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

What are the Problem Makers: Ranking Activities According to their Relevance for Process Changes

Recently, a new generation of adaptive process management technology has emerged, which enables dynamic changes of composite services and process models respectively. This, in turn, results in a large number of process variants derived from the same process model, but differing in structure due to the applied changes. Since such process variants are expensive to maintain, the process model should be evolved accordingly. In this context, we need to know which activities have been more often involved in process adaptations than others, such that we can focus on them when reconfiguring the process model. This paper provides two approaches for ranking activities according to their involvement in process adaptations. The first one allows to precisely rank the activities, but is expensive to perform since the algorithm is at NP level. We therefore provide as alternative an approximation ranking algorithm which computes in polynomial time. The performance of the approximation algorithm is evaluated and compared through a simulation of 3600 process models. Statistical significance tests indicate that the performance of the approximation ranking algorithm does not depend on the size of process models, i.e., our algorithm can scale up

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

A Heuristic Approach for Discovering Reference Models by Mining Process Model Variants

Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime while preserving PAIS robustness and consistency. Such flexibility, in turn, leads to a large number of process variants derived from the same model, but differing in structure. Generally, such variants are expensive to configure and maintain. This paper provides a heuristic search algorithm which fosters learning from past process changes by mining process variants. The algorithm discovers a reference model based on which the need for future process configuration and adaptation can be reduced. It additionally provides the flexibility to control the process evolution procedure, i.e., we can control to what degree the discovered reference model differs from the original one. As benefit, we can not only control the effort for updating the reference model, but also gain the flexibility to perform only the most important adaptations of the current reference model. Our mining algorithm is implemented and evaluated by a simulation using more than 7000 process models. Simulation results indicate strong performance and scalability of our algorithm even when facing large-sized process models

The labial gene is required to terminate proliferation of identified neuroblasts in postembryonic development of the Drosophila brain

The developing brain of Drosophila has become a useful model for studying the molecular genetic mechanisms that give rise to the complex neuronal arrays that characterize higher brains in other animals including mammals. Brain development in Drosophila begins during embryogenesis and continues during a subsequent postembryonic phase. During embryogenesis, the Hox gene labial is expressed in the developing tritocerebrum, and labial loss-of-function has been shown to be associated with a loss of regional neuronal identity and severe patterning defects in this part of the brain. However nothing is known about the expression and function of labial, or any other Hox gene, during the postembryonic phase of brain development, when the majority of the neurons in the adult brain are generated. Here we report the first analysis of Hox gene action during postembryonic brain development in Drosophila. We show that labial is initially expressed in six larval brain neuroblasts, of which only four give rise to the labial expressing neuroblast lineages present in the late larval brain. Although MARCM-based clonal mutation of labial in these four neuroblast lineages does not result in an obvious phenotype, a striking and unexpected effect of clonal labial loss-of-function does occur during postembryonic brain development, namely the formation of two ectopic neuroblast lineages that are not present in wild-type brains. The same two ectopic neuroblast lineages are also observed following cell death blockage and, significantly, in this case the resulting ectopic lineages are Labial-positive. These findings imply that labial is required in two specific neuroblast lineages of the wildtype brain for the appropriate termination of proliferation through programmed cell death. Our analysis of labial function reveals a novel cell autonomous role of this Hox gene in shaping the lineage architecture of the brain during postembryonic development

Towards an Intelligent Workflow Designer based on the Reuse of Workflow Patterns

In order to perform process-aware information systems we need sophisticated methods and concepts for designing and modeling processes. Recently, research on workflow patterns has emerged in order to increase the reuse of recurring workflow structures. However, current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. In this paper we gather a suite for both process modeling and normalization based on workflow patterns reuse. This suite must be used in the extension of some workflow design tool. The suite comprises components for the design of processes from both legacy systems and process modeling

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