Visual Analytics Architecture for large table-based datasets

Master thesis project by Juan Camilo Ortiz Román working under the supervision of John Alexis Guerra Gómez

Tadava Sequence Diagram


Design and test a technique that allows visual analytics tools to sample, summarize and explore big datasets in a web context.

Problem Statement

Visual Analytics provide the user with tools to process data in a very intuitive way. One of the challenges Visual Analytics face nowadays is the need to represent big amounts of information in a way that the user can explore. This large amounts of data can not be managed by conventional machines and must be partitioned or underrepresented. This thesis project presents a technique of representative sampling for large table-based datasets.


  • Open source Tadava Code implementing sampling algorithms through Elasticsearch queries.
  • Modified Navio code to be used with Tadava.
  • Backend architecture that will connect with Navio.

Github Poster Thesis Document Thesis Article Slides