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Web services generally communicate with their clients using XML messages described with XML Schema On the semantic level, however, Web service inputs and outputs are described using ontologies A semantic client then needs grounding information that describes how the semantic data should be written in an XML form that can be sent to the service, and how XML data coming back from the service can be interpreted semantically by the client In other words, the outgoing data must be transformed from an ontological form to XML and, conversely, the incoming data must be transformed from XML to an ontological form.



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4, June 1999, pp 524 527 [47] MJ Chen, MC Chu, and SY Lo, Motion Vector Composition Algorithm for Spatial Scalability in Compressed Video, IEEE Trans of Consumer Electronics, vol 47, no 3, Aug 2001, pp 319 325 [48] Z Lei and ND Georganas, H263 Video Transcoding for Spatial Resolution Downscaling, Proc of IEEE Int Conf Information Technology: Coding and Computing, April 2002, pp 425 430 [49] KT Fung, YL Chan, and WC Siu, Dynamic Frame Skipping for High-Performance Transcoding, IEEE Int Symposium Circuits on Circuits and Systems, vol 3, May 2002, pp 389 392 [50] YQ Liang and YP Tan, A New Content-Based Hybrid Video Transcoding Method, Proceedings of IEEE Int Conf Image Processing, vol 1, Oct 2001, pp 429 432 [51] H Sun, W Kwok, and JW.





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But had the Euro even existed back then How could they do charts for a currency pair that didn t exist I deleted the weekly chart and wondered what Harvey would do in a situation like this I decided that first of all, he most certainly would not risk losing money on this trade So I definitely wasn t going to suggest a buy trade with a stop and a profit target No way But would it be appropriate to express my view that I thought the EUR/USD would stop moving down once it hit the 11850 mark Roddy had asked me, after all, where I thought it would fall to Why not write that down It was just paper trading, after all No one was going to get hurt So I wrote down: EUR/USD Stops: 1.

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Zdepski, Architecture for MPEG Compressed Bitstream Scaling, IEEE Transactions of Circuits and Systems for Video Technology, vol 6, no 2, April 1996, pp 191 199 [52] SF Chang and DG Messerschmitt, Manipulation and Compositing of MC-DCT Compressed Video, IEEE Journal on Selected Areas in Communications, vol 13, no 1, Jan 1995, pp 1 11 [53] BK Natarajan and B Vasudev, A Fast Approximate Algorithm for Scaling Down Digital Images in the DCT Domain, Proceedings of IEEE Int Conf Image Processing, vol 2, Oct 1995, pp 241 243 [54] International Organization for Standardization, Test Model Editing Committee, ISO-IEC/JTC1/SC29/WG11/ N0400 Test Model 5, April 1993..

Since the semantics of XML data is only implicit, at best described in plain text in the speci cation of an XML language, a human designer may be required to specify these data transformations so that they can be executed when a semantic client needs to communicate with a syntactic Web service In Figure 1010 we propose a way of distinguishing between data grounding approaches The gure shows a fragment of the ontology of the semantic description of an example Web service in the upper right corner and the XML data described in WSDL in the lower left corner.

Continuous media data streams generate vast amount of data. Even after compression, the data rate is often still substantial, especially if audio and video are encoded. In terms of storage, the amount of storage space required in a, say, hard disk-based media server can easily be estimated. Moreover, the recent advances in hard disk technology have signi cantly reduced the price-per-byte storage cost. At the time of writing the cost for 100GB of hard disk storage is less than US$100, and hard disk models of size up to 300GB are already widely available. Thus, although it is still an issue to be considered in the design of media servers, the storage capacity issue is becoming less of a problem in recent years. By contrast, the I/O capacity of hard disk, while it has improved steadily over the years, has not progressed at the same pace as storage capacity. While we can store many media contents in a large hard disk, it may not have suf cient I/O capacity to satisfy the streaming demand. Therefore, the challenge of improving disk I/O ef ciency is still a relevant and important research issue. In this chapter, we review the hard disk technology with an emphasis on the impact to media streaming. We present ways to model the hard disk to derive performance results useful in designing media servers. Of particular emphasis are the design and engineering of the disk scheduler, which has signi cant impact on disk I/O ef ciency. We analyze the trade-offs in disk scheduler design in both single-disk and multi-disk media servers.

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