Download or read book Query-driven Analysis and Visualization for Large-scale Scientific Dataset Using Geometry Summarization and Bitmap Indexing written by Tzu-Hsuan Wei and published by . This book was released on 2017 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: The computational power of modern supercomputers grows rapidly, and it facilitates scientists to produce high-resolution datasets when simulating physical or weather models, which generate extreme scale data with multiple variables most of the time. However, storage, transmission, or exploration of such large-scale data is challenging. In the past decades, several visualization approaches have been developed to effectively explore datasets by displaying underlying information of datasets. Query-driven visualization is one of the prominent approaches, as it significantly reduces visual exploration time by only focusing on interesting or important features for further analysis and decision making. However, as the size of scientific datasets becomes too large, traditional data exploration approaches become ineffective. An emerging approach is to create data summarizations to first reduce the size of the dataset, and then perform data exploration on the data summarization. An ideal data summarization aims at preserving the characteristics of the raw data as much as possible while keeping the size small. However, to retrieve salient features from the raw data and create such importance-based data summarizations is challenging. In this dissertation, we address the issues that need to be solved when applying query-driven analysis and visualization using data summarizations.