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Although very super cial descriptions, these items do capture something of the essence of the development. The latter three give rise to a qualitatively different dynamic nature in the Web than what we are currently used to. Addressing some Practical Features Let us put the abstract vision into a few simple possible contexts by recalling some of the de ciencies and desired features discussed in 1 and seeing what a Semantic Web infrastructure could add in practice to the personal user experience. User-context tracking. Imagine that your Web client could (if you want it to) track your semantic context. For example, it might pre- lter search results to show only those hits with a high correlation to the meaning implied by previous contexts. Imagine further that meaningfully related search terms were automatically generated to catch resources that would otherwise be missed only because a particular search word did not occur or was spelled differently in the target. When browsing, the client might always highlight Web links that lead to resources with a high semantic correlation to the current context, based on background metadata processing. It might even suggest links not mentioned in the displayed content, and provide pop-up comments on demand.

When you make statistical measurements, use the data to establish a baseline, to tune your understanding of the network, and to focus troubleshooting efforts For example, if you assumed that File Server 1 was the most active station, but it turns out that File Server 2 is the most active, then you can avoid wasting time and money trying to optimize and troubleshoot the wrong machine If it turns out that User 22 is actually the most active station, then perhaps you have a problem Users may be playing graphics-intensive network games, downloading music or videos, or testing a new nonoptimized piece of software that probably should be tested in the lab first rather than on your operational network Analyze the statistics you gather with respect to the network maps and device configurations you also gathered.

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In fact, in some ways I m glad that he was the one that did get caught if any of us were going to because I think he was the most equipped to handle that situation For all I know he had handled things like back in Eastern Europe He exhibited some loyalty and did not give us up He didn t talk about any partners or anything like that He was nervous and upset but he was tough under fire and basically said he was working alone He said, Look, am I under arrest, are you guys police, what s the deal It s a law enforcement type of interrogation except that they re not police and don t have any real authority, which is kind of weird They kept on questioning him, but they didn t exactly manhandle him..

Understanding the structure of your network and the architecture of devices within the network can help you make sense of the statistics For example, if you place a 10-Mbps Ethernet protocol analyzer on one port of a switch and configure the switch to mirror traffic for multiple 10-Mbps ports, don't be surprised if the analyzer reports that utilization is 100 percent The analyzer is simply computing bits per second received and comparing the rate to 10 Mbps It has no knowledge of the switch mirroring configuration or switch architecture A statistic of 100 percent utilization does not mean that your network or your switch backplane are about to go into meltdown, as some novices have been known to report It's helpful to remember the famous quote attributed to the Victorian-era statesman Benjamin Disraeli: "There are three kinds of lies: lies, damned lies, and statistics.

" You should calculate more than a simple mean (average) for some types of statistics Network performance data is often bimodal or heavily skewed from the mean For example, response time from a server is often bimodal, if the server sometimes retrieves data from speedy Random Access Memory (RAM) cache and sometimes gets the data from a slow mechanical disk drive When network performance data is bimodal, multimodal, or skewed from the mean, you should document a standard deviation with any measurements of the mean Standard deviation is a measurement of how widely data disperses from the mean..

for good performance, on the other hand, to achieve ef cient execution with contemporary computer architectures.

Its time complexity is O(n log n) The basic idea is to use the pair-wise boundary merging method to compute the boundary of the neighbor s area of s Initially, each node is arbitrarily paired with another node to merge their coverage boundaries Then, the merged pair s boundary is further merged with another pair s boundary This merge operation is repeated until there is only one big merged boundary, which is the boundary of neighbor s area of s The minimal F (s) consists of the nodes that contribute to this boundary The source s rst computes its F (s) and then broadcasts a ooding message which attaches IDs of nodes in F (s) Upon receiving the ooding message, each node u takes the same operation as the sender if its ID is included in the message and it is the rst time for receiving the message.

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