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Table 2 summarizes the results of our Network Challenge competition entry. We

in Secure, Efficient Data Transport and Replica Management for High-Performance Data-Intensive Computing
by Bill Allcock Joe, Joe Bester, John Bresnahan, Ann L. Chervenak, Ian Foster, Carl Kesselman, Sam Meder, Veronika Nefedova, Darcy Quesnel Steven 2001
"... In PAGE 12: ...9 Mbits/sec Total data transferred in 1 hour 230.8 Gbytes Table2 : Network Challenge Configuration and Performance Results The next version of the GridFTP implementation will support higher bandwidth with data channel caching . This mechanism allow s a client to indicate that a TCP stream is likely to be re - used soon after the existing transfer is complete.... ..."
Cited by 36

Table 1: Stock streams used in our simulation

in A Scalable Approach to Approximating Aggregate Queries over Intermittent Streams £
by unknown authors
"... In PAGE 8: ... Highly volatile data are more challenging for our adaptive query evaluation model. For example, the AC value is a measure of the rela- tive volatility of a stock to the market (see Table1 ). Gen- erally, symbols with AC BE CJBDBN BGCL are considered to have high volatility.... ..."

Table 1: Grand Challenges

in Science, Computational Science and Computer Science: At a Crossroads
by D. E. Stevenson 1994
"... In PAGE 1: ...n December, 1991, the U. S. Congress passed the High Performance Computing and Communications Act, commonly known as the HPCC. This act fo- cuses on several aspects of computing technology, but two have received the most attention: (i) computa- tional science as embodied in the Grand Challenges ( Table1 ) and (ii) the National Research and Educa- tional Network (NREN ). The NREN is to be a net- work of extremely high speed, capable of transmit- ting in the terabit per second range|approximately ten times faster than we can currently transmit data.... ..."
Cited by 21

Table 5. Stream data for Case 1 Stream

in Tabu search algorithm for chemical process optimization
by B. Lin, D. C. Miller 2004
Cited by 2

Table 1.1: Summary of challenges and focus areas of existing streaming content distribution networks.

in ENABLING LARGE-SCALE PEER-TO-PEER STORED VIDEO STREAMING SERVICE WITH QOS SUPPORT
by Masaru Okuda, Masaru Okuda Phd 2006

Table 2: Test Data Streams

in Density Estimation Over Data Stream ∗
by Aoying Zhou
"... In PAGE 6: ... Next, data streams are drawn randomly and independently from these distributions. Table2 shows that. Data Stream 1-3 are drawn randomly from distribution I-III, accordingly.... ..."

Table 2. Test Data Streams

in M-Kernel Merging: Towards Density Estimation over Data Streams
by Aoying Zhou, Zhiyuan Cai, Li Wei, Weining Qian 2003
"... In PAGE 6: ... Then, data streams are drawn randomly and independently from these distributions. Table2 shows that the data stream BD AO BF are drawn randomly from the distributions C1 AO C1C1C1, accordingly. The data stream BG is also drawn from the distribution C1C1, but it is sorted by the value.... ..."
Cited by 5

Table 1: Data stream classification.

in unknown title
by unknown authors
"... In PAGE 3: ... Each of these categories has its specific characteristics and must be handled with an appropriate communication technology. Table1 classifies the previ- ous data streams into these categories. Among the above data types, those which directly lead to a visual or aural output are more critical than others.... ..."

Table 1. Rainfall and stream data

in Development of Improved Hydrological Forecasting Models for the Lower Mekong Basin DEVELOPMENT OF IMPROVED HYDROLOGICAL FORECASTING MODELS FOR THE LOWER MEKONG RIVER BASIN
by Terry Malone, Marco Hartman, Sopharith Tes, Phung Katry
"... In PAGE 4: ... The model consists of 55 sub-areas representing 9,300 km2. Rainfall and stream flood data from the stations shown in Table1 was extracted from the MRC HYMOS database for use in the model. The rainfall stations available from HYMOS, shown in Figure 2, do not provide accurate coverage of the observed rainfall in the catchment.... ..."

Table 1. Performance Comparison on Synthetic Stream Data Binary Stream Data

in On Appropriate Assumptions to Mine Data Streams: Analysis and Practice
by Jing Gao, Wei Fan, Jiawei Han
"... In PAGE 7: ...01. From Table1 , it is clear that the two ensemble approaches (SV and AP) have better performances (best are highlighted in bold font) regardless of the measures we are using, the problem type (binary or multi-class) and the chunk size. Take the binary problem with chunk size 100 as an example.... ..."
Results 1 - 10 of 94,143