A generic solution for warehousing business process data

Fabio Casati, Malu Castellanos, Umeshwar Dayal, Norman Salazar

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

48 Citations (Scopus)

Abstract

Improving business processes is critical to any corporation. Process improvement requires analysis as its first basic step. Process analysis has many unique challenges: i) companies execute many business processes, and devising ad hoc solutions for each of them is too costly. Hence, generic approaches must be sought; ii) the abstraction level at which processes need to be analyzed is much higher with respect to the information available in the process execution environment; iii) the rapidly increasing need of co-developing the process analysis and the process automation solution and the scale of the problem makes it hard to cope with frequent changes in the sources of process data. To address these problems, we have developed a process warehousing solution, used by HP and its customers. In this paper we describe the solution, the challenges we had to face, and the lessons we learned in implementing and deploying it.

Original languageEnglish
Title of host publication33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings
PublisherAssociation for Computing Machinery, Inc
Pages1128-1137
Number of pages10
ISBN (Electronic)9781595936493
Publication statusPublished - 2007
Externally publishedYes
Event33rd International Conference on Very Large Data Bases, VLDB 2007 - Vienna, Austria
Duration: 23 Sep 200727 Sep 2007

Conference

Conference33rd International Conference on Very Large Data Bases, VLDB 2007
CountryAustria
CityVienna
Period23.9.0727.9.07

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems and Management
  • Information Systems
  • Software

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  • Cite this

    Casati, F., Castellanos, M., Dayal, U., & Salazar, N. (2007). A generic solution for warehousing business process data. In 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings (pp. 1128-1137). Association for Computing Machinery, Inc.