In this work, we present an information-theoretic approach for in situ reduction of large-scale time-varying data sets via a combination of key and fused time ...
Abstract—Scientists nowadays use data sets generated from large-scale scientific computational simulations to understand the.
This work presents an information-theoretic approach for in situ reduction of large-scale time-varying data sets via a combination of key and fused time ...
Jan 16, 2022 · Demonstration of the proposed spatio-temporal data summarization scheme using a sequence of time steps from the Tornado data set. Figure 2(a), 2 ...
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[19] developed a technique for summarizing large spatio- temporal images. In a recent work, Shah et al. [20] proposed an algorithm for real-time summarization ...
In this work, we present an information-theoretic approach for in situ reduction of large-scale time-varying data sets via a combination of key and fused time ...
In Situ Adaptive Spatio-Temporal Data Summarization. S Dutta, H Tasnim, TL ... Dynamic Spatio-Temporal Summarization using Information Based Fusion. H Tasnim ...
Answer: Scholarly articles for In situ adaptive spatio-temporal data summarization, dutta et al., ieee bigdata. Explore all similar answers. arrow right.
Jan 13, 2024 · In this paper, we propose the use of Kernel Density Estimation (KDE) and Kullback-Leibler (KL) divergence, in order to estimate the amount of ...
Nov 18, 2019 · In this paper, we propose the use of Kernel Density Estimation (KDE) and Kullback-Leibler (KL) divergence, in order to estimate the amount of ...
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