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rounds of the g different groups. Figure 3.6 illustrates this GSS scheduler with two groups of media streams. There are two important characteristics in GSS. First, a media stream can join any one of the groups as long as the group is not already running at full capacity. Second, transmission of a data block lasts for a macro service round (i.e., g micro service rounds) to sustain a continuous transmission of media data to the client while waiting for the next media block to be retrieved. Using GSS, the retrieval of a data block can be completed in a shorter time than in the case of SCAN/CSCAN. For example, suppose at full capacity the server can serve k concurrent max media streams. The service round length in SCAN will then be equal to tround (k). However, if we employ GSS with two groups, then each group will serve up to k/2 requests and so the max micro-round length will be equal to tround (k/2) while the macro-round length will be equal to max 2tround (k/2). This shorter service round length is desirable in terms of buffer requirement. A buffer is occupied from the time a service round begins to the time the data retrieval is completed. Thus, max comparing GSS to CSCAN, it is clear that the buffer-holding time is reduced from tround (k) to max tround (k/2), thus allowing the buffer to be reused more quickly for the next service round. With g groups, it can be shown [1] that the buffer requirement of GSS is given by BGSS = k + k g Q (3.8)



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Comparing GSS to CSCAN s buffer requirement 2kQ, it is clear that we can reduce the buffer requirement by using GSS with more groups (i.e., increasing g). On the other hand, a new user joining the system running GSS may also experience shorter admission delay. This is possible because the new stream can join any of the g groups to receive service. If the new stream arrives in the middle of, say, micro-round serving group i, for example, then it can simply join the next group ((i + 1) mod g) provided that it is not max already fully utilized. In this case the average delay is only 1.5tround (k/g), which is shorter than max the corresponding delay under CSCAN (i.e., 1.5tround (k)). From the previous discussion it seems that we should use GSS with as many groups as possible to reduce buffer requirement and admission delay. Taking it to the limit for a server





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1. Inducing concepts/clustering of instances (given instances). 2. Inducing relations (given concepts and the associated instances). 3. Ontology population (given an ontology and relevant, but not associated instances). 4. Ontology generation (given instances and any other background information). 5. Ontology updating/extending (given an ontology and background information, such as, new instances or the ontology usage patterns). Knowledge discovery methods can be used in all of the above typical scenarios of ontology learning. When performing the learning using Knowledge Discovery, we need to select a language for representation of a membership function. Examples of different representation languages as used by machine learning algorithms are: Linear functions (e.g., used by Support-Vector-Machines), Propositional logic (e.g., used in decision trees and decision rules), First order logic (e.g., used in Inductive Logic programming). The representation language selected informs the expressive power of the descriptions and complexity of computation.

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with capacity of k concurrent streams we can at most divide them into g = k groups under GSS However, in this case the disk will serve the streams one by one in a round-robin manner, effectively reducing to FCFS! Knowing that FCFS has poor disk throughput, it raises the rst trade-off in using more groups under GSS Speci cally, with k requests divided into g groups, each micro-round will retrieve only k/g data blocks Since we are using worst-case analysis to dimension disk capacity, we will assume that the k/g data blocks are evenly spaced across the full span of the disk surface Thus, with more groups, fewer requests are served in each micro-round, which in turn increases the worst-case per-request seek distance Obviously this added overhead will result in longer service round length and thus reduces disk throughput.

After a brief move downward, the EUR/JPY was ready to pop upward I had been doing my homework, calling around to other young traders that I knew, and we were all convinced that there was no way the currency pair could break and stay below 11900 This was late September 2002 I didn t have any secret information, but I was convinced that I was right ME: How did you know you were right CRAIG: Mostly it was my own research Besides what I ve already told you, I called some analysts around Wall Street, asked them for their opinion on the British Pound against the Yen, the Swiss Franc against the Yen, and the Euro Everyone was telling me that there was going to be a sharp sell-off that would end soon They sent me some charts that confirmed what I already suspected 119.

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