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Knowledge Discovery techniques are in general aiming at discovering knowledge and that is often achieved by nding some structure in the data. This means that we can use these techniques to map unstructured data sources, such as a collection of text documents, into an ontological structure. Several techniques that we nd relevant for ontology learning have been developed in Knowledge Discovery, some of them in combination with related elds such as Information Retrieval (van Rijsbergen, 1979) and Language Technologies (Manning and Schutze, 2001). Actually, Knowledge Discovery techniques are well integrated in many aspects of Language Technologies combining human background knowledge about the language with automatic approaches for modeling the soft nature of ill structured data formulated in natural language. More on the usage of Language Technologies in knowledge management can be found in Cunningham and Bontcheva (2005). It is also important to point out that scalability is one of the central issues in Knowledge Discovery, where one needs to be able to deal with real-life dataset volumes of the order of terabytes. Ontology construction is ultimately concerned with real-life data and on the Web today we talk about tens of billions of Web pages indexed by major search engines. Because of the exponential growth of data available in electronic form, especially on the Web, approaches where a large amount of human



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The second trade-off is more subtle and only occurs under heavy system load Speci cally, if a new stream arrives to nd the next micro-round already fully occupied, then it will have to wait for a group with available capacity to join Note that this scenario will not occur if the number of active streams in service is smaller than k/g, as none of the groups will be fully occupied As more and more streams join the system, more and more groups will be fully occupied In the worst case a new stream could arrive in the middle of the only group with available capacity, and thus will need to wait until that particular group cycles back again in the next macro-round max In this worst-case scenario the admission delay will become (g + 1) tround (k/g).





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Nevertheless, the beauty of the GSS scheduler is that we can control the trade-off between throughput and buffer requirement/admission delay by dividing the streams into different number of groups In fact, the GSS scheduler subsumes as special cases both the FCFS scheduler (when g = k) and the CSCAN scheduler (when g = 1) The same principle will also nd applications in multi-disk (Section 353) and multi-server streaming systems (Part II of the book)..

intervention is necessary, become inapplicable. Here we see a great potential for Knowledge Discovery with its focus on scalability. The following subsections brie y describe some of the Knowledge Discovery techniques that can be used for addressing the ontology learning scenarios described in Section 2.5.

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So far, we have only considered media storage and retrieval in a single hard disk. In practice, the capacity of a single disk may not be suf cient to serve a large number of concurrent users. While disk storage capacity is advancing rapidly, improvements in disk throughput are far slower due to mechanical limits like spindle speed, disk seek, etc. Therefore in most, if not all, media servers, an array of disks will be needed to provide suf cient streaming capacity. The challenge then is how to ef ciently store and retrieve media data from multiple disks.

First, if the disks are operated independently, i.e., each disk is accessed independently from other disks using, say, a separate logical disk drive or lesystem, then we can simply distribute the media objects such as movie les to the disks according to certain placement policy. For example, we may place media object 1 in disk 1, media object 2 in disk 2, and so on. When a user requests the streaming of media object 1, then the server will simply retrieve the media data blocks from disk 1 for transmission to the user. Each disk in this case is independent and thus the same disk schedulers discussed earlier in Sections 3.2 to 3.4 can be applied directly without modi cation.

In the broader context, the Knowledge Discovery approach to ontology learning deals with some kind of data objects which need to have some kind of properties these may be text documents, images, data records or some combination of them From the perspective of using Knowledge Discovery methods for inducing concepts given the instances (ontology learning scenario 1 in Section 25), the important part is comparing ontological instances to each other As document databases are the most common data type conceptualized in the form of ontologies, we can use methods developed in Information Retrieval and Text Mining research, for estimating similarity between documents as well as similarity between objects used within the documents (eg, named entities, words, etc) these similarity measures can be used together with unsupervised learning algorithms, such as clustering algorithms, in an approach to forming an approximation of ontologies from document collections.

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