Progressively consolidating historical visual explorations for new discoveries

Kaiyu Zhao; Matthew O. Ward; Elke A. Rundensteiner; Huong N. Higgins

doi:10.1117/12.2041342

3 February 2014 Progressively consolidating historical visual explorations for new discoveries

Kaiyu Zhao, Matthew O. Ward, Elke A. Rundensteiner, Huong N. Higgins

Proceedings Volume 9017, Visualization and Data Analysis 2014; 90170T (2014) https://doi.org/10.1117/12.2041342
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

A significant task within data mining is to identify data models of interest. While facilitating the exploration tasks, most visualization systems do not make use of all the data models that are generated during the exploration. In this paper, we introduce a system that allows the user to gain insights from the data space progressively by forming data models and consolidating the generated models on the fly. Each model can be a a computationally extracted or user-defined subset that contains a certain degree of interest and might lead to some discoveries. When the user generates more and more data models, the degree of interest of some portion of some models will either grow (indicating higher occurrence) or will fluctuate or decrease (corresponding to lower occurrence). Our system maintains a collection of such models and accumulates the interestingness of each model into a consolidated model. In order to consolidate the models, the system summarizes the associations between the models in the collection and identifies support (models reinforce each other), complementary (models complement each other), and overlap of the models. The accumulated interestingness keeps track of historical exploration and helps the user summarize their findings which can lead to new discoveries. This mechanism for integrating results from multiple models can be applied to a wide range of decision support systems. We demonstrate our system in a case study involving the financial status of US companies.

Citation Download Citation

Kaiyu Zhao, Matthew O. Ward, Elke A. Rundensteiner, and Huong N. Higgins "Progressively consolidating historical visual explorations for new discoveries", Proc. SPIE 9017, Visualization and Data Analysis 2014, 90170T (3 February 2014); https://doi.org/10.1117/12.2041342

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Visualization

Systems modeling

Visual process modeling

Autoregressive models

Data analysis

Process modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years