Visual analysis of stream data

Michael Cheng; Miron Livny; Raghu Ramakrishnan

doi:10.1117/12.205942

7 April 1995 Visual analysis of stream data

Michael Cheng, Miron Livny, Raghu Ramakrishnan

Proceedings Volume 2410, Visual Data Exploration and Analysis II; (1995) https://doi.org/10.1117/12.205942
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1995, San Jose, CA, United States

Abstract

We present the DEVise (data exploration via visualization environment) toolkit designed for visual exploration of stream data. Data of this type are collected continuously from sources such as remote sensors, program traces, and the stock market. A typical application involves looking for correlations, which may not be precisely defined, by experimenting with graphical representations. This includes selectively comparing data from multiple sources, selective viewing by zooming and scrolling at various resolutions, and querying the underlying data from the graphics. DEVise is designed to provide greater support than packages such as AVS or Khoros for this type of application. First, by abandoning the network flow model of AVS and Khoros in favor of a database query model, we are able to incorporate many performance improvements for visualizing large amounts of data. To our knowledge, this is the first attempt to eliminate data size limitations in a visualization package. Second, by structuring the stand-alone graphics module of most existing tools into user accessible components, users can quickly create, destroy, or interconnect the components to generate new visualizations. This flexibility greatly increases the ease with which users can browse their data. Finally, through limited programming, users can query the underlying data through the graphical representation for more information about the records used to generate the graphical representation.

Citation Download Citation

Michael Cheng, Miron Livny, and Raghu Ramakrishnan "Visual analysis of stream data", Proc. SPIE 2410, Visual Data Exploration and Analysis II, (7 April 1995); https://doi.org/10.1117/12.205942

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