### Table 2. Results of query every L (t)/(n) M (t)/(n) S (t)/(n) R (t)/(n)

"... In PAGE 9: ... Nevertheless, with 3138 resources in the entire dataset, it is unlikely that there will be many queries returning more than half of the dataset. Table2 shows the results of different strategies for query every. Due to the very restrictive XML query conditions, the number of resources returned by this query is very small (at most 7 for large areas).... ..."

Cited by 1

### Table 3. Results of query population L (t)/(n) M (t)/(n) S (t)/(n) R (t)/(n)

"... In PAGE 9: ... This means that when a small number of resources are returned, ids disjunctions (aver- aged 891 for large areas) plus the non-spatial conditions will be evaluated faster than the evaluation of numerical com- parisons (in place of spatial predicates) and the non-spatial predicates on the Tamino database. For query population ( Table3 ), we can see that in the case of large and random area spatial queries, Tamino-Only and Naive are faster than Informix-First. The statistical test results in Table 4 also ascertains that Informix-First performance worse in the two cases.... ..."

Cited by 1

### Table 4: Comparing t(n) for d = 3

"... In PAGE 12: ... We note that, unlike these other efficient algorithms, our (d = 3) algorithm is time-optimal. Table4 lists the number of tests required by these algorithms for some small values of n. Table 4: Comparing t(n) for d = 3... ..."

Cited by 1

### Table 4: Comparing t(n) for d = 3

"... In PAGE 13: ... We note that, unlike these other efficient algorithms, our (d = 3) algorithm is time-optimal. Table4 lists the number of tests required by these algorithms for some small values of n. Table 4: Comparing t(n) for d = 3... ..."

Cited by 1

### Table 4.3 Comparing t(n) for d =3.

Cited by 1

### Table 3: Values of t(n, m) for various n, m

### Table 1: Upper and lower bounds of linear FD-terms

1994

Cited by 24

### Table 1: Values of n log n ; n +1,T(n) when n is a power of 2.

1998

"... In PAGE 3: ...Table 1: Values of n log n ; n +1,T(n) when n is a power of 2. = 4T(n=4) + 2n ; 2 ; 1 = 4(2T(n=8) + n=4 ; 1) + 2n ; 2 ; 1 = 8T(n=8) + 3n ; 4 ; 2 ; 1: Therefore, T(n)=2 i T(n=2 i )+in ; i;1 X j=0 2 j =2 i T(n=2 i )+in ; 2 i +1: Taking i = log n, T(n)=2 logn T(n=2 logn )+n log n ; 2 logn +1=n log n ; n +1: This enables us to predict exactly the number of comparisons in the special case where n is a power of 2 (shown in Table1 for n 1024). We can also use the solution to the recurrence for n apower of 2 to derive an upper bound on the solution for general n.... ..."