No abstract

No abstract

In this work, we analyze content and structure of the Twitter trending topic #cuentalo with the purpose of providing a visualization of the movement. A supervised learning methodology is used to train the classifying algorithms with hand-labeled observations. The methodology allows us to classify each tweet according to its role in the movement.

We designed and implemented a parallel visualisation system for the analysis of large scale time-dependent particle type data. The particular challenge we address is how to analyse a high performance computation style dataset when a visual representation of the full set is not possible or useful, and one is only interested in finding and inspecting smaller subsets that fulfil certain complex criteria. We used Paraview as the user interface, which is a familiar tool for many HPC users, runs in parallel, and can be conveniently extended. We distributed the data in a supercomputing environment using the Hadoop file system. On top of it, we run Hive or Impala, and implemented a connection between Paraview and them that allows us to launch programmable SQL queries in the database directly from within Paraview. The queries return a Paraview-native VTK object that fits directly into the Paraview pipeline. We find good scalability and response times. In the typical supercomputer environment (like the one we used for implementation) the queue and management system make it difficult to keep local data in between sessions, which imposes a bottleneck in the data loading stage. This makes our system most useful when permanently installed on a dedicated cluster.

No abstract