Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

From Design to Production Control Through the Integration of Engineering Data Management and Workflow Management Systems

At a time when many companies are under pressure to reduce "times-to-market" the management of product information from the early stages of design through assembly to manufacture and production has become increasingly important. Similarly in the construction of high energy physics devices the collection of (often evolving) engineering data is central to the subsequent physics analysis. Traditionally in industry design engineers have employed Engineering Data Management Systems (also called Product Data Management Systems) to coordinate and control access to documented versions of product designs. However, these systems provide control only at the collaborative design level and are seldom used beyond design. Workflow management systems, on the other hand, are employed in industry to coordinate and support the more complex and repeatable work processes of the production environment. Commercial workflow products cannot support the highly dynamic activities found both in the design stages of product development and in rapidly evolving workflow definitions. The integration of Product Data Management with Workflow Management can provide support for product development from initial CAD/CAM collaborative design through to the support and optimisation of production workflow activities. This paper investigates this integration and proposes a philosophy for the support of product data throughout the full development and production lifecycle and demonstrates its usefulness in the construction of CMS detectors.Comment: 18 pages, 13 figure

Modeling and Execution of Multienterprise Business Processes

We discuss a fully featured multienterprise business process plattform (ME-BPP) based on the concepts of agent-based business processes. Using the concepts of the subject-oriented business process (S-BPM) methodology we developed an architecture to realize a platform for the execution of distributed business processes. The platform is implemented based on cloud technology using commercial services. For our discussion we used the well known Service Interaction Patterns, as they are empirically developed from typical business-to-business interactions. We can demonstrate that all patterns can be easily modeled and executed based on our architecture. We propose therefore a change from a control flow based to an agent based view to model and enact business processes.Comment: arXiv admin note: substantial text overlap with arXiv:1404.273

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

Agent oriented modeling of business information systems

Enterprise modeling is an abstract definition of processes running in enterprise using process, value, data and resource models. There are two perspectives of business modeling: process perspective and value chain perspective. Both have some advantages and disadvantages. This paper proposes a combination of both perspectives into one generic model. The model takes also social part or the enterprise system into consideration and pays attention to disturbances influencing the enterprise system. Due to heterogeneous nature of the enterprise processes the outline proposed is oriented on the modeling using agents. Several types of agents are defined for the simulation model. The agents are structured into several layers. Well known JADE platform is proposed as the modeling framework. Further research directions are summed up in conclusionprocess model, value chain model, business pattern, agent, control loop, feedback, JADE platform.

Towards a service-oriented e-infrastructure for multidisciplinary environmental research

Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures