Comparative Analysis of Different Tools Business Process Simulation

Business process modelling is an increasingly popular research area for both organisations and enterprises due to its usefulness in facilitating better planning of resources, business reengineering and optimized business performance. The modelling and simulation of Business Processes has been able to show Business Analysts, and Managers where bottleneck exists in the system, how to optimize the Business Process to reduce cost of running the Organization, and the required resources needed for an Organization An important part of the evaluation of designed and redesigned business processes is Business Process Simulation (BPS). Although an abundance of simulation tools exist, the applicability of these tools is diverse. In this paper we thrash out a number of simulation tools that are applicable for the BPM field, we estimate their applicability for BPS and formulate recommendations for further research. This paper is limited to analysis three tools that is IBM WebSphere, FLOWer and FileNet (process management); and Arena and CPN Tools (discrete event simulation)) are compared based on the capabilities of modelling, support of simulation and output analysis

Semantic process mining tools: core building blocks

Process mining aims at discovering new knowledge based on information hidden in event logs. Two important enablers for such analysis are powerful process mining techniques and the omnipresence of event logs in today's information systems. Most information systems supporting (structured) business processes (e.g. ERP, CRM, and workflow systems) record events in some form (e.g. transaction logs, audit trails, and database tables). Process mining techniques use event logs for all kinds of analysis, e.g., auditing, performance analysis, process discovery, etc. Although current process mining techniques/tools are quite mature, the analysis they support is somewhat limited because it is purely based on labels in logs. This means that these techniques cannot benefit from the actual semantics behind these labels which could cater for more accurate and robust analysis techniques. Existing analysis techniques are purely syntax oriented, i.e., much time is spent on filtering, translating, interpreting, and modifying event logs given a particular question. This paper presents the core building blocks necessary to enable semantic process mining techniques/tools. Although the approach is highly generic, we focus on a particular process mining technique and show how this technique can be extended and implemented in the ProM framework tool

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

On the verification of EPCs using T-invariants

To verify a (business) process model, for example expressed in terms of an Event-driven Process Chain (EPC), most of the approaches described in literature require the construction of its state space. Unfortunately, for complex business processes the state space can be extremely large (if at all finite) and, as a result, constructing the state space may require excessive time. Moreover, semi-formal modeling languages such as the EPC language require a rather lenient interpretation of their semantics. To circumvent both the state-explosion problem and the semantics-related problems of EPCs, we propose an alternative approach based on transition invariants (T-invariants). T-invariants are well-known in the Petri-net community. They do not require the construction of the state space and can be computed efficiently. Moreover, we will show that our interpretation of T-invariants in this context can be used to deal effectively with the semantics-related problems of EPCs. To demonstrate our approach we will use two case studies: one is based on the reference model of SAP R/3 while the other one is based on a trade execution process within a large Dutch bank. We will also argue that the approach can be applied to other (informal or formal) modeling techniques

On the verification of EPCs using T-invariants

To verify a (business) process model, for example expressed in terms of an Event-driven Process Chain (EPC), most of the approaches described in literature require the construction of its state space. Unfortunately, for complex business processes the state space can be extremely large (if at all finite) and, as a result, constructing the state space may require excessive time. Moreover, semi-formal modeling languages such as the EPC language require a rather lenient interpretation of their semantics. To circumvent both the state-explosion problem and the semantics-related problems of EPCs, we propose an alternative approach based on transition invariants (T-invariants). T-invariants are well-known in the Petri-net community. They do not require the construction of the state space and can be computed efficiently. Moreover, we will show that our interpretation of T-invariants in this context can be used to deal effectively with the semantics-related problems of EPCs. To demonstrate our approach we will use two case studies: one is based on the reference model of SAP R/3 while the other one is based on a trade execution process within a large Dutch bank. We will also argue that the approach can be applied to other (informal or formal) modeling techniques

A SOA-based architecture framework

We present an Service-Oriented Architecture (SOA)– based architecture framework. The architecture framework is designed to be close to industry standards, especially to the Service Component Architecture (SCA). The framework is language independent and the building blocks of each system, activities and data, are first class citizens. We present a meta model of the architecture framework and discuss its concepts in detail. Through the framework, concepts of an SOA such as wiring, correlation and instantiation can be clarifie

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