Retida

Enabling real-time, large-scale analytical workflows for data-intensive science requires the integration of state-of-the-art technologies from Big Data (NoSQL solutions and data-intensive programming, streaming engines), HPC (parallel programming, multi-/many-core architectures, GPUs, clusters), data analytics (analytical models and algorithms), and workflow management (definition and orchestration). As the principal investigator of the project, I established the system architecture and developed a large-scale and massively parallel storage and execution platform for data pipelines.
FFG-Austrian Research Promotion Agency, July 2014 – December 2016

The core of the Retida system has been developed at the AIT mobility department to support the analysis of different types of trajectory data. I extended the data analytics pipeline framework with support for massively parallel programming paradigms and Big data storage systems and developed the architecture towards real-time analytics of huge data sets. The framework has been successfully used to conduct data analysis for several clients and research projects.