Sylvia Radeschütz

Business Process Optimization Using Formalized Optimization Patterns

The success of most of today’s businesses is tied to the efficiency and effectiveness of their core processes. Yet, two major challenges often prevent optimal processes: First, the analysis techniques applied during the optimization are inadequate and fail to include all relevant data sources. Second, the success depends on the abilities of the individual analysts to spot the right designs amongst a plethora of choices. Our deep Business Optimization Platform addresses these challenges through specialized data integration, analysis and optimization facilities. In this paper, we focus on how it uses formalized process optimization patterns for detecting and implementing process improvements.

BIAEditor : matching process and operational data for a business impact analysis

A profound analysis of all relevant business data in the company is necessary for optimizing business processes effectively. Current analyses typically exclusively run on business process execution data or on operational business data stored in a data warehouse. However, to achieve a more informative analysis and to fully optimize a companys business, a consolidation of all major business data sources is indispensable. Recent matching algorithms are insufficient for this task, since they are restricted either to schema or to process matching. Our demonstration presents BIAEditor that allows to annotate and match process variables and operational data models in order to perform such a global business impact analysis.

Matching of Process Data and Operational Data for a Deep Business Analysis

Efficient adaptation to new situations of a company’s business and its business processes plays an important role for achieving advantages in competition to other companies. For an optimization of processes, a profound analysis of all relevant information in the company is necessary. Analyses typically specialize either on process analysis or on data warehousing of operational data. A consolidation of major business data sources is needed to analyze and optimize processes in a much more comprehensive scope. This paper introduces a framework that offers various alternatives for matching process data and operational data to obtain a consolidated data description.

Design-Time Process Optimization through Optimization Patterns and Process Model Matching

The goal of process design is the construction of a process model that is a priori optimal w.r.t. the goal(s) of the business owning the process. Process design is therefore a major factor in determining the process performance and ultimately the success of a business. Despite this importance, the designed process is often less than optimal. This is due to two major challenges: First, since the design is an a priori ability, no actual execution data is available to provide the foundations for design decisions. Second, since modeling decision support is typically basic at best, the quality of the design largely depends on the ability of business analysts to make the ”right” design choices. To address these challenges, we present in this paper our deep Business Optimization Platform that enables (semi-) automated process optimization during process design based on actual execution data. Our platform 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.

Automated Process Decision Making Based on Integrated Source Data

Decision activities are frequently responsible for a major part of a process’s duration and resource consumption. The automation of these activities hence holds the promise of significant cost and time savings, however, only if the decision quality does not suffer. To achieve this, it is required to consider data from diverse sources that go beyond the process audit log, which is why approaches relying solely on it are likely to yield sub-optimal results. We therefore present in this paper an approach to process decision automation that incorporates data integration techniques, enabling significant improvements in decision quality.

Business impact analysis--a framework for a comprehensive analysis and optimization of business processes

The ability to continuously adapt its business processes is a crucial ability for any company in order to survive in today’s dynamic world. In order to accomplish this task, a company needs to profoundly analyze all its business data. This generates the need for data integration and analysis techniques that allow for a comprehensive analysis.A particular challenge when conducting this analysis is the integration of process data generated by workflow engines and operational data that is produced by business applications and stored in data warehouses. Typically, these two types of data are not matched as their acquisition and analysis follows different principles, i.e., a process-oriented view versus a view focusing on business objects.To address this challenge, we introduce a framework that allows to improve business processes considering an integrated view on process data and operational data. We present and evaluate various architectural options for the data warehouse that provides this integrated view based on a specialized federation layer. This integrated view is also reflected in a set of operators that we introduce. We show how these operators ease the definition of analysis queries and how they allow to extract hidden optimization patterns by using data mining techniques.

Exploiting the symbiotic aspects of process and operational data for optimizing business processes

A profound analysis of all relevant business data in a company is necessary for optimizing business processes effectively. Current analyses typically run either on business process execution data or on operational business data. Correlations among the separate data sets have to be found manually under big effort. However, to achieve a more informative analysis and to fully optimize a companys business, an efficient consolidation of all major data sources is indispensable. Recent matching algorithms are insufficient for this task since they are restricted either to schema or to process matching. We present a new matching framework to combine process data models and operational data models (semi-)automatically for performing such a profound business analysis. We describe this approach and its basic matching rules as well as an experimental study that shows the achieved high recall and precision.

A Combination Framework for Exploiting the Symbiotic Aspects of Process and Operational Data in Business Process Optimization

A profound analysis of all relevant business data in a company is necessary for optimizing business processes effectively. Current analyses typically run either on business process execution data or on operational business data. Correlations among the separate data sets have to be found manually under big effort. However, to achieve a more informative analysis and to fully optimize a company’s business, an efficient consolidation of all major data sources is indispensable. Recent matching algorithms are insufficient for this task since they are restricted either to schema or to process matching. We present a new matching framework to (semi-)automatically combine process data models and operational data models for performing such a profound business analysis. We describe the algorithms and basic matching rules underlying this approach as well as an experimental study that shows the achieved high recall and precision.