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December 2016

8th TUC Meeting - Martin Zand University of Rochester Clinical and Translational Science Institute). Graphing Healthcare Networks: Data, Analytics and Use Cases

  • Posted on: 27 December 2016
  • By: Adrian Diaz

Martin Zand, Professor of Medicine and Public Health Sciences at the Rochester enter for Health Informatics, switched the focus of the presentations talking as a user of graph databases. Zand pinpointed the relevance of using graph in healthcare comparing 3 characteristics of healthcare to their counterpart with graphs:

  • Healthcare is delivered by networks.
  • Patients traverse those networks.
  • The topology of the networks influences outcomes.

The talk of Dr. Zand was structured around the presentation of 3 uses cases:

8th TUC Meeting - Tim Hegeman (TU Delft). Social Network Benchmark, Analytics workload.

  • Posted on: 23 December 2016
  • By: Adrian Diaz

Tim Hegeman from TU Delft presented a very interesting talk about Social Network Benchmark analytics. Graphalytics is a benchmark developed by TU Delft for graph analytics, complex and holistic graph computations.

As per today, over 100 graph analytics systems exist, Hegeman explains, but they’re not comprehensive and there's where Graphalytics excels. It consists on algorithms and datasets (workload) that have been selected using a 2-stage process to ensure the representativity of the workload. The stages of the process were:

8th TUC Meeting - Social Network Benchmark: Interactive Workload

  • Posted on: 20 December 2016
  • By: Adrian Diaz

Arnau Prat, Lead Researcher at DAMA-UPC from the Technological University of Catalonia presented a talk on the Interactive Workload of the Social Network Benchmark. One of the key aspects of his talk was the introduction of the SNB Data Generator, tool that generates a Facebook-degree social network distribution (groups, posts, likes…). This synthetic social network follows the principle of homophily, isn’t uniform and allows a fair comparison and reproducibility of benchmark executions while being also scalable by using Apache Hadoop.