论文信息 - Extended Analysis Techniques for a Comprehensive Business Process Optimization

Extended Analysis Techniques for a Comprehensive Business Process Optimization

Efficient adaption of a company’s business and its business processes to a changing environment is a crucial ability to survive in today’s dynamic world. For optimizing business processes, a profound analysis of all relevant business data in the company is necessary. We define an extended data warehouse approach that integrates process-related data and operational business data. This extended data warehouse is used as the underlying data source for extended OLAP and data mining analysis techniques for a comprehensive business process optimization.

Bernhard Mitschang | Sylvia Radeschütz

[1] Bernhard Mitschang,et al. Matching of Process Data and Operational Data for a Deep Business Analysis , 2008, IESA.

[2] Jiawei Han,et al. Data Mining: Concepts and Techniques , 2000 .

[3] Beate List,et al. Striving towards Near Real-Time Data Integration for Data Warehouses , 2002, DaWaK.

[4] H. Altay Güvenir,et al. An overview of regression techniques for knowledge discovery , 1999, The Knowledge Engineering Review.

[5] Boudewijn F. van Dongen,et al. Process Mining Framework for Software Processes , 2007, ICSP.

[6] Huan Liu,et al. Subspace clustering for high dimensional data: a review , 2004, SKDD.

[7] Ramakrishnan Srikant,et al. Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[8] Dimitrios Gunopulos,et al. Mining Process Models from Workflow Logs , 1998, EDBT.

[9] Fabio Casati,et al. A Generic solution for Warehousing Business Process Data , 2007, VLDB.

[10] Fabio Casati,et al. Business Process Cockpit , 2002, VLDB.

[11] John F. Roddick,et al. CURIO: A Fast Outlier and Outlier Cluster Detection Algorithm for Large Datasets , 2007, AIDM.

[12] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.