Visual Analytics for Item Ranking Creation

Background

Identifying good items in large data sets can be difficult, epecially for data sets consisting of a large number of items where each item itself has a large number of attributes, so called multi-dimensional items. One strategy to cope with such data sets is to apply a ranking strategy on the data set. Rankings of items enable users to a) select a top candidate item, b) focus on a small set of highly preferable items, or c) identify items that are ranked particularly weak.

The human-centered ranking of multi-dimensional items is a non-trivial task. Comparing complex items to each other in order to create a ranking can be time-consuming and especially if data sets are large, there is no guarantee that the result is satisfying.

Approach

TBD

Thesis Goal

TBD

Requirements

• Programming experience in Python or willingness to learn. Alternative: Java
• Decent understanding of written English.