Improving Business Process Quality through Exception Understanding, Prediction, and Prevention
Grigori, Daniela; Casati, Fabio; Dayal, Umeshwar; Shan, Ming-Chien (2001), Improving Business Process Quality through Exception Understanding, Prediction, and Prevention, in Snodgrass, Richard Thomas; Ramamohanarao, Kotagiri; Paraboschi, Stefano; Ceri, Stefano; Atzeni, Paolo; Apers, Peter M. G., Proceedings of the 27th International Conference on Very Large Data Bases, Morgan Kaufmann Publishers Inc. : San Francisco, p. 159-168
TypeCommunication / Conférence
Conference title27th International Conference on Very Large Databases (VLDB 2001)
Book titleProceedings of the 27th International Conference on Very Large Data Bases
Book authorSnodgrass, Richard Thomas; Ramamohanarao, Kotagiri; Paraboschi, Stefano; Ceri, Stefano; Atzeni, Paolo; Apers, Peter M. G.
Number of pages727
MetadataShow full item record
Abstract (EN)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 a 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 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.
Subjects / Keywordsdata warehousing; Service Level Agreement; web services; business process management
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