David Arnu

The INFORE project addresses the challenge of processing streams of large datasets and pave the way for real-time, interactive extreme-scale analytics and forecasting. Today, at an increasing rate, industrial and scientific institutions need to deal with massive data flows, streaming-in from a multitude of sources. For instance, maritime surveillance applications need to combine high-velocity data streams, including position data from thousands of ships across the world and acoustic signals of autonomous, unmanned vehicles. Life-Science simulation data and finance transaction data are another source of very fast and volatile data streams. Our work aims to simplify the design of complex process workflows . This allows subject experts to analyze large data streams and bridge the gap between data science and high performance computing. The proposed architecture is system agnostic and supports modern architectures, like Apache Flink, Kafka and Spark.