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If the media data in the system are also stored in a back-up storage, then we can simply load the required media data from the back-up media to the spare server. This can be done automatically without affecting the remaining active servers. The rebuild rate will then be equal to the transfer capacity of the back-up device or the spare server, whichever is smaller. While this scheme is simple and ef cient, the mechanisms needed to automate the process (selection and loading of media data) are not inexpensive, such as large juke-boxes or robotic tape libraries. Moreover, management of the back-up data will be more complex as old media data are removed and new ones uploaded to the servers from time to time. Finally, if the media data streams are backed up sequentially, then considerable searching will be needed when loading the media data into a spare server due to the server stripping scheme used. For comparison with other rebuild algorithms, we assume the back-up device has in nite throughput and capacity. Therefore, the rebuild rate is bounded by the server throughput, or Rreload = SS which is also the maximum rate that can be achieved by any data rebuild algorithm. (14.1)

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The two methods discussed earlier both require extra hardware/software and/or human operator support. This section presents the rst automatic algorithm baseline rebuild, that requires neither extra hardware/software support nor human intervention. The principle is to utilize the idle capacities in the remaining servers to send data/redundant units to the spare server, which

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One was used for providing topics for discussion, hand raising, and voting The other one served to exchange arguments When the participants the same as in the rst experiment wanted to discuss a certain topic, for example the introduction of a new concept, they had to introduce it in the rst chat room The topics to discuss were published on a web site, and were processed sequentially Each topic could then be justi ed with the allowed arguments Participants could provide arguments only after hand raising and waiting for their turn The participants decided autonomously when a topic had been suf ciently discussed, called for a vote and thus decided how to model a certain aspect of the domain The evolving ontology was again published on a web site The moderator had the same tasks as in the rst experiment, but was stricter in interpreting the rules.

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then rebuilds the data lost in the failed server (Figure 14.5). This algorithm is similar to the baseline rebuild scheme in disk arrays [3]. To recover an unavailable data unit we need to perform erasure correction using the remaining (N S 1) data/redundant units. Since we want to keep the process transparent to normal streaming sessions, we can only utilize the servers idle capacities. Assuming the system is running at a utilization of [0,1], the average available transfer rate from each server will be equal to SS (1 ). The aggregate data transfer rate of the remaining (N S 1) servers is therefore equal to r = SS (1 )(N S 1) (14.2)

Now as the spare server has a transfer capacity of SS , the aggregate data rate r will exceed the spare server s transfer capacity if is less than (1 /(N S 1)). This is stated in the Theorem 14.1 which computes the upper limit on the baseline rebuild rate. Theorem 14.1. The data rebuild rate of baseline rebuild, denoted by Rbaseline is bounded by the capacity S S of the spare server and is given by Rbaseline = SS /(N S 1) for < (1 1/(N S 1)) SS (1 ) for (1 1/(N S 1)) (14.3)

Whenever needed, the moderator called for an example of an argument to enforce the participants to express their wishes clearly As expected the discussion was more focused, due to the stricter procedural rules Agreement was reached more quickly and a much wider consensus was reached With the stack of topics which were to be discussed (not all due to time constraints), the focus of the group was kept The restricted set of arguments is easy to classify and thus the ontology engineer was able to build the ontology in a straightforward way It is possible to explain to new attendees why a certain concept was introduced and modeled in such a way, by simply pointing to the archived focused discussion It is even possible to state the argumentation line used to justify it The participants truly shared the conceptualization and did understand it.

Proof. Case 1: < (1 1/(N S 1)) From equation (14.2): r = SS (1 )(N S 1) SS (1 (1 1/(N S 1)))(N S 1) = SS