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learning scenario 5 in Section 2.5). Collecting data relevant for the existing ontology can also be used in some other phases of the semiautomatic ontology construction process, such as ontology evaluation or ontology re nement (phases 5 and 6, Section 2.4), for instance, via associating new instances to the existing ontology in a process called ontology grounding (Jakulin and Mladenic, 2005). In the case of topic ontologies (see 7), where the concepts correspond to topics and documents are linked to these topics through an appropriate relation such as hasSubject (Grobelnik and Mladenic 2005a), one can use the Web to collect documents on a prede ned topic. In Knowledge Discovery, the approaches dealing with collecting documents based on the Web data are referred in the literature under the name Focused Crawling (Chakrabarti, 2002; Novak, 2004b). The main idea of these approaches is to use the initial seed information given by the user to nd similar documents by exploiting (1) background knowledge (ontologies, existing document taxonomies, etc.), (2) web topology (following hyperlinks from the relevant pages), and (3) document repositories (through search engines). The general assumption for most of the focused crawling methods is that pages with more closely related content are more inter-connected. In the cases where this assumption is not true (or we cannot reasonably assume it), we can still use the methods for selecting the documents through search engine querying (Ghani et al., 2005). In general, we could say that focused crawling serves as a generic technique for collecting data to be used in the next stages of data processing, such as constructing (ontology learning scenario 4 in Section 2.5) and populating ontologies (ontology learning scenario 3 in Section 2.5).



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had the FOMC report in just a little while, and I decided that I needed to get my trading account up and ready So are you trading still I asked, hoping to get a bit more information about John s plans Yes I m having some trouble pulling the trigger on my trades Some of the best ones in fact But it will pass I am down for the year still, but I am confident that will change And you I only have a small account But it s positive right now How much I don t know, I answered And then I calculated the percentage gain in my head Seventy percent so far He nearly choked Seventy percent Your account is up 70 percent for the year What was the big deal At the rate I was going, it would be up 100 percent by today.





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Over the years a number of streaming protocols have been developed both by commercial companies and the Internet community. On the commercial side, streaming solution companies often develop their own proprietary streaming protocols for use in their streaming products. For example, Microsoft developed a Microsoft Media Services (MMS) for use in its Windows

Media streaming solution. MMS employs TCP for the exchange of control messages and can send the media data over either UDP or TCP. RealNetworks also developed their own RealNetworks Data Transport (RDT) for use in their streaming solution. Because of the proprietary nature of these protocols we will not cover them further in this chapter. On the other hand, the Internet community has also developed open standards for media streaming. This includes the Real Time Streaming Protocol (RTSP) de ned in RFC 2326 [7], the Real-time Transport Protocol (RTP) and the RTP Control Protocol (RTCP), rst introduced in RFC 1889, later revised in RFC 3550 [8], which became an of cial standard in May 2004.

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Visualization of data in general and also visualization of document collections is a method for obtaining early measures of data quality, content, and distribution (Fayyad et al., 2001). For instance, by applying document visualization it is possible to get an overview of the content of a Web site or some other document collection. This can be useful especially for the rst phases of semi-automatic ontology construction aiming at domain and data understanding (see Section 2.4). Visualization can be also used for visualizing an existing ontology or some parts thereof, which is potentially relevant for all the ontology learning scenarios de ned in Section 2.5. One general approach to document collection visualization is based on clustering of the documents (Grobelnik and Mladenic, 2002) by rst representing the documents as word-vectors and performing k-means clustering on them (see Subsection 2.6.1). The obtained clusters are then represented as nodes in a graph, where each node in the graph is described by the set of most characteristic words in the

6.2.1 Real-Time Streaming Protocol (RTSP)

I told him, yes, it was up that far but I really still had a lot to learn I felt badly that I was doing so much better than he was doing Now I felt my leg start to shake a bit I wanted to start trading again as soon as possible I had profits to make, and all this talk was driving me crazy I raced back toward my desk, thinking about how great the day had been A great day in a great week in what was going to be the most Amazing great year of my life so far And now I was going to trade Or at least that is what I thought I was going to do Scott Needleway was standing at my cubicle when I got back from lunch He did not look happy Herbie is pissed and wants to see you now.

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