Modeling Data for Enterprise Systems with Memories

Modeling Data for Enterprise Systems with Memories

Tamara Babaian, Wendy Lucas
Copyright: © 2013 |Pages: 12
DOI: 10.4018/jdm.2013040101
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Abstract

Enterprise Resource Planning (ERP) systems are widely used but notoriously difficult to learn and master. The authors propose that a database approach to representing the system’s tasks, interface components, and usage logs in conjunction with the ERP domain data can serve as a foundation for improving system usability. The framework the authors designed supports automatic logging of user-system interactions and automated analysis of the logged data for enabling a variety of interface enhancements and assessments that can be performed dynamically by the system. Compared to existing work on usage logging, the authors’ framework expands the logging capabilities of ERP systems while providing a unifying basis for many different kinds of applications of log data.
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ERP systems support business processes throughout the enterprise, with each of those processes consisting of separate but related transactions. Researchers have explored applying data mining to system logs for identifying the composition of transactions into processes within desktop and web-based enterprise applications (e.g. Greco, Guzzo, and Sacc, 2005; Khasawneh and Chan, 2006; Bayir, Toroslu, Cosar, and Fidan, 2009; Shen, Fitzhenry, and Dietterich, 2009; van der Aalst, 2011). Rozinat and van der Aalst (2008) have shown that process sequences mined from ERP system logs often deviate from prescribed processes. Discovering the ways in which users actually perform processes with systems can provide critical input to organizations, which rely on ERP systems for standardizing around best practices. At the same time, knowledge of typically performed processes enables a variety of system enhancements, such as intelligent workflow assistants, auto-generated To-Do lists, and the automating of workflows.

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