Understanding the operations of a large "net-centric system-of-systems" requires in-depth knowledge of the interfaces and the interactions among the various systems, subsystems, and components. Architectural modeling can help in reducing the complexity involved in designing large networked systems. This paper demonstrates a modeling approach for network centric systems. An example of such a complex system is the Global Earth Observation System of Systems (GEOSS)-a system for monitoring and collecting information related to Earth's resources. The GEOSS is an evolving complex network centric system. The modeling of the GEOSS has been demonstrated using the Systems Modeling Language (SysML). In this paper, 203 architecture representation using SysML demonstrates an object-oriented approach of model development. This paper discusses issues related to architecture description, development, presentation, and integration for the chosen domain. This paper also highlights some of the differences between SysML, which is used to model a wide range of systems, and Unified Modeling Language (UML), which is primarily used to model information systems only. Finally, in order to synthesize an executable model from the static views developed using SysML, Colored Petri-nets (CP-nets) have been used. The executable model, constructed using CP nets, is used to validate the architecture against the static model. Overall, this research defines a methodology to model and simulate complex network centric system of systems in order to understand and simulate their behavior using a scenario based approach.
Patient-centric healthcare and evidence-based medicine with the emphasis on prevention and wellness promise to deliver better and more affordable healthcare. At minimal, they require health related information to be shared among a community including patients, providers, payers, and regulators. It is important for IT systems to facilitate information sharing within such communities. Furthermore, we argue that it is highly valuable to develop IT technologies that can foster sustainable healthcare ecosystems for collaborative, coordinated healthcare delivery.The emerging cloud computing appears well-suited to meet the demand of a broad set of health service scenarios. In particular, the concept of shared infrastructure and services provides the foundation for supporting healthcare service ecosystems. This paper proposes an ecosystem approach to identify high-level requirements for cloud computing technologies to provide hosting environments for sustainable healthcare ecosystems. We draw the lessons and principles from the sustainable ecological ecosystems, review some of the existing IT-enabled healthcare ecosystems, and provide our view on the imperatives for cloud computing research to support future healthcare IT needs.
Offering personalized services through dynamically formed ecosystems is essential to personal well ness management. In this paper, we present the design of a cloud-enabled platform to facilitate the collection and delivery of evidence for personalization in a multi-provider ecosystem environment. In addition, the platform also provides essential building blocks of personalization services: smarter analytics for active personalization and dynamic provisioning. While the former common service takes charge of inferring user well ness risks from multiple data sources on the fly and making risk-driven recommendations, the latter common service determines optimal platform pricing and resource allocation given the constraint of acceptable quality of service.
PurposeThe need for studying the effects of cannibalization and its importance has been established in the literature, especially, since an assessment of the expected cannibalization effect of a new product can help in deciding on suitable times for new product introduction and promotions. However, quantitative measures that can be easily monitored and interpreted are not commonly available.Design/methodology/approachThis study uses parametric measures to help identify and investigate the effects of cannibalization. It proposes a predictive framework that may be used to investigate the effects of cannibalization. A case study, with real data from a consumer beverage company, illustrates the practical applicability of the model.FindingsThe parametric measures developed helped to identify the level of product cannibalization at the product, product group, family and brand levels in the portfolio.Originality/valueMarketing strategists who can identify the victims of cannibalization in the product portfolio will be better prepared for the effects of cannibalization.
Product platform concepts are often deployed to achieve product variety and hence effective product customization. One of the popular methods to achieve product variety is to scale one or more design variables called the scaling variable(s). This necessitates efficient methods for identifying the values for scaling variables. This paper presents a graph-based optimization method called Platform Ant Colony Optimization (PACO) for identifying the values of the scaling variable(s) for platform formation. In PACO, the overall decision is a function of the cumulative decisions of simple computing agents called the ‘ants.’ The method employs an autocatalytic mechanism using a probabilistic search to improve the solution iteratively. We use a universal electric motor example cited in the literature to test the efficiency of the proposed method. Simulation results on the example problem indicate that the PACO method produces promising results.
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