Big Data Analytics Debrief

1. Big Data Analytics Debrief Rahul Ramachandran, Morris Reidel rahul.ramachandran@uah.edu

2. Objectives • Clarify some foundational terminologies in the context of data analytics understanding differences/overlaps with terms like data analysis, data mining, etc. • Systematically analyze different specific scientific domain data analytics needs and their potential use of various big data analytics techniques. • Develop a recommendation documents with a systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries. • Recommendation documents can serve as a best practice guide for scientific groups/communities interested in investing in Big Data technologies

3. Breakout Session Focus • Introduction • Terminology refocus • What does it enable that is new, different? • Use Case Presentations • Atmospheric Science – (Volume, Interactivity) Event Analytics • Astronomy – (Volume, Real time) Anomaly Detection • Bioinformatics – (Heterogeneity, Interactivity) • Linguistics – (Unstructured, Complex datasets) • Technology Presentation • Array based Databases • Update on NIST Big Data activities

4. Next Steps • Flesh out “use case” template (started) • Capture all the use cases (started) • RDA host a variety of “benchmark big data sets” • Similar UCI Machine Learning Datasets • Flesh out “analysis methodology/technology” template (started) • Survey and capture all analysis methodologies/technologies (started) • Mapping between use cases and analysis methodology/technology (to be done by the 3rd Plenary) • Added a new co-chair – Peter Baumann

5. Process Survey Survey Technologies Use Cases Template Template Technology Classification Use Case Classification Mapping Recommendation Document