Efficient feature exploration in large-scale data sets using traditional post-hoc analysis approaches is becoming prohibitive due to the bottleneck stemming from I/O and output data sizes. This problem becomes more ch...
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ISBN:
(纸本)9781538614242
Efficient feature exploration in large-scale data sets using traditional post-hoc analysis approaches is becoming prohibitive due to the bottleneck stemming from I/O and output data sizes. This problem becomes more challenging when an ensemble of simulations are required to run for studying the influence of input parameters on the model output. As a result, scientists are inclining more towards analyzing the data in situ while it resides in the memory. In situ analysis aims at minimizing expensive data movement while maximizing the resource utilization for extraction of important information from the data. In this work, we study the evolution of rotating stall in jet engines using data generated from a large-scale flow simulation under various input conditions. Since the features of interest lack a precise descriptor, we adopt a fuzzy rule-based machinelearning algorithm for efficient and robust extraction of such features. For scalable exploration, we advocate for an off-line learning and in situ prediction driven strategy that facilitates in-depth study of the stall. Task-specific information estimated in situ is visualized interactively during the post-hoc analysis revealing important details about the inception and evolution of stall. We verify and validate our method through comprehensive expert evaluation demonstrating the efficacy of our approach.
Data scientists often time had to spend more time and effort to resolve common problems such as over-plotting when visualizing IoT/big data. This research will focus on the exploratory study on visualization on IoT da...
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ISBN:
(纸本)9781538626672
Data scientists often time had to spend more time and effort to resolve common problems such as over-plotting when visualizing IoT/big data. This research will focus on the exploratory study on visualization on IoT data, its issues, related works and feasibility of building a general framework for data visualization in IoT data. This paper contains literature reviews to proceed for the development of the framework. Although this paper does not present any results, it gives an overview of what has been done so far in regards to IoT data visualization and application of deep learning in this research area. This paper will highlight the challenges of IoT data visualization, Visual Analytics (VA), its misconception and use cases and where deep/machinelearning can be applied in VA and lastly, Decanter AI and how it motivates the development of a general data visualization framework. Further discussions include evidence of the research gap, the need for a general framework and how a future work may be done.
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