Visual exploration of large data sets

By Daniel A. Keim. Communications of the ACM, 2001

326 citations recorded by the source corpus.

Data is often recorded, captured, and stored automatically via sensors and monitoring systems. Many of the simple transactions now part of our everyday lives, such as paying for food and clothes by credit card or using the telephone, are typically recorded for future reference by computers. Many parameters of each transaction are routinely captured, resulting in highly dimensional data. The data is collected because companies, including those engaged in some kind of e-commerce, view it as a source of potentially valuable information that, as a strategic asset, could provide a competitive advantage. But actually finding this valuable information is difficult. Today’s data management systems make it possible to view only small portions of it. If the data is presented in text form, the amount that can be displayed amounts to only about 100 data items—a drop in the ocean when dealing with data sets containing millions of data items. Lacking the ability to adequately explore the large amounts being collected, and despite its potential usefulness, the data becomes useless and the databases data dumps. Visual data exploration, which aims to provide insight by visualizing the data, and information visualization techniques (such as distorted overview displays and dense pixel displays) can help solve this problem. Effective data mining depends on having a human in the data exploration process while combining this person’s flexibility, creativity, and general knowledge with the enormous storage capacity and computational power of today’s computers. Visual data exploration seeks to integrate humans in the data exploration process, applying their perceptual abilities to the large data sets now available. The basic idea is to present the data in some visual form, allowing data analysts to gain insight into it and draw conclusions, as well as interact with it. The visual representation of the data reduces the cognitive work needed to perform certain tasks. Visual data mining techniques have proved their value in exploratory data analysis; they also have great Computer systems today store vast amounts of data. Researchers, including those working on the “How Much Information?” project at the University of California, Berkeley, recently estimated, about 1 exabyte (1 million terabytes) of data is generated annually worldwide, including 99.997% available only in digital form. This worldwide data deluge means that in the next three years, more data will be generated than during all previous human history. Visual Exploration of Large Data Sets

Subfields: Computer Vision and Pattern Recognition, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/69361340d0afe89e2fb8db1ae24dbc44b59e8b3c

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Citations in the corpus

Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “This led to visual data exploration and visual data mining[64].”
Analysis of biomedical data with multilevel glyphs (2014, methodology): “Visual data exploration methods on large data sets were described by several authors, and particularly Keim (2001) [21], Hege et al.”
An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health (2018, background): “This led to visual data exploration and visual data mining [29] and widened considerably the scope of applications of visualization, statistics, and data mining—the three pillars of analytics.”
Information Visualization and Visual Data Mining (2002, unknown): “This is an extended version of [ 6 ], portions of which are copyrighted by ACM.”