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PROMPT the merged ontology replaces the source ontologies. The output of the merge operation in OntoMerge is not a complete merged ontology, as in PROMPT, but a bridge ontology which imports the source ontologies and which has a number of Bridging Axioms (see Figure 6.2(b)), which are translation rules used to connect the overlapping part of the source ontologies. The two source ontologies, together with the bridging axioms, are then treated as a single theory by a theorem prover optimized for three main operations: 1. Dataset translation (cf. instance transformation in de Bruijn and Polleres (2004)): dataset translation is the problem of translating a set of data (instances) from one representation to the other. 2. Ontology extension generation: the problem of ontology extension generation is the problem of generating an extension (instance data) O2s, given two related ontologies O1 and O2, and an extension O1s of ontology O1. The example given by the authors is to generate a WSDL extension based on an OWL-S description of the corresponding Web Service. 3. Querying different ontologies: query rewriting is a technique for solving the problem of querying different ontologies, whereas the authors of Dou et al. (2002) merely stipulate the problem.



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for real-time delivery may even surpass that of data integrity and presentation timing integrity. For example, it may be necessary to allow data loss (or even discard data) and/or playback jitter in order to meet the given delay budget. On the other hand, for soft-real-time delivery, there is no delay budget given. Instead, the system must deliver the media data so that data integrity and presentation timing integrity are preserved, while reducing the delay as far as possible. Examples of soft-real-time delivery are video-on-demand (VoD) where a user can select and playback a video title from the video collection available at a video server over the network as shown in Figure 1.3. These applications are far more tolerable to longer start-up delays (e.g., in seconds) as long as smooth playback is maintained after playback has started.

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In the previous section we have seen an overview of a number of representative approaches for different aspects of ontology mediation in the areas of ontology mapping, alignment, and merging. In this section we focus on an approach for ontology mapping and ontology alignment to query disparate knowledge bases in a knowledge management scenario. However, the techniques are largely applicable to any ontology mapping or alignment scenario. In the area of knowledge management we assume there are two main tasks to be performed with ontology mappings: (a) transforming data between different representations, when transferring data from one knowledge base to another; and (b) querying of several heterogeneous knowledge bases, which have different ontologies. The ontologies in the area of knowledge management are large, but lightweight, that is, there is a concept hierarchy with many concepts, but there are relatively few relations and axioms in the ontology. From this follows that the mappings between the ontologies will be large as well, and they will generally be lightweight; the mapping will consist mostly of simple correspondence between concepts. The mappings between ontologies are not required to be completely accurate, because of the nature of the

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Deal, I said, and with that I decided that for the rest of the week I would split my time between planning more trades and learning to use a spreadsheet And over the next few days, both endeavors worked out very well Learning the spreadsheets was easy And overall, the trades worked out very, very well Some of them stayed open for over a month Some were over nearly as soon as they were opened The first trade expressed my desire to benefit from the interest rates differential between the United Kingdom and Japan So I looked at the GBP/JPY, starting as usual with the four-hour chart I wanted to find a place to buy The first thing that I noticed was that this pair tended to move up and down a lot it seemed to trade inside a range all the time.

Delivering data over a network is not new and there are many different methods already available Among them, download is the most common method to deliver data from a server to a client The download model, depicted in Figure 14, is relatively straightforward: the client rst sends a request to the server indicating the data object to be downloaded; the server then retrieves the data object (eg, from the local le system) and start sending it over the network to the client using some application/transport protocol Take the WWW as an example, the web browser rst sends a HTTP GET request using TCP to a web server, which then retrieves the required le object and sends it back over the same TCP connection using a HTTP reply message After completely receiving the data object, the client (eg.

, web browser) then decodes and displays the data object to the user The key characteristic of the download model is that the data object is rst completely received, and possibly cached either in memory buffer or in the local le system, before being decoded and played back Clearly, as the complete data object is available to the client, the decoding processing and presentation can be done in the same way as local data objects This download model works well in many applications but, unfortunately, is not very suitable for continuous media data delivery Let us reconsider the download process as shown in Figure 15 Ignoring processing time, the delay from the instant the user initiates the request to the instant the requested data object can be presented is determined by the size of the data object and the rate at which it is transmitted across the network.

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