Open AccessProceedings Article
Extended Analysis Techniques for a Comprehensive Business Process Optimization.
Sylvia Radeschütz,Bernhard Mitschang +1 more
- pp 77-82
TLDR
An extended data warehouse approach is defined that integrates process-related data and operational business data that is used as the underlying data source for extended OLAP and data mining analysis techniques for a comprehensive business process optimization.Abstract:
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.read more
Citations
More filters
Book ChapterDOI
Business Process Optimization Using Formalized Optimization Patterns
TL;DR: This paper focuses on how the deep Business Optimization Platform uses formalized process optimization patterns for detecting and implementing process improvements.
Deep Business Optimization: A Platform for Automated Process Optimization.
TL;DR: This paper proposes a platform that enables (semi-)automated process optimization during the process design, execution and analysis stages, based on insights from specialized analytical procedures running on an integrated warehouse containing both process and operational data.
Proceedings ArticleDOI
Design-Time Process Optimization through Optimization Patterns and Process Model Matching
TL;DR: The deep Business Optimization Platform is presented, which enables (semi-) automated process optimization during process design based on actual execution data and achieves this task by matching new processes to existing processes stored in a repository based on similarity metrics and by using a set of formalized best-practice process optimization patterns.
Journal ArticleDOI
Business impact analysis--a framework for a comprehensive analysis and optimization of business processes
TL;DR: A framework that allows to improve business processes considering an integrated view on process data and operational data is introduced and various architectural options for the data warehouse that provides this integrated view based on a specialized federation layer are presented.
Book ChapterDOI
Automated Process Decision Making Based on Integrated Source Data
TL;DR: This paper presents an approach to process decision automation that incorporates data integration techniques, enabling significant improvements in decision quality.
References
More filters
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Journal ArticleDOI
Data clustering: a review
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Proceedings Article
Fast Algorithms for Mining Association Rules in Large Databases
Journal ArticleDOI
Subspace clustering for high dimensional data: a review
TL;DR: A survey of the various subspace clustering algorithms along with a hierarchy organizing the algorithms by their defining characteristics is presented, comparing the two main approaches using empirical scalability and accuracy tests and discussing some potential applications where sub space clustering could be particularly useful.
Journal Article
Mining process models from workflow logs
TL;DR: In this paper, the authors present an approach for a system that constructs process models from logs of past, unstructured executions of the given process, which conforms to the dependencies and put executions present in the log.