Model Based Systems Engineering (MBSE) is an evolving practice in the early stages of adoption similar to the mechanical, electrical and software domains 20 to 30 years ago. Today there is increasing recognition of the potential MBSE brings to system life cycle processes with the increasing complexity of systems and the demands of the global marketplace. In order for the practice to realize this potential, system modeling and MBSE must be part of the larger model based engineering effort, and integrate with other engineering discipline models and modeling activities across the life cycle of a system. This is placing increasing demands on the need for Model Lifecycle Management (MLM) as an essential part of an MBSE infrastructure. This paper establishes the motivation for MLM, as well as laying the foundation for addressing challenges that lay ahead. The paper is focused on describing key concepts, requirements, current practices, and future directions of MLM, and setting the basis for more in depth overview of MLM solutions and vendor offering that are beyond the scope of this paper. Motivation -Model Lifecycle Management as an Enabler of Model-Based Systems Engineering (MBSE)Smarter and more complex products enter our lives every day. The modern society in the 21th century is more dependent than ever on such systems that serve our basic needs for health, communication, transportation, financial management, education, entertainment and much, much more. These smarter products today are not independent. They usually consist of collections of other constituent systems, and often dependent on the behavior of external systems. Products are more and more autonomous, capable of optimizing their operation and perform goal-seeking behaviors. Moreover, we witness the growing importance of smarter, cyber-physical systems that combine software, hardware, mechanical and electrical components. Ever increasing demands on system performance is driving tighter integration of the engineering disciplines to provide this performance. This convergence of engineering disciplines, as well as growing business challenges such as shorter time to market, strict safety requirements, higher product quality and stricter regulatory compliance increase the need for new and holistic system approaches and methodologies that support system design. These factors have led to the the engineering domain innovators to modeling, abstraction and multi-disciplinary

e-Commerce companies acknowledge that customers are their most important asset and that it is imperative to estimate the potential value of this asset.In conventional marketing, one of the widely accepted methods for evaluating customer value uses models known as Customer Lifetime Value (CLV). However, these existing models suffer from two major shortcomings: They either do not take into account significant attributes of customer behavior unique to e-Commerce, or they do not provide a method for generating specific models from the large body of relevant historical data that can be easily collected in e-Commerce sites.This paper describes a general modeling approach, based on Markov Chain Models, for calculating customer value in the e-Commerce domain. This approach extends existing CLV models, by taking into account a new set of variables required for evaluating customers value in an e-Commerce environment. In addition, we describe how data-mining algorithms can aid in deriving such a model, thereby taking advantage of the historical customer data available in such environments. We then present an application of this modeling approach-the creation of a model for online auctions-one of the fastest-growing and most lucrative types of e-Commerce. The article also describes a case study, which demonstrates how our model provides more accurate predictions than existing conventional CLV models regarding the future income generated by customers.

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Before requirements analysis takes place in a business context, business analysis is usually performed. Important concerns emerge during this analysis that need to be captured and communicated to requirements engineers. In this paper, we take the position that tagging is a promising approach for identifying and organizing these concerns. The fact that tags can be attached freely to entities, often with multiple tags attached to the same entity and the same tag attached to multiple entities, leads to multi-dimensional structures that are suitable for representing crosscutting concerns and exploring their relationships. The resulting tag structures can be hardened into classifications that capture and communicate important concerns.