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A number of possible enhancements to QuizRDF are in progress or under consideration and we brie y mention three of them here. As we have seen, the resources returned from QuizRDF can be clustered based on their types (i.e. classes of which they are an instance). The proposal is that each resulting cluster can then be scored by combining some score of the individual WWW resources contained therein. Currently, as mentioned above, QuizRDF uses a variation of the well-understood tf.idf scoring scheme to rank WWW resources against a user query (Harman et al., 1992). In this way, each resource type (ontological class) can be ranked by relevance to the user query and results can be presented aggregated around resource types. Currently, we are developing a text-based way of presenting these results but there are clearly opportunities to exploit a graphical interface here also. A second area of ongoing work stems from an obvious limitation of QuizRDF as currently implemented: namely, queries can only be made around one class. To exemplify, we can ask the queries Find me all employees with last name Miller and Show me all instances of the class painter but we cannot (at least not in a single step) ask queries involving any chaining , for example: Find me all instances of class painting painted by an (instance of class) painter whose rst name is Pablo Allowing this possibility without comprising the simple and intuitive interface of QuizRDF is the subject of ongoing research. The third area of current work involves the implications of indexing large ontologies in QuizRDF. The issue is not one of scalability at the indexing

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In much the same way as there is no measure of darkness, there is no measure of quality You actually measure the lack of these things the amount of light or the volume of errors When trying to quantify quality liability you should bear in mind that not all bugs are equal If you have a colossal program with millions of lines of code and one Class A bug, the entire program is dead The damage that can be done by releasing it doesn t bear thinking about (as you will discover in the sidebar on the story of the Ariane 5 rocket later in this chapter) Software measurements polarize around two axes: characteristics and bugs Characteristics analyze the specification and structure of a program and provide measurements that enable you to compare your program with others.

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You, the network administrator, have control over a number of critical areas. The three main areas are easily identi ed according to the following high-level network administration areas: Applications. This area is one of the most important to put under strict change control. Users must in no way, shape, or form install and manage their own software. Security. To maintain a secure network, you need the authority to set security policy or to enforce the policy required by the business owners at both the servers and the workstations. Operating-system environment. Our modern operating systems, such as Windows 2000 and Windows XP, are extremely complex. Management and control of these systems and their myriad con guration needs to be rmly in the hands of experienced network technicians, support personnel, and administrators. These three areas merely encapsulate what amounts to thousands of possible management scenarios. We discuss each one in more detail in the following sections.

To achieve this it measures such attributes as code length, code complexity, and function Bugs measure the number and nature of defective parts and the rates at which they grow and dwindle So characteristics cover both estimated indicators and physically measurable attributes, while bugs measure the dysfunctional results of the coding Characteristics such as the number of lines of code do nothing more than alert you to the size of the issue They mean little in themselves There is little you can do about them other than rewrite the entire program You must be aware of them to put other characteristics in perspective Hitting a sign that says San Francisco 600 miles is not much use to you unless you also know which way to go.

2. 6.

Complexity is a much trickier yardstick, though we all know that it means the number of opportunities there are within a program for things to go wrong Complexity has been analyzed according to structures, algorithms, and cognitions Characteristics such as these give you insights into how to structure and write programs in simpler, more direct ways so your coding is less prone to errors..

1 Hence the reinterpretation of GSM from Groupe Speciale Mobile to Global System for Mobile communications.

AI 6 - Manage Changes Critical Success Factors Key Goal Indicators Key Performance Indicators Maturity Models

Figure 14.8 Continuation of the same patient as in Fig. 14.7. With ventricular pacing at a faster rate, the patient develops ventricular fibrillation and ultimately requires defibrillation (not shown).

(NP + MQ)/(N + M),

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