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16.09 - Comparative Knowledge Discovery: Analytizing, Understanding and Visualizing Rankings

Project - Summary


The overall objective of this research is to develop new methods for comparative knowledge discovery for ranking - specifically enabling decision makers to better compare objects through both new ranking methods and interactive visualization methods. The overall project has four specific objectives:

  • Develop a scalable partial ordering based ranking method
  • Develop data-driven approaches (supervised and unsupervised) to learn to rank
  • Implement a web-based visualization and interaction methods for comparative knowledge discovery

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Raju Gottumukkala PI (318) 680-5886 UL Lafayette
Vijay Raghavan PI (337) 280-8451 UL Lafayette
Mehmet Tozal PI (337) 852-8289 UL Lafayette
Moncef Gabbouj PI 358 40 073 6613 Tampere University
Alexandros Iosifidis PI +45 9350 8875 Tampere University
Amirhossein Tavanaei Graduate Student Not available Not available UL Lafayette
Siva Venna Graduate Student Not available Not available UL Lafayette
Guanqun Cao Graduate Student Not available Not available Tampere University

Project - Impact and Uses/Benefits

Ranking is a very classical problem that is very relevant to each and every organization - as organizations rank departments, products, clients, or geographical regions for important decisions. Yet, ranking - for example in situations like KPI dashboards use a composite index with linear weighted models to compare objects; While existing visualization dashboards do provide drill down capabilities, they do not offer deeper understanding with respect to how well the objects compare with each other particularly conflicts or missing information. The proposed approaches offer clear benefits in terms of: (1) Reducing manual effort involved in developing weights for ranking by experts that would save time and money, (2) Offers deeper insights into how objects are ordered for people to make better decisions in government and industry domains that could potentially save costs and improve the performance of decision making in the context of ranking.

Project - Deep Dive

Project - Documents

FilenameFilesizeLast modified
16.09_year_5_presentation.pdf405.1 KiB2019/08/22 13:21
16.09_year_5_poster.pptx464.5 KiB2019/08/22 13:21
16.09_year_5_final_report.pdf2.2 MiB2019/08/22 10:47
projects/year5/16.09.txt · Last modified: 2021/06/02 15:36 by sally.johnson