2012, Expert Syst. Appl..

[3]  Sai S. Nudurupati,et al.  State of the art literature review on performance measurement , 2011, Comput. Ind. Eng..

[4]  Moe Thandar Wynn,et al.  Profiling Event Logs to Configure Risk Indicators for Process Delays , 2013, CAiSE.

[5]  Wil M. P. van der Aalst,et al.  Analyzing Resource Behavior Using Process Mining , 2009, Business Process Management Workshops.

[6]  Mark P. J. van der Loo,et al.  Distribution based outlier detection in univariate data , 2010 .

[7]  Peter Murphy,et al.  Service performance measurement using simple techniques actually works , 1999 .

[8]  Niall M. Adams,et al.  Two Nonparametric Control Charts for Detecting Arbitrary Distribution Changes , 2012 .

[9]  Wil M. P. van der Aalst.  Business Process Simulation Revisited , 2010, EOMAS.

[10]  P. Priya,et al.  Dealing With Concept Drifts in Process Mining , .

[11]  Douglas M. Hawkins,et al.  A Nonparametric Change-Point Control Chart , 2010 .

[12]  Andy Neely,et al.  Performance measurement system design , 1995 .

[13]  Gary M. Thompson,et al.  Variable employee productivity in workforce scheduling , 2006, Eur. J. Oper. Res..

[14]  Wil M. P. van der Aalst,et al.  Towards comprehensive support for organizational mining , 2008, Decis. Support Syst..

[15]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[16]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[17]  Timothy Coelli,et al.  An Introduction to Efficiency and Productivity Analysis , 1997 .

[18]  Moe Thandar Wynn,et al.  Predicting Deadline Transgressions Using Event Logs , 2012, Business Process Management Workshops.

[19]  Michael J. Watts,et al.  IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS Publication Information , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Panagiotis Papapetrou,et al.  Size Matters: Finding the Most Informative Set of Window Lengths , 2012, ECML/PKDD.

[21]  Peter A. Flach,et al.  Evaluation Measures for Multi-class Subgroup Discovery , 2009, ECML/PKDD.

[22]  Hajo A. Reijers,et al.  Discovering Social Networks from Event Logs , 2005, Computer Supported Cooperative Work (CSCW).

[23]  M. J. Mawdesley,et al.  Perception of human risk factors in construction projects: an exploratory study , 2004 .

[24]  Guido Governatori,et al.  Compliance aware business process design , 2008 .