Steven P. Reiss
ABSTRACT
BLOOM is a system for doing software understanding through visualization. It provides facilities for static and dynamic data collection. It offers a wide range of data anal?yses. It includes a visual query language for specifying what information should be visualized. All these are used in con?junction with a back end that supports a variety of 2D and 3D visualization strategies.
- 1.Kenneth W. Church and Jonathan I. Helfman, "Dotplot: a program for exploring self-similarity in millions of lines for text and code," Journal of Computational and Graphical Statistics Vol. 2 pp. 153-174 (1993).Google Scholar
Cross Ref
- 2.Stephen G. Eick, Joseph L. Steffen, and Eric E. Sumner, Jr., "Seesoft - a tool for visualizing software," AT&T Bell Laboratories (1991).Google Scholar
- 3.Steven P. Reiss, "Cacti: a front end for program visualization," IEEE Symp. on Information Visualization, pp. 46-50 (October 1997). Google ScholarDigital Library
- 4.Steven P. Reiss, "Software visualization in the Desert environment," Proc. PASTE '98, pp. 59-66 (June 1998). Google ScholarDigital Library
- 5.Steven P. Reiss, "The Desert environment," ACM TOSEM Vol. 8(4) pp. 297-342 (October 1999). Google ScholarDigital Library
- 6.Steven P. Reiss and Manos Renieris, "Generating Java trace data," Proc Java Grande, (June 2000). Google ScholarDigital Library
- 7.Steven P. Reiss and Manos Renieris, "Encoding program executions," Proc ICSE 2001, (May 2001). Google ScholarDigital Library
- 8.Ben Schneiderman, "Tree visualization with tree-maps: a 2-D space-filling approach," ACM Transactions on Graphics Vol. 11(1) pp. 92-99 (January 1992). Google ScholarDigital Library
Index Terms
- An overview of BLOOM
Recommendations
Interactive 4D overview and detail visualization in augmented reality
ISMAR '12: Proceedings of the 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)In this paper we present an approach for visualizing time-oriented data of dynamic scenes in an on-site AR view. Visualizations of time-oriented data have special challenges compared to the visualization of arbitrary virtual objects. Usually, the 4D ...
Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets
AbstractScientific visualizations are the foundation for communicating results and findings to a variety of audiences. As the creation of novel and large environmental datasets has grown, this has necessitated new schemes and recommendations ...
Highlights- We discuss the challenges of visualizing large environmental datasets.
- We ...
Comments