Big Data Exploration Requires Collaboration Between Visualization and Data Infrastructures
- Danyel Fisher
HILDA 2016: Workshop on Human-In-the-Loop Data Analytics |
Published by ACM
As datasets grow to tera- and petabyte sizes, exploratory data visualization becomes very difficult: a screen is limited to a few million pixels, and main memory to a few tens of millions of data points. Yet these very large scale analyses are of tremendous interest to industry and academia. This paper discusses some of the major challenges involved in data analytics at scale, including issues of computation, communication, and rendering. It identifies techniques for handling large scale data, grouped into “look at less of it,” and “look at it faster.” Using these techniques involves a number of difficult design tradeoffs for both the ways that data can be represented, and the ways that users can interact with the visualizations.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].