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shutdown a faulty sensor) The article gives an example of a context-aware museum tour guide whose goal is to deliver information about exhibits in the language of each visitor The paper analyzes the information and meta-information content in this example The required information is the visitor's language, the exhibit the visitor is examining, and the exhibit's description The rst two are sensed context The paper presents the quality criteria of these context examples Schilit et al [75] describe systems that examine and react to an individual's changing context The investigation starts with the de nition of `mobile distributed computing system' and `context-aware systems' Four categories of context-aware applications are described: proximate selection, automatic contextual recon guration, contextual information and commands, and context-triggered actions Instances of these application types have been prototyped on the PARCTAB, a wireless palm-sized computer.

The Grid network community is developing methods that will enhance distributed environments by enabling a closer integration of communication services and other resources within the Grid environment. At the same time, the more general network research and development community continues to create new architectures, methods, and technologies that will enhance the quality of services at all traditional network layers. Almost all of these initiatives are, like Grid development efforts, providing core capabilities with higher levels of abstraction. Most current Grid development efforts are focused on leveraging these efforts to combine the best innovations of networking research and development with those of the Grid community. This chapter provides a brief introduction to several basic network service concepts, primarily standard network services at layers 1 through 4, as a prelude to discussions that will be the focus of much of the rest of this book. Later chapters describe architectural and technical details of the services at each of these layers.

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The aforementioned categories are the product of two points along two orthogonal dimensions: whether the task at hand is getting information or carrying out a command, and whether it is effected manually or automatically Proximate selection (get information, effected manually) involves a user interface technique where discovered objects that are nearby are emphasized or otherwise made easier to choose The discovered objects could be input and output devices that require physical interaction, for example printers or displays, nonphysical objects and services that are routinely accessed from particular locations, for example bank accounts or menus, or places about which one wants information, for example restaurants or stores In the above cases, location information can be used to weight the available choices Automatic Contextual Recon guration (get information, effected automatically): Recon guration is the process of adding new components, removing existing ones, or altering the connections between components.

Java skills come in at different levels: it is possible to be able to understand and manipulate JavaScript for writing simple Web applications, particularly on clients, but understanding how to use JavaBeans as part of a component architecture probably indicates a higher skill level, if only because the technology is newer and there are fewer people to ask when things go wrong. See also the middleware section for further discussion on Java server-based coding.

In the case of context-aware systems, the interesting aspect is how context of use might bring about different system con gurations and what these adaptations are In the case that the context of use is location for example, one possible application is related to the use of the PARCTAB whiteboard, a multi-user drawing.

The idea underlying the application of INES to the vehicle database is very simple Since we are interested in causes of faults, a two-layered network is learned The top layer of this network contains attributes describing the vehicle configuration, whereas the bottom layer contains attributes describing possible vehicle faults This is illustrated in Figure 118 and Table 112 (Since real dependences and numbers are, of course, highly confidential, these figures show fictitious examples Any resemblance to actual dependences and numbers is purely coincidental2 ) Figure 118 shows a possible learned twolayered network, Table 112 the frequency distribution associated with the first of its subnets.

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Since in this example the fault rate for cars with an air conditioning system and an electrical sliding roof is considerably higher than that of cars without one or both of these items, we can conjecture that the increased consumption of electrical energy due to installed air conditioning and an electrical sliding roof is a cause of increased battery faults Of course, such a conjecture has to be substantiated by a technical analysis It should be noted that this approach is preferable to the alternative that naturally comes to mind first, namely learning, for instance, a decision tree for each of the faults The first reason is that faults (like fraudulent calls in the telecommunications application) are rare However, decision trees have to achieve a classification rate higher than 50% for a fault in a leaf node in order to actually predict a fault.

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