Segmented In-advance Data Analytics for Fast Scientific Discovery

Scientific discovery usually involves data generation, data preprocessing, data storage and data analysis. As the data volume exceeds a few terabytes (TB) in a single simulation run, the data movement, which happens during each cycle of the scientific discovery, continues to be the bottleneck in most scientific big data applications. A lot of research works have been conducted on reducing the data movement. Among the existing efforts and based on our previous research, reusing the analysis results shows a significant potential in optimizing the data movement between analysis operations. In this work, we propose the Segmented InAdvance (SIA) data analytics approach for optimizing the data movement and we also provide a cloud-based elastic distributed in-memory database to manage the intermediate analysis results. The fundamental idea of this Segmented In-Advance approach is to analyze the history operations and to predict the future interesting analytics operations. The predicted analysis operation is in-advance performed on the finer segmented dataset and the segmented results are distributed in an in-memory key-value store for future reuse. The evaluation shows that the segmented in-advance data analytics approach achieves 1.2X-6.1X speedup. The evaluation also shows a good scalability of the in-memory distributed data store. The proposed Segmented In-Advance data analytics approach is a promising data movement reduction solution for scientific big data applications and fast scientific discovery. Keywords-segmented in-advance data analytics, big data, data intensive computing, scientific computing