Improving Business Process Quality through Exception Understanding, Prediction, and Preventing
Identifieur interne : 008C68 ( Main/Exploration ); précédent : 008C67; suivant : 008C69Improving Business Process Quality through Exception Understanding, Prediction, and Preventing
Auteurs : Daniela Grigori [France] ; Fabio Casati ; Umesh Dayal ; Ming-Chien ShanSource :
English descriptors
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.
Url:
Affiliations:
- France
- Grand Est, Lorraine (région)
- Nancy
- Institut national polytechnique de Lorraine, Université Nancy 2, Université de Lorraine
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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.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Nancy</li></settlement><orgName><li>Institut national polytechnique de Lorraine</li><li>Université Nancy 2</li><li>Université de Lorraine</li></orgName></list><tree><noCountry><name sortKey="Casati, Fabio" sort="Casati, Fabio" uniqKey="Casati F" first="Fabio" last="Casati">Fabio Casati</name><name sortKey="Dayal, Umesh" sort="Dayal, Umesh" uniqKey="Dayal U" first="Umesh" last="Dayal">Umesh Dayal</name><name sortKey="Shan, Ming Chien" sort="Shan, Ming Chien" uniqKey="Shan M" first="Ming-Chien" last="Shan">Ming-Chien Shan</name></noCountry><country name="France"><region name="Grand Est"><name sortKey="Grigori, Daniela" sort="Grigori, Daniela" uniqKey="Grigori D" first="Daniela" last="Grigori">Daniela Grigori</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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