Marc Drobek

Conformance checking for BPMN-based process models

Measuring how well business process models conform to the execution of the process in reality is an important topic with many applications. While current conformance checking approaches are tailored to formal models such as Petri nets they lack support for domain-specific standards such as BPMN. In this paper we present two approaches for directly measuring the conformance of business process models based on BPMN elements and event logs. We define methods for extracting properties from such models that enable an easy comparison to event logs on a local level (i.e. for individual parts of the process and individual events). Furthermore, we present a method for replaying whole event logs on such models, allowing for a global conformance measure (i.e. on trace level). By utilising the previously extracted properties, we eliminate the need for expensive state-space exploration.

Evolutionary Computation Based Discovery of Hierarchical Business Process Models

Business process models that describe how the execution of work in a business is structured are an important asset of modern enterprises. They serve as documentation, and, if easily understandable, allow process stakeholders to make better decisions on the business process. Traditionally, these models have been created manually after analyzing the process, which can lead to outdated information when changes are introduced into the process. Today, information systems connected to the business processes log event data reflecting the real execution of the processes, and process discovery techniques have been developed to automatically extract models from these event logs. Most of these techniques discover well formalized models such as Petri nets, which can be hard to understand in case of larger process models. The evolutionary computation based approach presented in this paper discovers process models complying to the specification of BPMN, one of the most used but not well formalized notations for documenting business processes. Our approach limits the set of possible process models to hierarchically structured models, and therefore facilitates well structured and simple results. An evaluation with eight event logs shows that, despite the limitation to well structured and simple models, the approach delivers competitive results when compared with other process discovery techniques.

On Advanced Business Simulations-Converging operational and strategic levels

Business Dynamics (BD) enables strategic Key Performance Indicator (KPI) predictions to monitor the health status of companies and support the decision making process. Nevertheless, a very important factor, which is generally overlooked, is that the top level strategic KPIs are highly influenced by the operational level business processes. These two domains are, however, mostly segregated and examined as silos with different solutions. In this paper, we are proposing a framework for advanced business simulations, which converges the two domains by utilising Ontologies and process execution data. Establishing this connection enables drilling down from a high level KPI perspective into the underlying operational level details to discover hidden bottlenecks and pre-emptively apply corrective actions.

Automated Equation Formulation for Causal Loop Diagrams

The annotation of Business Dynamics models with parameters and equations, to simulate the system under study and further evaluate its simulation output, typically involves a lot of manual work. In this paper we present an approach for automated equation formulation of a given Causal Loop Diagram (CLD) and a set of associated time series with the help of neural network evolution (NEvo). NEvo enables the automated retrieval of surrogate equations for each quantity in the given CLD, hence it produces a fully annotated CLD that can be used for later simulations to predict future KPI development. In the end of the paper, we provide a detailed evaluation of NEvo on a business use-case to demonstrate its single step prediction capabilities.

Introducing a Framework for Scalable Dynamic Process Discovery

Businesses are becoming increasingly globally interconnected and need to continuously adapt to global market changes and trends in order to stay competitive. Business processes are fundamental parts and drivers of these globally connected organizations which is why their management, analysis, and optimization are of utmost importance. Discovering and understanding the actual execution flow of processes deployed in your organization is an important enabler for these tasks. However, this has become increasingly difficult since business processes are now mostly distributed over different systems, highly dynamic, and may produce thousands of events per second which may conform to a number of different formats. These particular challenges are currently not specifically accounted for in the research field of Process Discovery. In order to address these challenges, this paper presents a concept for scalable dynamic process discovery, which is a scalable solution for identifying and keeping up with the evolution of dynamic, collaborative business processes. Furthermore, a framework for this concept is proposed along with the requirements and implementation details for the involved components and models.

Diversity Guided Evolutionary Mining of Hierarchical Process Models

Easy-to-understand and up-to-date models of business processes are important for enterprises, as they aim to describe how work is executed in reality and provide a starting point for process analysis and optimization. With an increasing amount of event data logged by information systems today, the automatic discovery of process models from process logs has become possible. Whereas most existing techniques focus on the discovery of well-formalized models (e.g. Petri nets) which are popular among researchers, business analysts prefer business domain-specific models (such as Business Process Model Notation, BPMN) which are not well formally specified. We present and evaluate an approach for discovering the latter type of process models by formally specifying a hierarchical view on business process models and applying an evolution strategy on it. The evolution strategy efficiently finds process models which best represent a given event log by using fast methods for process model conformance checking, and is partly guided by the diversity of the process model population. The approach contributes to the field of evolutionary algorithms by showing that they can be successfully applied in the real-world use case of process discovery, and contributes to the process discovery domain by providing a promising alternative to existing methods.

Advanced Business Simulations-Incorporating business and process execution data

In recent years, the artifact-centric approach to process mod- eling has attracted a lot of attention. One of the research lines in this area is finding a suitable way to represent the dimensions in this approach. Bearing this in mind, this paper proposes a way to specify artifact-centric business process models by means of well-known UML diagrams, from a high-level of abstraction and with a technology-independent perspective. UML is a graphical language, widely used and with a precise semantics.Service-oriented cloud-based web and mobile applications have placed new expectations and demands on software architectural design. In Maciaszek et al. (2014) we proposed a newmeta-architecture as a reference model for developing such applications. The seven-layer meta-architecture is called STCBMER (Smart Client Template Controller Bean Mediator Entity Resource). This paper concentrates on the description of principles that guide architects of specific service cloud applications that aim at conforming to STCBMER or similar meta-architectures. The principles are derived from a predecessor meta-architecture called PCBMER (Presentation Controller Bean Mediator Entity Resource) and are extended based on a comparative evaluation of principles in two other meta-architectures SANTA (Solution Architecture for N-Tier Applications) and MAAG (Microsoft Application Architecture Guide).

Advanced Business Simulations

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless, a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution&business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.