Improving business process quality through exception understanding, prediction, and prevention

Daniela Grigori, Fabio Casati, Umesh Dayal, Ming Chien Shan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

123 Citations (Scopus)

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 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.

Original languageEnglish
Title of host publicationVLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases
PublisherMorgan Kaufmann Publishers, Inc.
Pages159-168
Number of pages10
ISBN (Electronic)1558608044, 9781558608047
Publication statusPublished - 2001
Externally publishedYes
Event27th International Conference on Very Large Data Bases, VLDB 2001 - Roma, Italy
Duration: 11 Sep 200114 Sep 2001

Conference

Conference27th International Conference on Very Large Data Bases, VLDB 2001
CountryItaly
CityRoma
Period11.9.0114.9.01

Fingerprint

Industry
Data warehouses
Data mining
Automation
Personnel
Process quality
Business process
Prediction
Service level agreement
E-services
Data warehousing
Employees
Deviation

ASJC Scopus subject areas

  • Information Systems and Management
  • Computer Science Applications
  • Hardware and Architecture
  • Software
  • Computer Networks and Communications
  • Information Systems

Cite this

Grigori, D., Casati, F., Dayal, U., & Shan, M. C. (2001). Improving business process quality through exception understanding, prediction, and prevention. In VLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases (pp. 159-168). Morgan Kaufmann Publishers, Inc..

Improving business process quality through exception understanding, prediction, and prevention. / Grigori, Daniela; Casati, Fabio; Dayal, Umesh; Shan, Ming Chien.

VLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers, Inc., 2001. p. 159-168.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Grigori, D, Casati, F, Dayal, U & Shan, MC 2001, Improving business process quality through exception understanding, prediction, and prevention. in VLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers, Inc., pp. 159-168, 27th International Conference on Very Large Data Bases, VLDB 2001, Roma, Italy, 11.9.01.
Grigori D, Casati F, Dayal U, Shan MC. Improving business process quality through exception understanding, prediction, and prevention. In VLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers, Inc. 2001. p. 159-168
Grigori, Daniela ; Casati, Fabio ; Dayal, Umesh ; Shan, Ming Chien. / Improving business process quality through exception understanding, prediction, and prevention. VLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases. Morgan Kaufmann Publishers, Inc., 2001. pp. 159-168
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