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Model-Based Systems Engineering Colloquium
Pluggable Analysis Viewpoints for Design Space Exploration
IBM Research – Haifa Lab
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Viewpoint modeling is an effective approach for analyzing and designing complex systems. Splitting various elements and corresponding constraints into different perspectives of interests, enables separation of concerns such as domains of expertise, levels of abstraction, and stages in lifecycle. Specifically, in Systems Engineering different viewpoints could include functional requirements, physical architecture, safety, geometry, timing, scenarios, etc. Despite partial interdependence, their models are usually developed independently by different parties, using different tools and languages. However, the essence of Systems Engineering requires repetitive integration of many viewpoints to find feasible designs and make good architectural decisions, e.g., in each mapping between consecutive levels of abstraction and in each design space exploration. This integration into one consistent model becomes a significant challenge from both modeling and information management perspectives. In this work we suggest (1) a unique modular algebraic viewpoint representation robust to design evolution and suitable for automatic generation of the integrated model, and (2) an ontology-based approach for consistent integration of local viewpoint concepts into the unified model. We show an example how an optimization model could be generated for different combinations of partially interdependent Analysis Viewpoints without having to modify the underlying viewpoints’ equations, making the approach pluggable, and suggest an information management infrastructure to support it.
Ph.D. 1998, M.Sc. 1992 (Industrial Engineering, Technion, Israel), M.Sc. 1987 (Mechanical Engineering, Moscow State University of Railway Transport, Russia). He is currently a Research Staff Member in Systems Engineering Technologies group at IBM Research – Haifa Lab (HRL) and has strong teaching and research ties to the Technion and Tel Aviv University. Michael’s research interests focus on the development of tools and applications for deterministic and stochastic combinatorial multi-objective optimization including (1) optimization-based engineering and system of systems design and (2) design, control, and integration of production, service, and logistics systems. Dr. Masin has published many papers in leading professional journals and conferences, filed 10 IBM patents, supervised many graduate students at the Technion and TAU, and been Technical Lead/Principal Investigator of projects, both with government and private customers, involving Model Based Simulation and Optimization of Complex Systems and System of Systems.
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