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Again we model the system as a continuous-time Markov chain with state denoted by (h, k), where h is the number of failed servers and k is the number of redundancies activated for transmission. Let K max be the total number of redundancies in the system and K min be the excess number of redundancies to be transmitted. Figure 13.3 illustrates an example with K min = 2 and K max = 4. In general, there are three types of state transition. Speci cally, the system will transit from state (h, k) to state (h + 1, k) at a rate of h when an additional server fails. After the failure is detected, with a mean detection time of 1/ , the system will activate a new redundancy transmission and transit to state (h + 1, k + 1), thus bringing the number of excess redundancies back to K min . When a failed server is repaired at a rate of h in state (h, k), then the system will transit from state (h, k) to state (h 1, k) if (k h) K min . Otherwise, i.e., (k h) = K min , it will transit from state (h, k) to state (h 1, k 1) by deactivating one redundancy currently being transmitted. This again brings the number of excess redundancies back to K min . Note that there is a subtle problem associated with this model. In FEC, since all the servers in the system are operating, server failure will only occur in servers actively transmitting video



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In this section, we present our framework for reasoning with inconsistent ontologies. First we will introduce some de nitions and terminology for inconsistency reasoners, for instance what do we mean with a meaningful answers to a query. We continue with identifying when to use the reasoner for inconsistent ontologies.

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We do not restrict ontology speci cations to a particular language (although OWL and its underlying description logic are the languages we have in mind). In general, an ontology language can be considered to be a set of formulas that is generated by a set of syntactic rules. Thus, we can consider an ontology speci cation as a formula set. We use a nonclassical entailment for inconsistency reasoning. In the following, we use j to denote the classical entailment, and use j% to denote some nonstandard inference relation, which may be parameterized to remove ambiguities. With classical reasoning, a query f given an ontology can be expressed as an evaluation of the consequence relation j f. There are only two answers to that query: either yes ( j f), or no ( j = f). A yes answer means that f is a logical consequence of . A no answer, however, means that f cannot be deduced from because we usually do not adopt the closed world assumption when using an ontology. Hence, a no answer does not imply that the negation of f holds given an ontology . For reasoning with inconsistent ontologies, it is more suitable to use Belnap s (1977) four-valued logic to distinguish the following four epistemic states of the answers: De nition 1. (a) (b) (c) (d) Over-determined: j% f and j% :f. Accepted: j% f and j% :f. = Rejected: j= f and j% :f. % Undetermined: j= f and j= :f. % %

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To make sure reasoning is reliable when it is unclear if an ontology is consistent or not, we can use the decision tree that is depicted in Figure 5.1. For a query f we test both the consequences j f and j :f using classical reasoning. In case different answers are obtained, that is both yes and no, the ontology must be consistent and the answer to j f can be returned. In case of two answers that are the same the ontology is either incomplete, that is when both answers are no, or the ontology is inconsistent, that is when both answers are yes. When the ontology

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: state with h servers failed and k level of redundancy : aggregate server failure rate with h server failed : aggregate repair rate with h server failed : detection rate

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