NDLI: Model-driven Visual Analytics

Content Provider	IEEE Xplore Digital Library
Author	Garg, S. Nam, J.E. Ramakrishnan, I.V. Mueller, K.
Copyright Year	2008
Description	Author affiliation: Comput. Sci. Dept., Stony Brook Univ., Stony Brook, NY (Garg, S.; Nam, J.E.; Ramakrishnan, I.V.; Mueller, K.)
Abstract	We describe a visual analytics (VA) infrastructure, rooted on techniques in machine learning and logic-based deductive reasoning that will assist analysts to make sense of large, complex data sets by facilitating the generation and validation of models representing relationships in the data. We use logic programming (LP) as the underlying computing machinery to encode the relations as rules and facts and compute with them. A unique aspect of our approach is that the LP rules are automatically learned, using Inductive Logic Programming, from examples of data that the analyst deems interesting when viewing the data in the high-dimensional visualization interface. Using this system, analysts will be able to construct models of arbitrary relationships in the data, explore the data for scenarios that fit the model, refine the model if necessary, and query the model to automatically analyze incoming (future) data exhibiting the encoded relationships. In other words it will support both model-driven data exploration, as well as data-driven model evolution. More importantly, by basing the construction of models on techniques from machine learning and logic-based deduction, the VA process will be both flexible in terms of modeling arbitrary, user-driven relationships in the data as well as readily scale across different data domains.
Starting Page	19
Ending Page	26
File Size	3398101
Page Count	8
File Format	PDF
ISBN	9781424429356
DOI	10.1109/VAST.2008.4677352
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data analysis Visual Analytics H.5.2 [Information Interfaces and Presentation]: User Interfaces—Graphical user interfaces Logic programming Data security Humans Scattering Knowledge Discovery Network Security Grand Tour Visual Clustering Machinery Machine Learning High-dimensional Data Visual analytics I.2.6 [Artificial Intelligence]: Learning—Concept Learning Data visualization Information security Machine learning I.5.3 [Pattern Recognition]: Clustering—Similarity Measures
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Analyzing delays in trajectories

Sequencing of categorical time series

Supporting exploration awareness for visual analytics

Evaluating the relationship between user interaction and financial visual analysis

CEDARS: Combined exploratory data analysis recommender system

Grand challenge award: Interactive visual analytics palantir: The future of analysis

Integrative visual analytics for suspicious behavior detection

Anomaly detection in GPS data based on visual analytics

Adapting Daniel and Wood's modeling approach to interactive visual analytics

Model-driven Visual Analytics

Similar Documents

Analyzing delays in trajectories

Sequencing of categorical time series

Supporting exploration awareness for visual analytics

Evaluating the relationship between user interaction and financial visual analysis

CEDARS: Combined exploratory data analysis recommender system

Grand challenge award: Interactive visual analytics palantir: The future of analysis

Integrative visual analytics for suspicious behavior detection

Anomaly detection in GPS data based on visual analytics

Adapting Daniel and Wood's modeling approach to interactive visual analytics

Model-driven Visual Analytics