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distributions all resemble the normal distribution. The same observation holds for all ve disk models simulated. In retrospect, this is expected since the round length is a summation of multiple random variables and hence would approach normal according to the central limit theorem. We take advantage of this observation and use the normal distribution in place of Fr ound (t,k) to compute numerical results in the following sections. As shown in Figure 4.4, the normal approximation curves closely overlap with their simulated counterparts and hence justify their use for computing numerical results.



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Kalfoglou Y, Schorlemmer M 2003 Ontology mapping: The state of the art The Knowledge Engineering Review 18(1):1 31 Kifer M, Lausen G 1997 F-logic: A higher-order language for reasoning about objects SIGMOD Record 18(6):134 146 Klein M 2001 Combining and relating ontologies: An analysis of problems and solutions In Workshop on Ontologies and Information Sharing, Gomez-Perez A, Gruninger M, Stuckenschmidt H, Uschold M (eds) IJCAI 01 Seattle, USA Maedche A, Motik B, Silva N, Volz R 2002 Mafra a mapping framework for distributed ontologies In Proceedings of the 13th European Conference on Knowledge Engineering and Knowledge Management EKAW-2002, Madrid, Spain Melnik S, Garcia-Molina H, Rahm E 2002 Similarity ooding: A versatile graph matching algorithm and its application to schema matching In Proceedings of the 18th International Conference on Data Engineering (ICDE 02), IEEE Computer Society, 117 p Noy NF 2004 Semantic integration: A survey of ontology-based approaches.





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Once the round length distribution is known, we can compute the usable disk capacity from equation (4.3). The rst set of results is obtained from simulation with media bit-rates of 150KB/s (e.g., MPEG-1 video). Figures 4.5a and 4.5b show the normalized gains in disk capacity versus over ow probability constraint for bit-rate of 150KB/s and block sizes of 64KB and 128KB respectively. Figures 4.6a and 4.6b show a similar set of results for bit-rate of 600KB/s (e.g., MPEG-2 video) and block size of 256KB and 512KB respectively. Note that the normalized capacity gain is de ned as G= C( ) C C (4.29)

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HARVEY: Great question! Harry, in those three hundred trades, did you tally the wins and the losses ME: Sure did Two hundred thirty-two winners and seventy-eight losers That means that over the course of a few hundred trades, I am likely to have 77 percent winning trades GINI: So this is a good system A really good one ME: Right But it wasn t the best I did better on the four-hour chart than any other I tested over six hundred trades across the same currency pairs I ended up with an average winner of 267 pips and an average loser of 94 I had a 74 percent win percentage as well HARVEY: Impressive Is that the chart you are going to focus on now with live trading ME: Yes, as soon as I m ready Until then I am going to keep paper trading HARVEY: Good.

Sigmod Record, Special Issue on Semantic Integration 33(4):65 70 Noy NF, Musen MA 2000a Anchor-PROMPT: Using non-local context for semantic matching In Proceedings of the Workshop on Ontologies and Information Sharing at the Seventeenth International Joint Conference on Arti cial Intelligence (IJCAI-2001), Seattle, WA, USA Noy NF, Musen MA 2000b PROMPT: Algorithm and tool for automated ontology merging and alignment In Proceedings 17th National Conference On Arti cial Intelligence (AAAI2000) , Austin, Texas, USA Noy NF, Musen MA 2003 The PROMPT suite: Interactive tools for ontology merging and mapping International Journal of Human-Computer Studies 59(6): 983 1024 Omelayenko B 2002 RDFT: A mapping meta-ontology for business integration In Proceedings of the Workshop on Knowledge Transformation for the Semantic Web (KTSW 2002) at the 15th European Conference on Arti cial Intelligence, Lyon, France, pp 76 83 Rahm E, Bernstein PA 2001 A survey of approaches to automatic schema matching.

where C( ) is the usable disk capacity under soft scheduling with over ow constraint and C is the usable disk capacity under hard scheduling. The lowest over ow probability constraint is set to 1 10 10 , equivalent to a mean-time-between-over ow of 138.5 years assuming the disk is operated continuously at full capacity 24 hours a day. Depending on the over ow probability constraint, the block size, and the particular disk model, the capacity gains ranges from around 20% to over 40%. To further investigate the effect of media block size on capacity gains, we plot in Figure 4.7 the capacity gains versus media block sizes for media bit-rate of 150KB/s and over ow probability constraint of 10 6 . We observe that while the capacity gains vary according to the chosen block size, the gains remain substantial for all block sizes, with all but one case exceeding 25%.

To investigate the additional gains achievable using Dual-Round Scheduling, we compute the normalized additional capacity gain from = CDRS ( ) C( ) C( ) C (4.30)

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