Data Analytics and Management
Data Analytics and Management is the branch of computer science that is concerned with extracting usable information from data. At LLNL, we’re working with data in many forms: text, images, videos, semantic graphs, and more. This data may be “at rest” in files or databases, or “in motion” as it streams in from sensors or other live sources. Our informatics research aims to gain insight from data that is very large, geographically distributed, complex, fast moving, or some combination of these characteristics. Applications for this work span a wide range of LLNL missions, including energy security and efficiency, biosecurity, computer security, and climate change. View content related to Data Analytics and Management.
More than 35 members of LLNL’s Computation Directorate will attend the 2017 Supercomputing Conference. The Laboratory’s presence includes tutorials, poster and paper sessions, and the Job Fair.
The first-ever LLNL Developer Day event included Lightning Talks, Deep Dives, a discussion panel, and plenty of snacks.
AIMS (Analytics and Informatics Management Systems) develops integrated cyberinfrastructure for big climate data discovery, analytics, simulations, and knowledge innovation.
The latest hackathon event promotes cross-directorate collaboration and grassroots ideas.
The increasing size of scientific datasets presents challenges for fully exploring the data and evaluating an analysis approach. In the IDEALS project, statistical and machine learning techniques are combined to give scientists and data analysts the same level of confidence in the analysis of large-scale data as they have in the analysis of small datasets, resulting in improved scientific and policy decisions.
Simulation workflows for Arbitrary Lagrangian–Eulerian (ALE) methods are highly complex and often require a manual tuning process. There is an urgent need to semi-automate this process to reduce user burden and improve productivity. To address this need, we are developing novel predictive analytics for simulations and an in situ infrastructure for integration of analytics. Our ongoing goals are to predict simulation failures ahead of time and proactively avoid them as much as possible.