What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, 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, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours 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 business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

Design-time Models for Resiliency

Resiliency in process-aware information systems is based on the availability of recovery flows and alternative data for coping with missing data. In this paper, we discuss an approach to process and information modeling to support the specification of recovery flows and alternative data. In particular, we focus on processes using sensor data from different sources. The proposed model can be adopted to specify resiliency levels of information systems, based on event-based and temporal constraints

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

Context-Aware Querying and Injection of Process Fragments in Process-Aware Information Systems

Cyber-physical systems (CPS) are often customized to meet customer needs and, hence, exhibit a large number of hard-/software configuration variants. Consequently, the processes deployed on a CPS need to be configured to the respective CPS variant. This includes both configuration at design time (i.e., before deploying the implemented processes on the CPS) and runtime configuration taking the current context of the CPS into account. Such runtime process configuration is by far not trivial, e.g., alternative process fragments may have to be selected at certain points during process execution of which one fragment is then dynamically applied to the process at hand. Contemporary approaches focus on the design time configuration of processes, while neglecting runtime configuration to cope with process variability. In this paper, a generic approach enabling context-aware process configuration at runtime is presented. With the Process Query Language process fragments can be flexibly selected from a process repository, and then be dynamically injected into running process instances depending on the respective contextual situations. The latter can be automatically derived from context factors, e.g., sensor data or configuration parameters of the given CPS. Altogether, the presented approach allows for a flexible configuration and late composition of process instances at runtime, as required in many application domains and scenarios

Towards Run-time Flexibility for Process Families: Open Issues and Research Challenges

The increasing adoption of process-aware information systems and the high variability of business processes in practice have resulted in process model repositories with large collections of related process variants (i.e., process families). Existing approaches for variability management focus on the modeling and configuration of process variants. However, case studies have shown that run-time configuration and re-confifiguration as well as the evolution of process variants are essential as well. Effectively handling process variants in these lifecycle phases requires deferring certain configuration decisions to the run-time, dynamically re-configuring process variants in response to contextual changes, adapting process variants to emerging needs, and evolving process families over time. In this paper, we characterize these flexibility needs for process families, discuss fundamental challenges to be tackled, and provide an overview of existing proposals made in this context