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Table 7.2 Comparison of admission complexity between the Optimal Smoothing and the AMDR schedulers (client buffer size xed at 32MB) Unsuccessfull Admission # Scheduler Temporal smoothing complexity at = 0.6 complexity at = 0.7 complexity at = 0.8 complexity at = 0.9 AMDR scheduler complexity at = 0.6 complexity at = 0.7 complexity at = 0.8 complexity at = 0.9 Comparisons 0.00 (0.00) 0.01 (0.00) 4.15 (0.20) 74.00 (5.15) 0.00 (0.00) 0.00 (0.00) 0.20 (0.20) 5.19 (5.17) Additions 0.00 (0.00) 0.01 (0.00) 4.15 (0.20) 74.00 (5.15) 0.00 (0.00) 0.00 (0.00) 0.20 (0.20) 5.19 (5.17) Successfull Admission Comparisons 5782.52 5782.20 5782.04 5782.08 5008.15 5007.47 5007.15 5007.08 Additions 5782.52 5782.20 5782.04 5782.08 5781.70 5781.38 5781.22 5781.26



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The two main drivers of practical knowledge management are technology and people, as pointed out earlier by Davenport (1996). Traditional IT-supported knowledge management applications are built around some kind of corporate or organizational memory (Abecker et al., 1998). Organizational memories integrate informal, semi-formal, and formal knowledge in order to facilitate its access, sharing, and reuse by members of the organization(s) for solving their individual or collective tasks (Dieng et al., 1999), for example as part of business processes (Staab and Schnurr, 2002). The knowledge structures underlying such knowledge management systems constitute a kind of ontology (Staab and Studer, 2004) that may be built according to established methodologies (Fernandez-Lopez et al., 1999; Sure, 2003). These methodologies have a centralized approach towards engineering knowledge structures requiring knowledge engineers, domain experts, and others to perform various tasks such as a requirement analysis and interviews. While the user group of such an ontology may be huge, the development itself is performed by a comparatively small group of domain experts who provide the model for the knowledge, and ontology engineers who structure and formalize it. Decentralized knowledge management systems are becoming increasingly important. The evolving Semantic Web (Berners-Lee et al., 2001)





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Notes: *Numerical results are measured as the average number of computations required to admit a client at a given average network utilization ( ), averaged over requests for all 274 videos. # Numbers in parentheses are the average number of unsuccessful admission tests performed for each request.

60 Mean Waiting Time (seconds) 55 50 45

Semantic Web Technologies: Trends and Research in Ontology-based Systems John Davies, Rudi Studer, Paul Warren # 2006 John Wiley & Sons, Ltd

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I could feel the door close behind me it hit me in the butt but I hardly moved I was staring at the trading floor of a major Wall Street firm, and I thought I had been teleported to another planet There were at least 20 rows of desks, formed in a giant half-circle, starting directly in front of me, and extending out all the way across the floor to the other end of the building At least 200 men and a few women were yelling, pounding on their computers, and watching their screens There was so much happening right here, just a few floors below where I had been working for 11 years I forgot entirely about Anderson, and just wandered onto the floor Glass windows looked out on midtown from the west side, and every other wall had glass conference rooms, one after the other, mostly empty.

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will foster the development of numerous use cases for this new paradigm. Therefore, methodologies based on traditional, centralized knowledge management systems are no longer feasible. There are some technical solutions toward Peer-to-Peer knowledge management systems (e.g., Bonifacio et al., 2003; Ehrig et al., 2003). Still, the traditional methodologies for creating and maintaining knowledge structures appear to be unusable in distributed and decentralized settings, and so the systems that depend on them will fail to cope with the dynamic requirements of big or open user groups. The chapter is structured as follows. First, we de ne methodology and ontology engineering methodology in Section 9.2. Then we provide a survey of existing ontology engineering methodologies in Section 9.3. Since we believe that the engineering of ontologies in practical settings requires tool support to cope with the various complex tasks we also include a survey of corresponding ontology engineering tools in this section. The survey ends with an enumeration of open research issues. We partly address these research issues with the new DILIGENT (Distributed, Loosely controlled, and evolvInG Engineering of oNTologies) methodology which is introduced in Section 9.4. Before concluding we present some rst lessons learned from applying DILIGENT in a number of case studies.

30 0 50 100 150 Client Buffer Size (MBs) 200 250

Figure 7.7a Mean client waiting time versus client buffer size Note: The MDR scheduler does not have a buffer constraint.

Above the conference rooms were screens: Some screens showed what were obviously quotes on stocks or bonds or whatever, and some showed what appeared to be news conferences from around the world, with closed.

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