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However, it is still necessary to retrieve the entire parity group to reconstruct the unavailable block, and by doing so, the RAID-5 disk array is practically operated as a RAID-3 disk array with block interleaving instead of bit interleaving In this case, the algorithms presented in this chapter will also be applicable For the previous two reasons, it is more common to employ the RAID-3 striping scheme for use in media server applications Unlike RAID-5, where each disk can serve a different I/O request, all the disks in RAID-3 participates in serving an I/O request, thereby maximizing disk throughput More importantly, no additional overhead will be incurred by the remaining disks during degraded mode This is because each I/O always retrieves the entire parity group from all the remaining disks and hence reconstruction can take place immediately.



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We will focus on this RAID-3 striping scheme in the rest of the chapter..

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Often it is too hard or too costly to integrate available background domain knowledge into fully automatic techniques. Active Learning and Semi-supervised Learning make use of small pieces of human knowledge for better guidance towards the desired model (e.g., an ontology). The effect is that we are able to reduce the amount of human effort by an order of magnitude while preserving the quality of results (Blum and Chawla, 2001). The main task of both the methods is to attach labels to unlabeled data (such as content categories to documents) by maximizing the quality of the label assignment and by minimizing the effort (human or computational). A typical example scenario for using semi-supervised and active learning methods would be assigning content categories to uncategorized documents from a large document collection (e.g., from the Web or from a news source) as described in (Novak, 2004a). Typically, it is too costly to label each document manually but there is some limited amount of human resource available. The task of active learning is to

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Table 5.1 summarizes the notations used in this chapter. Let N D be the number of disks in the server where (N D 1) of them store data while the remaining one stores parity. The storage is divided into blocks of Q bytes as shown in Figure 5.1. Assuming the disks are homogeneous, then a parity group comprises one block at the same location from each one of the N D disks.

Table 5.

use the (limited) available user effort in the most ef cient way, to assign high quality labels (eg, in the form of content categories) to documents; semi-supervised learning, on the other hand, is applied when there are some initially labeled instances (eg, documents with assigned topic categories) but no additional human resources are available Finally, supervised learning is used when there is enough labeled data provided in advance and no additional human resources are available All the three methods can be useful in populating ontologies (ontology learning scenario 3 in Section 25) using document categorization as well as in more sophisticated tasks such as inducing relations (ontology learning scenario 2 in Section 25), ontology generation and extension (ontology learning scenarios 4 and 5 in Section 25).

Harvey and I had spoken in person every day since the day that he had shown up at Mass Within a couple of weeks, he was repeating a lot of what he already had said, and although he always checked my notebooks to.

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