DEVise (demo abstract): integrated querying and visual exploration of large datasets

ABSTRACT

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 100MB—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 visualizations 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.

REFERENCES

• 1: José A. Blakeley, Data access for the masses through OLE DB, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.161-172, June 04-06, 1996, Montreal, Quebec, Canada
• 2: A. Silberschatz and S. Z. et al., editors. Proc. NSF Workshop on Strategic Directions in Computing, Cambridge, MA, 1996.
• 3: Tian Zhang , Raghu Ramakrishnan , Miron Livny, BIRCH: an efficient data clustering method for very large databases, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.103-114, June 04-06, 1996, Montreal, Quebec, Canada

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT

General Terms:
Design, Management, Performance, Theory