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Business Process Management (BPM) is a central element of today’s organizations. Over the years, its main focus has been the support of business processes (BPs) in highly controlled domains. However—in the current era of Big Data and Internet-of-Things—several real-world domains are becoming cyber-physical (e.g., consider the shift from traditional manufacturing to Industry 4.0), characterized by ever-changing requirements, unpredictable environments and increasing amounts of data and events that influence the enactment of BPs. In such unconstrained settings, BPM professionals lack the needed knowledge to model all possible BP variants/contingencies at the outset. Consequently, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents). In this context, automated planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviors in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for solving concrete problems in the BPM field that were previously tackled with hard-coded solutions. To this aim, we first propose a methodology that shows how a researcher/practitioner should approach the task of encoding a concrete problem as an appropriate planning problem. Then, we discuss the required steps to integrate the planning technology in BPM environments. Finally, we show some concrete examples of the successful application of planning techniques to the different stages of the BPM life cycle.

The increasing application of process-oriented approaches in new challenging dynamic domains beyond business computing (e.g., healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex knowledge-intensive processes in such domains. A knowledge-intensive process is influenced by user decision making and coupled with contextual data and knowledge production, and involves performing complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and knowledge-intensive processes must be robust to unexpected conditions and adaptable to unanticipated exceptions, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. To tackle this issue, in this paper we present SmartPM, a model and a prototype Process Management System featuring a set of techniques providing support for automated adaptation of knowledge-intensive processes at runtime. Such techniques are able to automatically adapt process instances when unanticipated exceptions occur, without explicitly defining policies to recover from exceptions and without the intervention of domain experts at runtime, aiming at reducing error-prone and costly manual ad-hoc changes, and thus at relieving users from complex adaptations tasks. To accomplish this, we make use of well-established techniques and frameworks from Artificial Intelligence, such as situation calculus, IndiGolog and classical planning. The approach, which is backed by a formal model, has been implemented and validated with a case study based on real knowledge-intensive processes coming from an emergency management domain.

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements.

In complex emergency/disaster scenarios, persons from teams from various emergency-response organizations collaborate to achieve a common goal. In these scenarios, the use of smart mobile devices and applications can improve the collaboration dynamically. The lack of basic interaction principles can be dangerous, as it could increase the level of disaster or can make the efforts ineffective. This paper examines the main results of the project WORKPAD finished in December 2009. WORKPAD worked on a two-level architecture to support rescue operators during emergency management. The use of a user-centered design methodology during the entire development cycle has guaranteed that the architecture and resulting system meet end-user requirements. The feasibility of its use in real emergencies is also proven by a demonstration showcased with real operators. The paper includes qualitative and quantitative results and presents guidelines that can be useful in developing emergency-management systems.