2017 IEEE Pacific Visualization Symposium (PacificVis) (2017)
Seoul, South Korea
April 18, 2017 to April 21, 2017
Jaemin Jo , Seoul National University, South Korea
Wonjae Kim , Seoul National University, South Korea
Seunghoon Yoo , Seoul National University, South Korea
Bohyoung Kim , Hankuk University of Foreign Studies, South Korea
Jinwook Seo , Seoul National University, South Korea
For interactive exploration of large-scale data, a preprocessing scheme (e.g., data cubes) has often been used to summarize the data and provide low-latency responses. However, such a scheme suffers from a prohibitively large amount of memory footprint as more dimensions are involved in querying, and a strong prerequisite that specific data structures have to be built from the data before querying. In this paper, we present SwiftTuna, a holistic system that streamlines the visual information seeking process on large-scale multidimensional data. SwiftTuna exploits an in-memory computing engine, Apache Spark, to achieve both scalability and performance without building precomputed data structures. We also present a novel interactive visualization technique, tailed charts, to facilitate large-scale multidimensional data exploration. To support responsive querying on large-scale data, SwiftTuna leverages an incremental processing approach, providing immediate low-fidelity responses (i.e., prompt responses) as well as delayed high-fidelity responses (i.e., incremental responses). Our performance evaluation demonstrates that SwiftTuna allows data exploration of a real-world dataset with four billion records while preserving the latency between incremental responses within a few seconds.
Data visualization, Visualization, Scalability, Data structures, Sparks, Data processing, Distributed databases
Jaemin Jo, Wonjae Kim, Seunghoon Yoo, Bohyoung Kim and J. Seo, "SwiftTuna: Responsive and incremental visual exploration of large-scale multidimensional data," 2017 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Seoul, South Korea, 2017, pp. 131-140.