Hybrid process modelling within business process management projects

Business Process Management (BPM) is still an important research topic amongst both academics and businesses. The recent recession has forced businesses to focus on cost control and efficiency in order to better cope with the economic downturn. Many companies in this situation turn to BPM software as a means of improving their efficiency and costs by reducing aspects of the business such as process lead-times and material costs. In order to identify areas of the business and its processes which require changing the business will most likely adopt a method of modelling their business processes. Because of the large number of available techniques decision makers usually struggle to decide the best approach. Recent literature has also pointed out that prevalent modelling techniques are designed to serve one specific purpose and may not be capable of modelling the whole picture. The key relationship between the information systems and the human behaviour is one example of where existing techniques are biased towards opposite ends of the scale. This paper proposes the use of a hybrid modelling notation composed of multiple existing notations in order to bridge this. The hybrid notation was applied to a BPM project at a company in the construction industry and a case study conducted with its users

Towards Run-Time Verification of Compositions in the Web of Things using Complex Event Processing

Following the vision of the Internet of Things, physical world entities are integrated into virtual world things. Things are expected to become active participants in business and social processes. Then, the Internet of Things could benefit from the Web Service architecture like today’s Web does, so Future ser-vice-oriented Internet things will offer their functionality via service-enabled in-terfaces. In previous work, we demonstrated the need of considering the behav-iour of things to develop applications in a more rigorous way, and we proposed a lightweight model for representing such behaviour. Our methodology relies on the service-oriented paradigm and extends the DPWS profile to specify the order with which things can receive messages. We also proposed a static verifi-cation technique to check whether a mashup of things respects the behaviour, specified at design-time, of the composed things. However, a change in the be-haviour of a thing may cause that some compositions do not fulfill its behaviour anymore. Moreover, given that a thing can receive requests from instances of different mashups at run-time, these requests could violate the behaviour of that thing, even though each mashup fulfills such behaviour, due to the change of state of the thing. To address these issues, we present a proposal based on me-diation techniques and complex event processing to detect and inhibit invalid invocations, so things only receive requests compatible with their behaviour.Work partially supported by projects TIN2008-05932, TIN2012-35669, CSD2007-0004 funded by Spanish Ministry MINECO and FEDER; P11-TIC-7659 funded by Andalusian Government; and Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tec

Distributed aspect-oriented service composition for business compliance governance with public service processes

Service-Oriented Architecture (SOA) offers a technical foundation for Enterprise Application Integration and business collaboration through service-based business components. With increasing process outsourcing and cloud computing, enterprises need process-level integration and collaboration (process-oriented) to quickly launch new business processes for new customers and products. However, business processes that cross organisations’ compliance regulation boundaries are still unaddressed. We introduce a distributed aspect-oriented service composition approach, which enables multiple process clients hot-plugging their business compliance models (business rules, fault handling policy, and execution monitor) to BPEL business processes

Refactoring Process Models in Large Process Repositories.

With the increasing adoption of process-aware information systems (PAIS), large process model repositories have emerged. Over time respective models have to be re-aligned to the real-world business processes through customization or adaptation. This bears the risk that model redundancies are introduced and complexity is increased. If no continuous investment is made in keeping models simple, changes are becoming increasingly costly and error-prone. Though refactoring techniques are widely used in software engineering to address related problems, this does not yet constitute state-of-the art in business process management. Process designers either have to refactor process models by hand or cannot apply respective techniques at all. This paper proposes a set of behaviour-preserving techniques for refactoring large process repositories. This enables process designers to eectively deal with model complexity by making process models better understandable and easier to maintain

Predictive Monitoring of Business Processes

Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business goals during business process execution. At any point during an execution of a process, the user can define business goals in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business goal. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business goals. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital