EAGER: Scalable Big Data Analytics

As the data sizes reach unprecedented scales with high velocity, variety and volume, informatics requires bridging the gap between data-intensive computing and data-driven computing to obtain actionable insights. The former has primarily focused on optimizing data movement, reuse, organization and storage, while the latter has focused on hypothesis-driven, bottom-up data-to-discovery. Our overarching goal is to create a holistic Ecosystem, or a virtuous cycle that • optimizes data generation from simulations, sensors, or business processes (Transaction Step); • organizes this data (possibly combining with other data) to enable reduction, pre-processing for downstream data analysis (Organization Step); • performs knowledge discovery, learning and mining models from this data (Prediction Step); and • leads to actions (e.g., refining models, new experiments, recommendation) (Feedback Step). Thus, the entire Ecosystem is streamlined for timely actionable insights from massive data.