Future Data and Security Engineering by Tran Khanh Dang Roland Wagner Josef Küng Nam Thoai Makoto Takizawa & Erich Neuhold

Author:Tran Khanh Dang, Roland Wagner, Josef Küng, Nam Thoai, Makoto Takizawa & Erich Neuhold
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

Keywords

Sensible processesProcess storiesBusiness process modellingBusiness process management

1 Introduction

Business Process Management (BPM) has evolved towards a mature discipline concerned with the transformation of business goals, rules, processes, and practices into electronic services. Built on top of a variety of enterprise software and infrastructural components such as workflow engines, enterprise resource planning, service-oriented architectures and information repositories, BPM has provided broad facilities to manage business processes, which potentially increase productivity and reduce cost [1]. The typical BPM lifecycle includes eliciting and analysing process-related information, designing process models using specialised tools and languages, enacting process rules in enterprise systems, and executing/maintaining the services [2].

According to this lifecycle, the success of a BPM initiative starts with good elicitation, analysis and design, so that when reaching the enactment stage, the electronic services will effectively deliver the envisaged business goals. Of course, ensuring success is relatively easy in the case of purely automated systems, since their scope is well delimited, workflows are known, and procedures are always applicable. In these systems, systematic and preventive verifications of the relationships between process models and actual data processing usually ensure that services can be continuously provided within the required service-level agreements. Furthermore, exceptions in purely automated organisations tend to be expected exceptions, which can also be handled by pre-programmed instructions [3, 4].

Though the situation becomes much more challenging in areas where service provision involves a mix between humans and machines. Example areas include healthcare and customer relationship management, where human discretion is often necessary to resolve unique business cases [5]. In these areas, BPM needs to coordinate human decisions and automatic processes, which challenges the concept of purely automated system. Underlying these challenges, we find the different capabilities and constraints of humans and machines, e.g., machines can process more symbolic information in parallel and humans have more capacity for processing perceptual information [6]. Furthermore, humans have more capacity for recognising and interpreting context, making decisions with information gaps, and accommodating and improvising [7, 8].

Additionally, the BPM discipline must consider a business reality characterised by ever changing business contexts and goals, diverse clients’ needs, unexpected events, and emergent human behaviour. In such scenario, BPM experts may have to carefully consider the risks and consequences of mismatched process models and enacted operations, a problem that has been generally coined the “model reality divide” [9, 10], which is ultimately related with other problems predating BPM technology like the “lack of realism” (when rules do not exactly apply to the situation), “lack of details” (when precise rules about the situation are missing), and “lost in translation” (when rules have been erroneously converted to machine language) [11]. All these problems underline how difficult it is to integrate human and automated behaviour.

The BPM discipline has its roots in software engineering and computer science. Formal theory and methods such as Petri Nets, Pi-Calculus, and the Entity-Relationship and Relational models have been widely used to model data and processes [1]. Standards such as BPMN [12], UML [13], IDEF0 [14], BPEL [15], XPDL [16], and BPQL [17], just

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