Context-aware systems are pervading everyday life, therefore context modeling is becoming a relevant issue and an expanding research field. This survey has the goal to provide a comprehensive evaluation framework, allowing application designers to compare context models with respect to a given target application; in particular we stress the analysis of those features which are relevant for the problem of data tailoring. The contribution of this paper is twofold: a general analysis framework for context models and an upto-date comparison of the most interesting, data-oriented approaches available in the literature.

Common to all actors in today's information world is the problem of lowering the "information noise," both reducing the amount of data to be stored and accessed, and enhancing the "precision" according to which the available data fit the application requirements

-As any engineering faculty member teaching undergraduates knows, students possess a wide variety of misconceptions about fundamental engineering concepts. In the thermal sciences, there are numerous misconceptions about heat, energy, and temperature; mechanics students hold misconceptions about inertia, angular velocity, and energy. This is complicated by the fact that we possess many years of everyday experiences with energy flows, forces, and kinematics. Due to previous experiences, it is often difficult to repair these misconceptions -simple classroom lecturing often fails to instill correct conceptual knowledge. In order to provide real-world context, we are developing Model-Eliciting Activities (MEAs) to help repair misconceptions in dynamics and the thermal sciences. An MEA is a clientdriven problem that requires student teams to develop an engineering model or procedure. This approach creates an environment where students value abilities beyond using the traditional prescribed equations and models. During this process, we hypothesize that rich discussion and model re-formulation will help students recognize and repair misconceptions, and that the real world context will help them remember these critical concepts.Index Terms -Misconceptions, model-eliciting activities, thermal sciences, mechanics. CONCEPTUAL UNDERSTANDING IN ENGINEERINGIf a large SUV hits a motorcycle, doesn't it make sense that the SUV exerts more force on the motorcycle than the motorcycle exerts on SUV? If a wooden spoon feels warmer than a metal spoon, does that mean it is at a higher temperature? These examples illustrate how everyday experiences help to form our conceptual (mis)understanding of science and engineering.Although engineering professors are often successful in teaching students how to choose and apply an appropriate equation, we are typically less successful at producing true conceptual understanding in our students. The problem is widespread through STEM disciplines, with nearly 7700 reported studies of student misconception in the literature [1]. Prior research by Streveler, Miller, and Olds in engineering student misconceptions of thermal science topics shows that seniorlevel chemical and mechanical engineering students retain a significant number of robust misconceptions even after completing courses in fluid mechanics, heat transfer, and thermodynamics [2]. Over 40% consistently cannot distinguish between the rate and amount of heat transfer between two bodies at different temperatures and approximately 50% cannot distinguish between the quantity and quality of energy as described by the second law of thermodynamics. Nearly 30% cannot logically distinguish between temperature and energy in simple engineering systems and processes. Clearly, we need to continue to develop reliable and valid methods for both identifying and repairing important misconceptions.Two challenges must be met to promote deep conceptual learning in the face of misconceptions. The first is to identify prevalent and robust student misco...

Abstract:More and more often, we face the necessity of extracting appropriately reshaped knowledge from an integrated representation of the information space. Be such a global representation a central database, a global view of several ones or an ontological representation of an information domain, we face the need to define personalised views for the knowledge stakeholders: single users, companies or applications. We propose exploiting the information usage context within a methodology for context-aware data design, where the notion of context is formally defined together with its role within the process of view building by information tailoring. This paper presents our context model, called the context dimension tree, which plays a fundamental role in tailoring the information space according to user information needs.

The design of very small databases for smart cards and for portable embedded systems is deeply constrained by the peculiar features of the physical medium. We propose a joint approach to the logical and physical database design phases and evaluate several data structures with respect to the performance, power consumption, and endurance parameters of read/program operations on the Flash-EEPROM storage medium.