### Table 1: MapReduce jobs run in August 2004

"... In PAGE 10: ... At the end of each job, the MapReduce library logs statistics about the computational resources used by the job. In Table1 , we show some statistics for a subset of MapReduce jobs run at Google in August 2004. 6.... ..."

### Table 1: Partial and final results of the MapReduce operation

2006

### Table 1: Partial and final results of the MapReduce operation

2006

### Table I. MapReduce Statistics for Different Months.

### Table 1 Parameter classi cation of fuzzy inference systems. Class Parameters

"... In PAGE 4: ... One of the major problems in fuzzy modeling is the curse of dimensionality, meaning that the computation requirements grow exponentially with the number of variables. The parameters of fuzzy inference systems can be classi ed into four categories ( Table1 ): logical, structural, connective, and operational. Generally speaking, this order also represents their relative in uence on performance, from most in uential (logical) to least in uential (operational).... In PAGE 9: ...Table1 ). We then focus our attention on structural and connective parameters, presenting the three major evolutionary approaches: Michigan, Pittsburgh, and iterative rule learning.... In PAGE 9: ...1 Applying evolution to fuzzy modeling Depending on several criteria|including the available a priori knowledge about the system, the size of the parameter set, and the availability and com- pleteness of input/output data|arti cial evolution can be applied in di erent stages of the fuzzy parameters search. Three of the four types of fuzzy param- eters in Table1 can be used to de ne targets for evolutionary fuzzy modeling: structural parameters, connective parameters, and operational parameters. As noted in Subsection 1.... In PAGE 12: ...embership value of 0.8 entails a High membership value of 0.2). Referring to Table1 , and taking into account the above criteria, we delineate below the fuzzy system set up:... ..."

### Table 1 Parameters of fuzzy inference systems

"... In PAGE 1: ... Se- lection of important variables and adequate rules is critical for obtaining a good model. The parameters of fuzzy inference systems can be classi ed into four categories ( Table1 ): logic, struc- tural, connection, and operational. Generally speak- ing, this order also represents their relative in uence on system behavior (with logic being the most in u- ential and operational the least).... In PAGE 1: ... Our encoding of solutions (the genome) takes advantage of previous knowledge about the problem, thus reducing the search space while fa- voring the extraction of the most signi cant variables in order to provide more human-comprehensible rules. Referring to Table1 , the evolved parts of the fuzzy system in this work are: the relevant variables, the antecedents and consequents of rules, and the values of input membership functions. Thus, we evolve struc- tural, connection, and operational parameters at the same time.... In PAGE 2: ... Fuzzy system parameters Previous knowledge about the WBCD problem repre- sents valuable information to be used for our choice of fuzzy parameters. Following Table1 , we delineate below the fuzzy system set-up: Low d P High 1 Value 0 Degree of membership Figure 1 Orthogonal membership functions and their pa- rameters, plotted above as degree of membership versus input values. The orthogonality condition means that the sum of all membership functions at any point is one.... ..."

### Table 1 summarizes the parameters and their default values in the configuration of Mars. These parameters are similar to those in the existing CPU-based MapReduce framework [23]. All these parameter values can be specified by the developer for

"... In PAGE 5: ... This ensures that the output of the Reduce stage is sorted by the key. Table1 . The configuration parameters of Mars.... ..."

### Table 2: The Ecological Inference Problem at the National Level: July, 1932

2007

"... In PAGE 20: ...Within each of the six groups of precincts defined by unemployment and religion, the key substantive issue is filling in a cross-classification like that in Table2 . The rightmost column of the table gives the proportion of people in each occupational group, whereas the last row indicates the proportions of individuals who cast their ballots for each of our political party groupings.... In PAGE 20: ... The rightmost column of the table gives the proportion of people in each occupational group, whereas the last row indicates the proportions of individuals who cast their ballots for each of our political party groupings. [ Table2 about here.] While the margins of this table are observed, and the margins of analogous tables like it are observed for each precinct, the cells in the table (denoted by question marks) are not known and must be estimated.... In PAGE 22: ... 68 The same venerable methods also dominated other fields that made ecological inferences until King69 showed how to combine both sources of information in the same model.70 A variety of other methods have subsequently been proposed that also combine both sources of information,71 but few apply to tables as large as in Table2 and none are used much in applications. As such, we developed new techniques to study voting in Weimar Germany that extend this approach to combining deterministic and statistical information in the same model in a way that works for arbitrarily large tables.... ..."

### Table 2 Produced rules for different numbers of fuzzy sets

1999

"... In PAGE 13: ... The fact that the number of the produced rules is more or less a linear combination of the number of fuzzy sets used to partition the input space, could also be inferred from table 2, where someone can see the number of rules for different sizes of fuzzy partition. Table2 has an extra column containing the number of rules when the adaptive approach is applied. As expected in this situation the size of the rule base is bigger, as new rules are added during the decision making stage, where the testing data are processed.... ..."

Cited by 2

### Table 2 Produced rules for different numbers of fuzzy sets

1999

"... In PAGE 12: ... The fact that the number of the produced rules is more or less a linear combination of the number of fuzzy sets used to partition the input space, could also be inferred from table 2, where someone can see the number of rules for different sizes of fuzzy partition. Table2 has an extra column containing the number of rules when the adaptive approach is applied. As expected in this situation the size of the rule base is bigger, as new rules are added during the decision making stage, where the testing data are processed.... ..."

Cited by 2