Method Families Concept: Application to Decision-Making Methods

International audienceThe role of variability in Software engineering grows increasingly as it allows developing solutions that can be easily adapted to a specific context and reusing existing knowledge. In order to deal with variability in the method engineering (ME) domain, we suggest applying the notion of method families. Method components are organized as a method family, which is configured in the given situation into a method line. In this paper, we motivate the concept of method families by comparing the existing approaches of ME. We detail then the concept of method families and illustrate it with a family of decision-making (DM) methods that we call MADISE

A software framework for risk-aware Business Process Management

With the large diffusion of Business Process Managemen (BPM) automation suites, the possibility of managing process-related risks arises. This paper introduces an innovative framework for process-related risk management and describes a working implementation realized by extending the YAWL system. The framework covers three aspects of risk management: risk monitoring, risk prevention, and risk mitigation. Risk monitoring functionality is provided using a sensor-based architecture, where sensors are defined at design time and used at run-time for monitoring purposes. Risk prevention functionality is provided in the form of suggestions about what should be executed, by who, and how, through the use of decision trees. Finally, risk mitigation functionality is provided as a sequence of remedial actions (e.g. reallocating, skipping, rolling back of a work item) that should be executed to restore the process to a normal situation

EDIminer : a toolset for process mining from EDI messages

Organizations exchange data electronically to perform business transactions using Electronic Data Interchange (EDI). In order to gain insights on such transactions, approaches for inter-organizational business process mining based on the observation of exchanged EDI messages have been recently proposed. In recent approaches, however, only meta-information about the exchanged messages, such as message type, interchange time and sender/receiver information, has been used as data base for generating event logs. This neglects the opportunity of using business information from observed EDI messages to arrive at more detailed event logs, which in turn enable mining of detailed process models and fine-grained process performance analyses. In addressing this shortcoming, we present EDIminer, a toolset that allows for (i) enhanced visualization of contents of EDI messages, (ii) automatic and/or user-driven definition of mappings of EDI artifacts to events, (iii) generation of events from such mappings, (iv) semi-automatic correlation of events to process instances and (v) generation of industry-standard XES event logs for subsequent application of conventional process mining techniques. We demonstrate the utility of EDIminer by means of an exemplary EDI-based purchase order process based on real-world data

On the Contextualization of Event-Activity Mappings

Postprint version of original article. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-11641-5_35Event log files are used as input to any process mining algorithm. A main assumption of process mining is that each event has been assigned to a distinct process activity already. However, such mapping of events to activities is a considerable challenge. The current status-quo is that approaches indicate only likelihoods of mappings, since there is often more than one possible solution. To increase the quality of event to activity mappings this paper derives a contextualization for event-activity mappings and argues for a stronger consideration of contextual factors. Based on a literature review, the paper provides a framework for classifying context factors for event-activity mappings. We aim to apply this framework to improve the accuracy of event-activity mappings and, thereby, process mining results in scenarios with low-level events.acceptedVersio

Situational Method Engineering for Constructing Internet of Things Development Methods

Developing Internet of Things (IoT) systems is not trivial and needs to be performed systematically to derive an IoT system that meets the required functional and non-functional concerns. Since IoT is applied to different heterogeneous domains usually a one-size-fits-all method is less feasible. For some cases a lightweight method with a few method artefacts are sufficient while in other cases a detailed set of method artefacts over the whole lifecycle might be required. So far, a few IoT system development methods (SDM) have been provided that include the steps necessary for guiding the development of IoT systems but these do not explicitly consider the situational needs for the required IoT method. In this paper we propose a situational method engineering (SME) approach for developing a method base that includes a broad set of method fragments which can be reused to develop customized methods. We illustrate the development of the method base using the existing IoT methods that have been proposed in the literature so far. Further we show how the method base can be used to develop methods for two different cases.</p

On the Contextualization of Event-Activity Mappings

Event log files are used as input to any process mining algorithm. A main assumption of process mining is that each event has been assigned to a distinct process activity already. However, such mapping of events to activities is a considerable challenge. The current status-quo is that approaches indicate only likelihoods of mappings, since there is often more than one possible solution. To increase the quality of event to activity mappings this paper derives a contextualization for event-activity mappings and argues for a stronger consideration of contextual factors. Based on a literature review, the paper provides a framework for classifying context factors for event-activity mappings. We aim to apply this framework to improve the accuracy of event-activity mappings and, thereby, process mining results in scenarios with low-level events