### Table 1 Characteristics of common prefix structures.

1996

"... In PAGE 4: ... Also, these two bounded fan-out parallel-prefix structures are not compatible with the other structures and the synthesis algorithm presented in this paper, and thus were not considered any further in this work. Table1 summarizes some characteristics of the serial-prefix and the most common parallel-prefix structures with respect to maximum depth (D , number of black nodes on the critical path), size (# , total number of black nodes), maximum number of black nodes per bit position (#max =b ), wiring complexity (#tracks, horizontal tracks in the graph), maximum fan-out (F O max ), synthesis (compatibility with the pre- sented optimization algorithm), and area/delay performance (A/T ). The area/delay performance figures are obtained from a very rough classification based on comparing standard-cell implementations [3].... ..."

Cited by 15

### Table 1 Characteristics of common prefix structures.

"... In PAGE 4: ... Also, these two bounded fan-out parallel-prefix structures are not compatible with the other structures and the synthesis algorithm presented in this paper, and thus were not considered any further in this work. Table1 summarizes some characteristics of the serial-prefix and the most common parallel-prefix structures with respect to maximum depth (D , number of black nodes on the critical path), size (# , total number of black nodes), maximum number of black nodes per bit position (#max =b ), wiring complexity (#tracks, horizontal tracks in the graph), maximum fan-out (F O max ), synthesis (compatibility with the pre- sented optimization algorithm), and area/delay performance (A/T ). The area/delay performance figures are obtained from a very rough classification based on comparing standard-cell implementations [3].... ..."

### Table 10: Complexity of computing the minimal domains in tractable augmented qualitative networks. discrete domains, we shall keep two pointers, Inf and Sup, to inf(Di) = v1 and sup(Di) = vk, respectively. We shall use three parameters in analyzing the computational complexity of algorithms: n|the number of nodes in the network, e|the number of arcs, and k|the maximum domain size, that is, the number of values in a domain (for discrete domains) or the number of intervals per domain (for continuous domains). In the rest of this section we show that for augmented CPA networks and for some augmented PA networks, the interesting tasks can be solved in polynomial time using local consistency algorithms such as arc consistency (AC) and path consistency (PC).

1991

Cited by 132

### Table 10: Complexity of computing the minimal domains in tractable augmented qualitative networks. discrete domains, we shall keep two pointers, Inf and Sup, to inf(Di) = v1 and sup(Di) = vk, respectively. We shall use three parameters in analyzing the computational complexity of algorithms: n|the number of nodes in the network, e|the number of arcs, and k|the maximum domain size, that is, the number of values in a domain (for discrete domains) or the number of intervals per domain (for continuous domains). In the rest of this section we show that for augmented CPA networks and for some augmented PA networks, the interesting tasks can be solved in polynomial time using local consistency algorithms such as arc consistency (AC) and path consistency (PC).

1991

Cited by 132

### Table 1: Efficiency of Algorithm 2 at reducing average domain size and state space size, in terms of solubility.

"... In PAGE 5: ... If we look at this reduction in terms of state space size, however, it becomes much more significant: an average 94% decrease in state space size. Table1 presents the state space reduction efficiency of our constraint propagation tech- nique in terms of problem solubility. Since constraint prop- agation was fairly consistent in the percentage of state space reduced between those problems that were soluble and those that were insoluble, this suggests that the 74% of the prob- lems that remained insoluble were due to the large state space size inherent in their structure.... ..."

Cited by 1

### Table 1: Efficiency of Algorithm 2 at reducing average domain size and state space size, in terms of solubility.

"... In PAGE 5: ... If we look at this reduction in terms of state space size, however, it becomes much more significant: an average 94% decrease in state space size. Table1 presents the state space reduction efficiency of our constraint propagation tech- nique in terms of problem solubility. Since constraint prop- agation was fairly consistent in the percentage of state space reduced between those problems that were soluble and those that were insoluble, this suggests that the 74% of the prob- lems that remained insoluble were due to the large state space size inherent in their structure.... ..."

Cited by 1

### Table 20: In uence of domain on index size (in number of 4K pages)

"... In PAGE 18: ... ST has to traverse a larger number of branches, while the explosive growth of the ESH directory makes it necessary to generate a large number of subqueries during query processing. uniform distribution Zipf distribution = = Domain size right false right false right false right false right false right false 200 1 0 350 2258 1 195 1 0 8836 1327 1 1086 2000 1 0 34 2020 1 174 1 0 7077 2130 1 160 1000000 1 0 1 1995 1 158 1 0 3011 1453 1 486 Table 19: Number of right and false drops for signature-based index structures Table20 shows the results of the in uence of the domain size on the index size. The smaller the domain, the better the space demand of inverted les.... ..."

### Table 2. Order entry domain. Number and size of messages

2006

"... In PAGE 13: ... The overhead introduced by our replication algorithm can be compared to the one of JBoss clustering by measuring the number of messages sent from the primary to the backup and their average size. Table2 shows the results for the entry order domain and 10 Ir. Table 3 shows these figures for Ir=10 and both the order entry and the manufac-... ..."

Cited by 3

### Table 2. Description of learning tasks Domain Size No. of No. of Attributes

2000

"... In PAGE 15: ... This test suite covers a wide variety of domains from the UCI machine learning repository (Blake, Keogh, amp; Merz, 1998). Table2 gives a summary of the characteristics of these domains, in- cluding dataset size, the number of classes, the number of numeric attributes, and the number of nominal attributes. In each domain, two strati ed 10-fold cross-validations (Breiman, Friedman, Ol- shen, amp; Stone, 1984; Kohavi, 1995) are carried out for each algorithm.... ..."

Cited by 27