Nir Mashkif

Pluggable Analysis Viewpoints for Design Space Exploration

Abstract 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 interdependences, the 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 in order to find feasible designs and to 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 paper we suggest (1) a unique modular algebraic viewpoint representation robust to design evolution and suitable for generation of the integrated optimization/analysis models, and (2) an underlying ontology-based approach for consistent integration of local viewpoint concepts into the unified design space model. We show an example of an optimization model with different combinations of partially interdependent Analysis Viewpoints. Using the proposed modeling and information management approaches the underlying viewpoints equations can be applied without modification, making the approach pluggable.

Wearable-based Mobile App for Decision Making: The Case of a Safe Workplace

In this paper, we present the concept for the Decision Compass mobile app, an application based on wearable devices meant to enforce safety in the workplace. We illus-trate how data gathered by wearable devices can identify situations of cognitive-dissonance and may be combined with additional contextual information to identify safety related hazards. Our application is designed not only to alert on safety hazards but also to provide resolutions in cases of explicit regulation violations. Finally, we describe future directions to add customized resolutions based on gradual learning of the individual preferences.

Reusable Derivation of Operational Metrics for Architectural Optimization

Abstract Maintaining coherence between system functional, performance, production and operational requirements is a key to the ability to optimize the design of large-scale systems. Different architectural configurations entail significant differences in functionality, performance, ease of manufacturing/assembly and operational behavior. While the first two are the usual concerns in architectural tradeoff analysis, the last two, reflected by manufacturability and operational metrics, such as manufacturability and affordability, are often neglected in architectural optimization. In this work, we propose a methodology to derive the formal specification of operational metrics applicable to design optimization based on life cycle processes, such as “manufacturing”, “sunny day operation”, and “unplanned maintenance”. These operational metrics are presented in the context of an industrial case study for an Unmanned Underwater Vehicle (UUV) to provide context for the recommended approach. We suggest an approach to (1) define libraries of reusable operational metrics based on architectural properties, (2) build reusable data processing patterns to calculate these architectural properties, and (3) map calculated architectural parameters to a specific design model.