View maintenance in a warehousing environment
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Emerging trends in business analytics
Communications of the ACM - Evolving data mining into solutions for insights
Anton, a special-purpose machine for molecular dynamics simulation
Communications of the ACM - Web science
Scalable probabilistic databases with factor graphs and MCMC
Proceedings of the VLDB Endowment
Large science databases - are cloud services ready for them?
Scientific Programming - Science-Driven Cloud Computing
Towards a unified architecture for in-RDBMS analytics
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
The Art of Molecular Dynamics Simulation
The Art of Molecular Dynamics Simulation
DBToaster: higher-order delta processing for dynamic, frequently fresh views
Proceedings of the VLDB Endowment
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Molecular dynamics (MD) simulations generate detailed time-series data of all-atom motions. These simulations are leading users of the world's most powerful supercomputers, and are standard-bearers for a wide range of high-performance computing (HPC) methods. However, MD data exploration and analysis is in its infancy in terms of scalability, ease-of-use, and ultimately its ability to answer 'grand challenge' science questions. This demonstration introduces the Molecular Dynamics Database (MDDB) project at Johns Hopkins, to study the co-design of database methods for deep on-the-fly exploratory MD analyses with HPC simulations. Data exploration in MD suffers from a "human bottleneck", where the laborious administration of simulations leaves little room for domain experts to focus on tackling science questions. MDDB exploits the data-rich nature of MD simulations to provide adaptive control of the exploration process with machine learning techniques, specifically reinforcement learning (RL). We present MDDB's data and queries, architecture, and its use of RL methods. Our audience will co-operate with our steering algorithm and science partners, and witness MDDB's abilities to significantly reduce exploration times and direct computation resources to where they best address science questions.