Arithmetic coding for data compression
Communications of the ACM
Large scale distributed data repository: design of a molecular dynamics trajectory database
Future Generation Computer Systems
Understanding Molecular Simulation
Understanding Molecular Simulation
Histogram-Based Approximation of Set-Valued Query-Answers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Approximate query processing using wavelets
The VLDB Journal — The International Journal on Very Large Data Bases
An Algebraic Compression Framework for Query Results
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Compression
The Center for Plasma Edge Simulation Workflow Requirements
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Astronomical Image and Data Analysis (Astronomy and Astrophysics Library)
Astronomical Image and Data Analysis (Astronomy and Astrophysics Library)
Computing Distance Histograms Ef?ciently in Scientific Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
BioSimGrid: Grid-enabled biomolecular simulation data storage and analysis
Future Generation Computer Systems - Collaborative and learning applications of grid technology
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Improvements in the efficiency of scientific simulations have lead to requirements of large databases. The data captured in such simulations is of large scale and poses challenges in storage, transfer and query processing. However, the data are collected every fraction of a second, storing some redundant information. Thus, the temporal and spatial locality of the data gives us an opportunity to store and transfer over networks efficiently. The data locality also helps in efficiently processing complex analytical queries that are popular in scientific databases. Many scientific data analysis queries involve more than one object/body of interest. Processing such queries pose super-linear computational complexity. In this paper, we propose preliminary solutions to some of these problems along with initial results. Mainly, we try to exploit the spatial and temporal proximity of the data to achieve high levels of compression for efficient storage and analytical query processing.