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Table 1 Spatio-temporal extents and their interpretations

in Qualitative Extents for Spatio-Temporal Granularity
by John G. Stell
Cited by 1

Table 5: Qualitative comparison between results from the literature with 100 % density and the results for our multigrid implementations. AAE = average angular error. STD = standard deviation. 2D = spatial smoothness assumption. 3D = spatio-temporal smoothness assumption.

in A multigrid platform for real-time motion computation with discontinuity-preserving variational methods
by Andrés Bruhn, Andrés Bruhn, Joachim Weickert, Joachim Weickert, Timo Kohlberger, Timo Kohlberger, Christoph Schnörr, Christoph Schnörr 2005
"... In PAGE 26: ... This sequence that was created by Lynn Quam is very popular due to the fact that it combines translative and divergent motion under varying illumination. In Table5 the computed average angular errors [4] for both approaches are pre- sented where they are compared to the best results from the literature. The raw numbers show that the developed multigrid schemes maintain the quality of their original methods and are capable of giving very accurate results.... ..."
Cited by 6

Table 1. Qualitative spatio-temporal relations including distance between the events Relation between t and s point-based expression before (b) tEnd + distance lt; sStart after (bi) tStart gt; sEnd + distance equals (e)

in “First Have a Plan then Make Sure It Is a Good Plan ” or Dealing with Underspecified Spatio-Temporal Relations in Unfamiliar Large-Scale Environments
by Inessa Seifert

Table 4: Spatio-temporal queries posed on Precipitation

in An Evaluation of Multi-resolution Storage for Sensor Networks
by Deepak Ganesan, Ben Greenstein, Denis Perelyubskiy, Deborah Estrin, John Heidemann 2003
"... In PAGE 8: ... Drill-down Queries. Our implementation considers four types of queries (shown in Table4 ) that involve different extents of spatio- temporal processing that evaluate both advantages and limitations of wavelet compression. The GlobalYearlyEdge and LocalYear- lyMean queries explore features for which wavelet processing is typically well suited.... In PAGE 9: ... Our goal in this section is to demonstrate the search features of the system and prove our claim that multi-resolution storage can be useful for a broad variety of queries. To evaluate performance, each of the queries shown in Table4 was posed over the dataset. For yearly queries (GlobalYearlyEdge and and GlobalYearlyMax), there were 45 instances each, since there are 45 years of data.... In PAGE 10: ... Figure 6.2 shows the variation of query quality for queries defined in Table4 for different levels of drill-down. Performance for LocalYearlyMean, GlobalYearlyMax and Local- DailyMax queries are very similar, as shown in Figure 6.... In PAGE 10: ... Evaluating Training using Limited Information In our evaluation, we use a training period of two epochs of data (10% of total deployment time) to predict the query accuracy for the entire dataset. Summaries are constructed over the training set, and all queries in Table4 are posed over these summaries. Ideally, the error obtained from the training set would mirror error seen by the omniscient scheme.... In PAGE 11: ... The first column in Table 7 shows the difference between the per- formance of training and the optimal schemes. These results are ag- gregate results over a range of storage sizes (0 - 100KB) and query types (shown in Table4 ). Training performs exceedingly well, and in fact is on average less than 1% worse than the optimal solution.... ..."
Cited by 53

Table 4: Spatio-temporal queries posed on Precipitation

in An Evaluation of Multi-resolution Storage for Sensor Networks
by Deepak Ganesan, Ben Greenstein, Denis Perelyubskiy, Deborah Estrin, John Heidemann 2003
"... In PAGE 8: ... Drill-down Queries. Our implementation considers four types of queries (shown in Table4 ) that involve different extents of spatio- temporal processing that evaluate both advantages and limitations of wavelet compression. The GlobalYearlyEdge and LocalYear- lyMean queries explore features for which wavelet processing is typically well suited.... In PAGE 9: ... Our goal in this section is to demonstrate the search features of the system and prove our claim that multi-resolution storage can be useful for a broad variety of queries. To evaluate performance, each of the queries shown in Table4 was posed over the dataset. For yearly queries (GlobalYearlyEdge and and GlobalYearlyMax), there were 45 instances each, since there are 45 years of data.... In PAGE 9: ... Figure 6.2 shows the variation of query quality for queries defined in Table4 for different levels of drill-down. Performance for LocalYearlyMean, GlobalYearlyMax and Local- DailyMax queries are very similar, as shown in Figure 6.... In PAGE 10: ... Evaluating Training using Limited Information In our evaluation, we use a training period of two epochs of data (10% of total deployment time) to predict the query accuracy for the entire dataset. Summaries are constructed over the training set, and all queries in Table4 are posed over these summaries. Ideally, the error obtained from the training set would mirror error seen by the omniscient scheme.... In PAGE 11: ... The first column in Table 7 shows the difference between the per- formance of training and the optimal schemes. These results are ag- gregate results over a range of storage sizes (0 - 100KB) and query types (shown in Table4 ). Training performs exceedingly well, and in fact is on average less than 1% worse than the optimal solution.... ..."
Cited by 53

Table V. Spatio-temporal queries posed on Precipitation Dataset

in Multiresolution storage and search in sensor networks
by Deepak Ganesan, Ben Greenstein, Deborah Estrin, John Heidemann, Ramesh Govindan 2005
Cited by 17

Table 1: The domains, particles, and particle interactions that dominate the spatio-temporal

in Emergent Computation in Cellular Automata
by Rajarshi Das 1996
"... In PAGE 12: ... In addition, various types of particle interactions can be distinguished. As shown in Table1 , A supports ve stable particles, and one unstable \particle quot;, , which occurs at SS boundaries1. \lives quot; for only one time step, after which it decays into two other particles, and , respectively occurring at SD and DS boundaries.... ..."
Cited by 7

Table 2: The domains, particles, and particle interactions that dominate the spatio-temporal

in Emergent Computation in Cellular Automata
by Rajarshi Das 1996
"... In PAGE 15: ... The reactions between particles and depend on the relative distance d between them, where 0 d 2r. The most striking feature to be observed in Table2 is its similarity to the domain, particle, and particle interaction table for rule A. Indeed the number of domains, the number of particles, and the number and types of particle interactions are identical in the two tables.... ..."
Cited by 7

Table 3 Update procedures and their respective spatio-temporal changes

in Theodoridis. Literature review of spatiotemporal database models
by Nikos Pelekis, Babis Theodoulidis, Ioannis Kopanakis, Yannis Theodoridis 2005
Cited by 7

Table 23: The converse table for the spatio-temporal representation model

in Modeling Motion by the Integration of Topology and Time
by Lledó Museros, M. Teresa Escrig 2003
Cited by 1
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