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Conference papers

Improving Business Process Quality through Exception Understanding, Prediction, and Preventing

Daniela Grigori 1 Fabio Casati Umesh Dayal Ming-Chien Shan
1 ECOO - Environment for cooperation
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to customers. In order to satisfy customers and employees, business processes need to be executed with both high and predictable quality. In particular, it is crucial for organizations to meet the Service Level Agreements (SLAs) stipulated with the customers and to foresee as early as possible the risk of missing SLAs, in order to set the right expectations and to allow for corrective actions. In this paper we focus on a critical issue in business process quality: that of analyzing, predicting and preventing the occurrence of exceptions, i.e., of deviations from the desired or acceptable behavior. We characterize the problem and propose a solution, based on data warehousing and data mining techniques. We then describe the architecture and implementation of a tool suite that enables exception analysis, prediction, and prevention. Finally, we show experimental results obtained by using the tool suite to analyze internal HP processes.
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Submitted on : Tuesday, September 26, 2006 - 2:41:40 PM
Last modification on : Friday, February 4, 2022 - 3:22:55 AM


  • HAL Id : inria-00100410, version 1



Daniela Grigori, Fabio Casati, Umesh Dayal, Ming-Chien Shan. Improving Business Process Quality through Exception Understanding, Prediction, and Preventing. Proceedings of 27th International Conference on Very Large Data Bases - VLDB'2001, Sep 2001, Roma, Italy, 10 p. ⟨inria-00100410⟩



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