### Table 2: Average BCP time per implication. benchmark no optim. optim. speedup

"... In PAGE 2: ... Partial clauses together with immediate implications provide effective BCP optimization. Table2 presents examples of average time in machine cycles spent on BCP per implication; column no optim. shows data for BCP without optimizations; column optim.... ..."

### Table 3 Example of an individual implication matrix (Respondent R)

in Marketing

2002

"... In PAGE 18: ... As an example, the implication matrix found for one particular respondent, say respondent R, is given in Table 3. lt; Table3 about here gt; ... In PAGE 19: ... Table 3 shows which ends j are implied by which means i, and therefore, Reynolds and Gutman (1988) called such matrices implication matrices . It is immediately clear, that Table3 contains quite a few mutuals . For example, this respondent has stated that feel fine (1) is a means to personal development (2), but at the same time that personal development is a means to feel fine .... In PAGE 19: ... Aggregate analysis First we look at the aggregate implication matrix. The aggregate implication matrix is obtained by simply taking the average ratings of the implication matrices of the individual respondents, such as the one shown in Table3 . The aggregate implication matrix is presented in Table 4.... In PAGE 20: ... Aggregation could then produce entries in the implication matrix, both in cell (i,j) and cell (j,i). To refrain from the effects of mixing different respondents, an analysis at the level of the individual implication matrices, such as the one of Table3 is needed. Let g be the number of concepts making up the network.... In PAGE 21: ...Table3 ). This implies that it serves four times as a means and nine times as an end.... In PAGE 21: ... (Xij= Xji =0). For instance, Table3 shows a null dyad for quot; quot;prove yourself quot; and quot;satisfaction . The next possibility is an asymmetric dyad from i to j, i.... In PAGE 21: ...yad from i to j, i.e. if Xij is 1 and Xji is 0. An example in Table3 is the relation between quot;personal development quot; and quot;perform properly quot;. It can also be an asymmetric dyad from j to i, i.... In PAGE 21: ... Finally, between two concepts there can be a mutual dyad, where both Xij and Xji are 1. For example, in Table3 we have a mutual between quot;feel fine quot; and quot;personal development quot;. Let M be the total number of mutual dyads.... In PAGE 21: ...ine quot; and quot;personal development quot;. Let M be the total number of mutual dyads. M is the critical number for testing hypotheses regarding the symmetry of means-end relations. For the respondent presented in Table3 (respondent R) M is 10. If means- end relations are neutral toward symmetry, the expected value of M is equal to the number of mutual relations under mere chance (given the values of the marginals).... In PAGE 21: ...elations. For the respondent presented in Table 3 (respondent R) M is 10. If means- end relations are neutral toward symmetry, the expected value of M is equal to the number of mutual relations under mere chance (given the values of the marginals). As we will see later, the expected number by mere chance in the case of respondent R of Table3 , is 8.... In PAGE 23: ... The parameters g114 were estimated for all 136 respondents using the UCINET V program (Borgatti, Everett and Freeman, 1999). For respondent R, with the implication matrix of Table3 , the estimated g114 is 1.15, which confirms its tendency towards symmetry (g114 gt;0).... In PAGE 25: ...000 matrices that have the same number of indegrees and outdegrees as had been observed with these respondents. For example, let us look at the results for respondent R, of which the implication matrix is given in Table3 . For this respondent, the observed number of mutual relations, Mobserved, is 10.... ..."

### Table 5: ABox I - ground facts However, in a concrete system the introduction of a name for a description may have side-e ects, for example, that computations are performed which would otherwise not have occurred. In particular, named descriptions (= de ned concepts) are immediately classi ed into the concept graph. Descriptions are also used to de ne rules, which are expressed as implications between two descriptions. The left hand sides of the rules shown in Table 4 are descriptions of certain sets of observations which are asserted to be in the set of normal or abnormal ones by the description on the right hand side.

"... In PAGE 7: ... The left hand sides of the rules shown in Table 4 are descriptions of certain sets of observations which are asserted to be in the set of normal or abnormal ones by the description on the right hand side. Table5 shows how `data apos; is actually entered into the system. The `:: apos; operator is used for asserting that the description on the right hand side is true for the object referenced on the left hand side.... In PAGE 9: ... For each new description and each new object the proper posi- tion is determined at once. The object descriptions in Table5 are quite similar to what one would enter into an ordinary database: an entity patient is related to some particular examinations which are in turn related to the items representing the results. In fact, they are complete descriptions of the mentioned objects with respect to the given terminology: For each object, the set of primitive concepts it instantiates is exactly known (due to the not terms in the introductions enforcing disjointness, there is no indeterminacy).... In PAGE 9: ... In fact, these objects are not interrelated at all. However, they all instantiate the same concepts as the objects of Table5 . In this case because they were directly asserted to be instances of those concepts; the... In PAGE 10: ... In contrast to the forget operation, which puts the KB back into a state as if the description had never been entered, and therefore also has to retract all the derived information, the remove instance operation does not undo any consequences derived from the instance being removed. Table 7 shows an ABox which is equiva- lent to the ABox of Table5 after removeing the observations. Although after... ..."

### Table 3: Practical Implications

"... In PAGE 17: ... We experimented with several data sizes, which are 1, 10 and 20 in terms of buckets, but similar results are obtained. Table3 shows the results with data size 10. While the total size of a bcast is small, the experimental results show some di erences from analytical ones, but as the bcast gets bigger we could... In PAGE 18: ...3 Practical Implication We applied the SL into a real world wireless system called Quotrex[9] which periodically broadcasts stock market information (about 160KB) with 10Kbps on FM band. And we could obtain the result in Table3 . AT-opt method has no index but only data.... ..."

### Table 3 Implication result

2000

"... In PAGE 8: ..., 1994), that periodically broadcasts stock market information (about 160 KB) with 10 Kbps on FM band. The result is shown in Table3 . The AT-opt method has no index but only data, so it gives optimal access time but the worst tuning time.... ..."

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### Table 1 : Implication of HPSet

2001

"... In PAGE 3: ... HP-Set stands for High Per- formance index-Set. The index set is composed of the set of statistics shown in Table1 . In a nut- shell, HP-Set is a portfolio with its major indexes being the followings: general statistics, coherent misses, data reuse and locality, granularity and the IO index.... ..."

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