Diversity Aware Visualization

PhD candidate Sajan Raj Ojha

April 20, 2018
Versione stampabile

Time: h 09:00 am
Location: Room Ofek, Polo Ferrari 1 - Via Sommarive 5, Povo (TN) 

PhD Candidate

  • Sajan Raj Ojha

Abstract of Dissertation

This thesis aims to address a significant issue related with the consumption of diversified data in the field of semantics and knowledge representation by using a framework which allows the data consumption in a generic, scalable and pleasing manner. The work proposes a  multi-layered solution by splitting the issue into two independent subproblems: how to preserve the richness associated with the data; how to present and explore information about an object in a single or multiple visualization contexts with the better user experience.

A real-world object can have various representations which lead to data diversity. However, each representation captures a view (mostly partial) of an object. To preserve the richness associated with the data, we follow an entity-centric design approach. In this approach, we represent multiple datasets related to an object as an entity with various properties. An entity is then further categorized in a group according to its similarities or differences.
Our contextual model not only considers the transformation of objects as entities but also adapts to various visualization contexts. These contexts are space, list, timeline, and network. We design a multiview visualization framework that allows simultaneous presentation of entities according to these four defined visualization contexts.

To allow seamless interaction of data with the users, we emphasized on using a multilayered architecture where: 1) datasets are aggregated and stored using an entity-centric approach, 2) visualized in various contexts and viewpoints simultaneously according to the entity types and users' need. This adaptation is capable enough to facilitate presentation and exploration of diversified data according to users need. The mapping function does the filtering operation on the entities based on users' needs.

To prove the feasibility of our framework, we applied it to visualize diversified data in various settings. Continuous interaction with the end users produced valuable feedback and essential design suggestions. Finally, multiple prototypes were evaluated with the end users to verify their usability. The results obtained were highly favorable.