David Redlich

Mechanisms for Leveraging Models at Runtime in Self-adaptive Software

Modern software systems are often required to adapt their behavior at runtime in order to maintain or enhance their utility in dynamic environments. Models at runtime research aims to provide suitable abstractions, techniques, and tools to manage the complexity of adapting software systems at runtime. In this chapter, we discuss challenges associated with developing mechanisms that leverage models at runtime to support runtime software adaptation. Specifically, we discuss challenges associated with developing effective mechanisms for supervising running systems, reasoning about and planning adaptations, maintaining consistency among multiple runtime models, and maintaining fidelity of runtime models with respect to the running system and its environment. We discuss related problems and state-of-the-art mechanisms, and identify open research challenges.

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.

Constructs Competition Miner: Process Control-Flow Discovery of BP-Domain Constructs

Process Discovery techniques help a business analyst to understand the actual processes deployed in an organization, i.e. based on a log of events, the actual activity workflow is discovered. In most cases their results conform to general purpose representations like Petri nets or Causal nets which are preferred by academic scholars but difficult to comprehend for business analysts. In this paper we propose an algorithm that follows a top-down approach to directly mine a process model which consists of common BP-domain constructs and represents the main behaviour of the process. The algorithm is designed so it can deal with noise and not-supported behaviour. This is achieved by letting the different supported constructs compete with each other for the most suitable solution from top to bottom using ”soft” constraints and behaviour approximations. The key parts of the algorithm are formally described and evaluation results are presented and discussed.

Event-Driven Process-Centric Performance Prediction via Simulation

Today’s fast, competitive and extremely volatile markets exert a great deal of pressure on businesses to react quicker against the changes, and sometimes even before the changes actually happen. A late action can potentially result in a legal compliance failure or violation of service level agreements (SLA’s). A business analyst needs to be notified before these failures and violations occur. This paper proposes an approach that enables real-time and process-centric decision support in the form of performance prediction as an application of Event-Driven Business Process Management (EDBPM). The ability of simulations to produce future-events, which are of the same type like the live-events generated by the really executed business process, is utilised. Live-events and simulated future-events can therefore be treated by a Complex-Event Processing (CEP) engine in the same way and parameters representing the historic, current, and future performance of the business process can be easily computed.

Research Challenges for Business Process Models at Run-Time

Today’s fast and competitive markets require businesses to react faster to changes in its environment, and sometimes even before the changes actually happen. Changes can occur on almost every level, e.g. change in demand of customers, change of law, or change of the corporate strategy. Not adapting to these changes can result in financial and legal consequences for any business organisation. IT-controlled business processes are essential parts of modern organisations which motivates why business processes are required to efficiently adapt to these changes in a quick and flexible way. This requirement suggests a more dynamic handling of business processes and their models, moving from design-time business process models to run-time business process models. One general approach to address this problem is provided by the community of models@run.time, in which models reflect the system’s current state at any point in time and allow immediate reasoning and adaptation mechanisms. This paper examines the potential role of business process models at run-time by: (1) discussing the state-of the art of both, business process modelling and models@run.time, (2) reflecting on the nature of business processes at run-time, and (3) most importantly, highlighting key research challenges that need addressing to make this step.

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.

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).