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In introduction to HCI class.
SwiftTuna

Incrementally Exploring Large-scale Multidimensional Data

Jaemin Jo, Wonjae Kim, Seunghoon Yoo, Bohyoung Kim, and Jinwook Seo / 2017

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PARTICIPANTS

  • Jaemin Jo, Seoul National University, Seoul, Republic of Korea
  • Wonjae Kim, Seoul National University, Seoul, Republic of Korea
  • Seunghoon Yoo, Seoul National University, Seoul, Republic of Korea
  • Bohyoung Kim, Hankuk University of Foreign Studies, Yongin-si, Republic of Korea
  • Jinwook Seo, Seoul National University, Seoul, Republic of Korea

ABSTRACT

The advance in distributed computing technologies opens up new possibilities of data exploration even for datasets with a few billion entries. In this paper, we present SwiftTuna, an interactive system that brings in modern cluster computing technologies (i.e., in-memory computing) to InfoVis, allowing rapid and incremental exploration of large-scale multidimensional data without building precomputed data structures (e.g., data cubes). Our performance evaluation demonstrates that SwiftTuna enables data exploration of a real-world dataset with four billion records while preserving the latency between incremental responses within a few seconds.

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SUPPORT

This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIP) (No. NRF-2014R1A2A2A03006998 and NRF-2016R1A2B2007153) and by the Hankuk University of Foreign Studies Research Fund of 2016. Bohyoung Kim and Jinwook Seo are the corresponding authors.

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