Issues in Process Variants Mining

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, will lead to a large number of process variants, which are created from the same original process model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease. Finally, we compare our approach with existing process mining techniques, and show that process variants mining is additionally needed to learn from process changes

Mining Process Variants: Goals and Issues (Short Paper)

Recently, Process-Aware Information Systems (PAIS) were introduced, which allow for dynamic process and service changes. This, in turn, has led to a large number of process model variants, which are difficult to maintain and expensive to configure. This paper deals with goals and issues related to the mining of process model variants. Our overall goal is to learn from process changes and to ”merge” the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease

Discovering Process Reference Models from Process Variants Using Clustering Techniques

In today's dynamic business world, success of an enterprise increasingly depends on its ability to react to changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, has led to large number of process and service variants derived from the same model, but differs in structures due to the applied changes. This paper provides a sophisticated approach which fosters learning from past process changes and allows for determining such process variants. As a result we obtain a generic process model for which the average distances between this model and the process variants becomes minimal. By adopting this generic process model in the PAIS, need for future process configuration and adaptation will decrease. The mining method proposed has been implemented in a powerful proof-of-concept prototype and further validated by a comparison between other process mining algorithms

On Measures of Behavioral Distance between Business Processes

The desire to compute similarities or distances between business processes arises in numerous situations such as when comparing business processes with reference models or when integrating business processes. The objective of this paper is to develop an approach for measuring the distance between Business Processes Models (BPM) based on the behavior of the business process only while abstracting from any structural aspects of the actual model. Furthermore, the measure allows for assigning more weight to parts of a process which are executed more frequently and can thus be considered as more important. This is achieved by defining a probability distribution on the behavior allowing the computation of distance metrics from the field of statistics

Representing Block-structured Process Models as Order Matrices: Basic Concepts, Formal Properties, Algorithms

In various cases we need to transform a process model into a matrix representation for further analysis. In this paper, we introduce the notion of Order Matrix, which enables unique representation of block-structured process models. We present algorithms for transforming a block-structured process model into a corresponding order matrix and vice verse. We then prove that such order matrix constitutes a unique representation of a block-structured process model; i.e., if we transform a process model into an order matrix, and then transform this matrix back into a process model, the two process models are trace equivalent; i.e., they show same behavior. Finally, we analyze algebraic properties of order matrices

Ähnlichkeitsbasierte Suche in Geschäftsprozessmodelldatenbanken

Die Wiederverwendung von Prozessmodellen bietet sich zur Reduzierung des hohen Modellierungsaufwands an. Allerdings ist das Auffinden von ähnlichen Modellen in großen Modellsammlungen manuell nicht effizient möglich. Hilfreich sind daher Suchmöglichkeiten nach relevanten Modellen, die als Vorlage zur Modellierung genutzt werden können. In dieser Arbeit werden Ansätze beschrieben, um innerhalb von Prozessmodellbibliotheken nach ähnlichen Modellen und Aktivitäten zu suchen

Graph-based Pattern Matching and Discovery for Process-centric Service Architecture Design and Integration

Process automation and applications integration initiatives are often complex and involve significant resources in large organisations. The increasing adoption of service-based architectures to solve integration problems and the widely accepted practice of utilising patterns as a medium to reuse design knowledge motivated the definition of this work. In this work a pattern-based framework and techniques providing automation and structure to address the process and application integration problem are proposed. The framework is a layered architecture providing modelling and traceability support to different abstraction layers of the integration problem. To define new services - building blocks of the integration solution - the framework includes techniques to identify process patterns in concrete process models. Graphs and graph morphisms provide a formal basis to represent patterns and their relation to models. A family of graph-based algorithms support automation during matching and discovery of patterns in layered process service models. The framework and techniques are demonstrated in a case study. The algorithms implementing the pattern matching and discovery techniques are investigated through a set of experiments from an empirical evaluation. Observations from conducted interviews to practitioners provide suggestions to enhance the proposed techniques and direct future work regarding analysis tasks in process integration initiatives