In Situ Prediction Driven Feature Analysis in Jet Engine Simulations

Dutta, Soumya; Shen, Han‐Wei; Chen, Jen‐Ping

doi:10.1109/pacificvis.2018.00017

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…For example, sensors on aircraft jet engines, which use kerosene as a combustion fuel, generate hundreds of terabytes (TB) worth of data per flight operation. When this is put into perspective, a single flight alone can potentially generate far more data than what some firms have generated over their lifetime [38].…”

Section: Big Data Analyticsmentioning

confidence: 99%

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Munodawafa

Johl

2019

Sustainability

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Increased greenhouse gas (GHG) emissions in the past decades have created concerns about the environment. To stymie global warming and the deterioration of the natural environment, global CO2 emissions need to reach approximately 1.3 tons per capita by 2050. However, in Malaysia, CO2 output per capita—driven by fossil fuel consumption and energy production—is expected to reach approximately 12.1 tons by the year 2020. GHG mitigation strategies are needed to address these challenges. Cleaner production, through eco-innovation, has the potential to arrest CO2 emissions and buttress sustainable development. However, the cleaner production process has been hampered by lack of complete data to support decision making. Therefore, using the resource-based view, a preliminary study consisting of energy and utility firms is undertaken to understand the impact of big data analytics towards eco-innovation. Linear regression through SPSS Version 24 reveals that big data analytics could become a strong predictor of eco-innovation. This paper concludes that information and data are key inputs, and big data technology provides firms the opportunity to obtain information, which could influence its production process—and possibly help arrest increasing CO2 emissions.

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Section: Big Data Analyticsmentioning

confidence: 99%

Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies

Munodawafa

Johl

2019

Sustainability

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“…Thompson et al [3] approximated the topological structure and fuzzy isosurface by partitioning the data into several histogram representations. Dutta et al [1,2,18] used GMM to represent datasets compactly in an in situ environment. Furthermore, Wang et al [4,15,19] used distributions to compactly store volume, time-varying, ensemble datasets for the post-hoc data analysis and visualization.…”

Section: Related Work 21 Distribution-based Large Data Processing and Analysismentioning

confidence: 99%

“…It can represent a complicated distribution using a few parameters to provide a compact and accurate distribution representation. Therefore, GMM has been widely used to facilitate scientific data reduction and visualization [2,8,[17][18][19]37,38].…”

Section: Gaussian Mixture Model Modeling Using Data-parallel Primitivesmentioning

confidence: 99%

“…This workflow will suffer from storage space limitations and I/O bottlenecks if the dataset size is huge. Therefore, scientists are currently more inclined to process the scientific data in situ [1,2] while the data is still in the supercomputer's memory. The primary purpose of the in situ workflow is to keep only the crucial information for data analysis and reduce the size of data to be written to the hard disk.…”

Section: Introductionmentioning

confidence: 99%

“…Our data-parallel primitive-based algorithms can facilitate the data process that is run on supercomputers with different types of computing nodes. In this work, we propose parallel multi-set distribution modeling algorithms for multi-variant histogram [3,[12][13][14][15] and GMM [1,2,4,[15][16][17] modeling, because these are the most popular non-parametric and parametric distribution representations in the scientific data modeling, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Efficient and Portable Distribution Modeling for Large-Scale Scientific Data Processing with Data-Parallel Primitives

2021

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The use of distribution-based data representation to handle large-scale scientific datasets is a promising approach. Distribution-based approaches often transform a scientific dataset into many distributions, each of which is calculated from a small number of samples. Most of the proposed parallel algorithms focus on modeling single distributions from many input samples efficiently, but these may not fit the large-scale scientific data processing scenario because they cannot utilize computing resources effectively. Histograms and the Gaussian Mixture Model (GMM) are the most popular distribution representations used to model scientific datasets. Therefore, we propose the use of multi-set histogram and GMM modeling algorithms for the scenario of large-scale scientific data processing. Our algorithms are developed by data-parallel primitives to achieve portability across different hardware architectures. We evaluate the performance of the proposed algorithms in detail and demonstrate use cases for scientific data processing.

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