A generic solution for warehousing business process data

Fabio Casati, Malu Castellanos, Umeshwar Dayal, Norman Salazar

Результат исследований: Материалы для книги/типы отчетовМатериалы для конференции

48 Цитирования (Scopus)

Аннотация

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.

Язык оригиналаАнглийский
Название основной публикации33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings
ИздательAssociation for Computing Machinery, Inc
Страницы1128-1137
Число страниц10
ISBN (электронное издание)9781595936493
СостояниеОпубликовано - 2007
Опубликовано для внешнего пользованияДа
Событие33rd International Conference on Very Large Data Bases, VLDB 2007 - Vienna, Австрия
Продолжительность: 23 сен 200727 сен 2007

Конференция

Конференция33rd International Conference on Very Large Data Bases, VLDB 2007
СтранаАвстрия
ГородVienna
Период23.9.0727.9.07

ASJC Scopus subject areas

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

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  • Цитировать

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