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Table 1. Basic and advanced datasets for Books Shopping and Music Shopping domains

in A Task-specific Approach for Crawling the Deep Web
by Manuel Álvarez, Juan Raposo, Fidel Cacheda, Alberto Pan
"... In PAGE 9: ...efinitions are shown in Fig. 6. Once the domains were created, we used DeepBot to crawl 20 websites of the respective Yahoo Directory category. The websites visited by DeepBot for each domain are shown in Table1 . The websites used to define the attributes and aliases are grouped in a dataset named Basic, while the remaining sites are grouped in a dataset named Advanced.... ..."

Table 1: The Shoppings relation

in
by unknown authors

Table 4: Frequency of shopping acts.

in Consumers in Swiss Online Grocery Shops
by Pascal Sieber
"... In PAGE 9: ... Run Shoppers are people (cf. Table4 ) who have difficulty finding time to do their shopping during working hours, indicate that they have problems to take time for shopping and would be happy to save time, indicate that they have to do their shopping during the rush hours (between 11 a.... ..."

Table 3: Shopping Cart schema

in unknown title
by unknown authors 2006
"... In PAGE 3: ...able 2: Printer property multiplicities.....................................................................................24 Table3 : Shopping Cart schema.... ..."

Table 2: Job Shop Problem

in SCHEDULING BATCH PROCESSING MACHINES IN COMPLEX JOB SHOPS
by B. A. Peters, J. S. Smith, D. J. Medeiros, M. W. Rohrer
"... In PAGE 5: ... Therefore, the job shop problem is modified into a flexible flow shop prob- lem using dummy nodes to fill in the space in the matrix where a particular step is not included in a particular job route. Table2 and 3 show an example how a complex job shop problem can be modified into a flexible flow shop problem. Each step has an associated TG type and a recipe name.... ..."

Table 1: Cumulative market share of largest x% of shops % of shops

in Game Theory: Limitations and an Alternative
by Scott Moss 2001
"... In PAGE 2: ... This relationship obviously implies a straight line on double log scales. The data from which those plots have been drawn are reproduced in Table1 . Evidently, the most competitive markets are those with the steepest slope of the trend line of cumulative market share plotted ... In PAGE 3: ... Another way of stating this property is that the variance of the distribution is not finite. We see from Figure 1 and Table1 that the relationship between the number of shops and their cumulative market share is closer to the trend line as the industry is more competitive as measured by the market shares of the largest x per cent of outlets. This empirical result coheres well with the circumstances in which, by observation and simulation, we know power law distributions to emerge.... In PAGE 14: ... Moreover, there is no obvious trend. As with the empirical market share data reported in Table1 , the steeper slopes of the power function correspond to less skewness in the ... ..."
Cited by 2

Table 2: Directory states.

in LimitLESS Directories: A Scalable Cache Coherence Scheme
by David Chaiken, John Kubiatowicz, Anant Agarwal 1991
"... In PAGE 4: ... The memory controller side of this protocol is illustrated in Figure 2, which contains the memory states listed in Ta- ble 2. These states are mirrored by the state of the block in the caches, also listed in Table2 . It is the responsibil- ity of the protocol to keep the states of the memory and the cache blocks coherent.... ..."
Cited by 202

Table 2: Directory states.

in LimitLESS Directories: A Scalable Cache Coherence Scheme
by David Chaiken, John Kubiatowicz, Anant Agarwal 1991
"... In PAGE 4: ... The memory controller side of this protocol is illustrated in Figure 2, which contains the memory states listed in Ta- ble 2. These states are mirrored by the state of the block in the caches, also listed in Table2 . It is the responsibil- ity of the protocol to keep the states of the memory and the cache blocks coherent.... ..."
Cited by 202

Table 1: Results for the shopping model

in A Robust Algorithm For The Simultaneous Estimation Of Hierarchical Logit Models
by M. Bierlaire
"... In PAGE 15: ...pproximation (see Section 3.4.4), yielding to four variants. All tests have been computed on a 486DX (33 Mhz) PC computer using the HieLoW package (see for example Bierlaire, 1994 and Bierlaire and Vandevyvere, 1995) running in the Windows environment. Main results are gathered in Table1 for the shopping model and Table 2 for the parking model. For each variant of the algorithm, the following information is reported : the total number of iterations (Iter.... In PAGE 18: ...1 1 10 Magnitude 0 5 10 15 20 25 30 35 Iteration Step Trust region Figure 5: Step magnitude and trust region size not negligible (column OK of Table 3). Note that column -CURV of Table 3 gathers the same information as the last line in Table1 and 2. 4.... ..."

Table 1: Results for the shopping model

in hierarchical logit
by unknown authors 1995
"... In PAGE 15: ...pproximation (see Section 3.4.4), yielding to four variants. All tests have been computed on a 486DX (33 Mhz) PC computer using the HieLoW package (see for example Bierlaire, 1994 and Bierlaire and Vandevyvere, 1995) running in the Windows environment. Main results are gathered in Table1 for the shopping model and Table 2 for the parking model. For eachvariant of the algorithm, the following information is reported : the total number of iterations (Iter.... In PAGE 18: ...1 1 10 Magnitude 0 5 10 15 20 25 30 35 Iteration Step Trust region Figure 5: Step magnitude and trust region size not negligible (column OK of Table 3). Note that column -CURVofTable 3 gathers the same information as the last line in Table1 and 2. 4.... ..."
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