|
|
|
@inproceedings{DBLP:conf/sigmod/LivnyRBCDLMW97a,
author = {Miron Livny and
Raghu Ramakrishnan and
Kevin S. Beyer and
Guangshun Chen and
Donko Donjerkovic and
Shilpa Lawande and
Jussi Myllymaki and
R. Kent Wenger},
editor = {Joan Peckham},
title = {DEVise: Integrated Querying and Visual Exploration of Large Datasets
(Demo Abstract)},
booktitle = {SIGMOD 1997, Proceedings ACM SIGMOD International Conference
on Management of Data, May 13-15, 1997, Tucson, Arizona, USA},
publisher = {ACM Press},
year = {1997},
pages = {517-520},
ee = {http://doi.acm.org/10.1145/253260.253379, db/conf/sigmod/LivnyRBCDLMW97a.html},
crossref = {DBLP:conf/sigmod/97},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
DEVise is a data exploration system that allows users to easily develop, browse, and share visual presentations of large tabular datasets (possibly containing or referencing multimedia objects) from several sources. The DEVise framework, implemented in a tool that has been already successfully applied to a variety of real applications by a number of user groups, makes several contributions. In particular, it combines support for extended relational queries with powerful data visualization features. Datasets much larger than available main memory can be handled - DEVise is currently being used to visualize datasets well in excess of lOOMB - and data can be interactively examined at several levels of detail: all the way from meta-data summarizing the entire dataset, to large subsets of the actual data, to individual data records. Combining querying (in general, data processing) with visualization gives us a very versatile tool, and presents several novel challenges.
Our emphasis is on developing an intuitive yet powerful set of querying and visualization primitives that can be easily combined to develop a rich set of visual presentations that integrate data from a wide range of application domains. In this demo, we will present a number of examples of the use of the DEVise tool for visualizing and interactively exploring very large datasets, and report on our experience in applying it to several real applications.
Copyright © 1997 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.
CDROM Version: Load the CDROM "Volume 1 Issue 1, SIGMOD '93-'97" and ...