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Query-driven visualization has been applied to efficiently analyze and visualize large-scale data set by focusing on a smaller subset of raw data. In order to reduce data exploration time, scientists usually only focus on the interesting or important part of data that matches on some specified criteria for further analysis and decision making. Through highlighting a part of raw data, it constraints the computational complexity of data visualization and provides a much faster data exploration. In order to rapidly retrieve the subset of data queried by the user, query-driven visualization usually incorporates particular data structures, such as tree or indexing data structure. In GRAVITY lab, we are developing novel approaches to provide efficient and qualitative query-driven data analysis and visualization.
Publications:
Publications:
- Tzu-Hsuan Wei, Chun-Ming Chen, Jonathan Woodring, Huijie Zhang, Han-Wei Shen: Efficient distribution-based feature search in multi-field datasets. PacificVis 2017: 121-130
- Kewei Lu, Han-Wei Shen: A compact multivariate histogram representation for query-driven visualization. LDAV 2015: 49-56
- Tzu-Hsuan Wei, Chun-Ming Chen and Ayan Biswas: Efficient Local Histogram Searching via Bitmap Indexing, Computer Graphics Forum. Vol. 34, No. 3, 2015
- Abon Chaudhuri, Tzu-Hsuan Wei, Teng-Yok Lee, Han-Wei Shen, Tom Peterka: Efficient Range Distribution Query for Visualizing Scientific Data. PacificVis 2014: 201-208
- Steven Martin, Han-Wei Shen: Transformations for volumetric range distribution queries. PacificVis 2013: 89-96
- Ying Tu, Han-Wei Shen: GraphCharter: Combining browsing with query to explore large semantic graphs. PacificVis 2013: 49-56
- Kewei Lu, Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, Pak Chung Wong: Exploring vector fields with distribution-based streamline analysis. PacificVis 2013: 257-264
- Abon Chaudhuri, Teng-Yok Lee, Han-Wei Shen, Tom Peterka: Efficient range distribution query in large-scale scientific data. LDAV 2013: 125-126
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