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With the SS-VoD prototype, we conducted extensive experiments to obtain measured benchmark results to verify against the analytical and simulation results. We developed a traf c generator in order to simulate a large number of client requests. The service node runs on a Compaq Proliant DL360 serving one movie of length 120 minutes with 30 channels, each at 1.5Mbps. The clients are ordinary PCs and all machines are connected using a layer-3 IP switch with hardware IP multicast support. We measured the start-up latency for arrival rates ranging from 1 to 5 requests per second. Each benchmark test runs for a total of six hours. Benchmark data collected during the rst hour is discarded to reduce initial condition effect. Figure 19.16 compares the start-up latencies obtained from analysis, simulation, and benchmarking respectively. We observe that the benchmarking results agree very well with the analytical results and simulation results. Note that the latencies obtained from benchmarking are consistently larger than those obtained from simulation. We believe that this is due to the non-zero processing delay and network delay in the system, both of which have been ignored in the simulation model.

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the whole system as transparent as possible not only changes to the data set, but also changes to the ontology should be logged Moreover, if ontological changes are caused by changes to the underlying data, then the ontological changes should be associated with information about the corresponding changes to the data Optionally, in order to take different user preferences into account, various change strategies could be de ned This allows the speci cation of the extent to which changes to the data should change the ontology For example, a user might want the ontology to be updated in case of newly added or modi ed data, but, on the other hand, he might want the ontology to remain unchanged if some part of the data set is deleted In addition to the above-mentioned requirements, different kinds of knowledge have to be generated or represented within a change discovery system: 1.

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In this chapter, we investigated a Super-Scalar Video-on-Demand (SS-VoD) architecture that can achieve super-linear scalability by integrating techniques of batching, patching, and periodic broadcasting. In designing the SS-VoD architecture the focus is on its practicality and the implementation and deployment complexities. For example, instead of adopting more sophisticated open-loop algorithms to schedule the static multicast channels, we employed the simple staggered periodic multicast schedule that enables us to implement interactive playback control such as pause resume, slow motion, and seeking in a simple yet ef cient way. Moreover, the staggered schedule also requires signi cantly lower client buffer requirement and more importantly, eliminates the need to switch multicast channels during a video session.

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Generic knowledge about relationships between data and ontology is required, since in case of newly added or modi ed data, additional knowledge has to be extracted and represented by the ontology For example, generic knowledge may include heuristics of how to identify concepts and their taxonomic relationships in the data 2 Concrete knowledge about relationships between the data and ontology concepts, instances and relations is needed because deleting or modifying information in the data set might have an impact on existing elements in the ontology This impact has to be determined by the application to generate appropriate ontology changes The actual references to ontology elements in the data are an example for concrete knowledge It is quite obvious that automatic or semi-automatic data-driven change discovery requires a formal, explicit representation of both kinds of knowledge.

On the other hand, hybrid multicast streaming architectures present unique challenges to media server design due to the use of both periodic and aperiodic media retrievals. In the next chapter we address the issues in designing ef cient media streaming server that supports hybrid multicast streaming algorithms.

In this Appendix, we derive the mean waiting time for Type-2 users, denoted by W2 ( ). The complication is due to length biasing as a Type-2 user is more likely to observe a longer Type-1 wait than a shorter Type-1 wait. First, we compute the waiting time distribution for Type-1 users, denoted by f C (t), as observed by a Type-2 user using the results from Kleinrock [1]: f C (t) = tfC (t) WC ( ) (19.20)

Since this representation is usually unavailable in case of a manually built ontology, we can conclude that an implementation of data-driven change discovery methods should be embedded in the context of an ontology extraction system Such systems usually represent general knowledge about the relationship between an ontology and the underlying data set by means of ontology learning algorithms Consequently, the concrete knowledge to be stored by an ontology extraction system depends on the way these algorithms are implemented A concept extraction algorithm, for example, might need to store the text references and term frequencies associated with each concept, whereas a pattern-based concept classi cation algorithm might have to remember the occurrences of all hyponymy patterns matched in the text Whereas existing tools such as TextToOnto (Madche and Volz, 2001) mostly neglect this kind of concrete knowledge and therefore do not provide any support for data-driven change discovery, the next.

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