Practical lessons in supporting large-scale computational science
ACM SIGMOD Record
Approximate ad-hoc query engine for simulation data
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Critical Database Technologies for High Energy Physics
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Statistical modeling of large-scale simulation data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-resolution modeling of large scale scientific simulation data
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
The framework for approximate queries on simulation data
Information Sciences—Informatics and Computer Science: An International Journal
A hybrid approach for multiresolution modeling of large-scale scientific data
Proceedings of the 2005 ACM symposium on Applied computing
Driver input selection for main-memory multi-way joins
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Computational or scientific simulations are increasingly being applied to solve a variety of scientific problems. Domains such as astrophysics, engineering, chemistry, biology, and environmental studies are benefiting from this important capability. Simulations, however, produce enormous amounts of data that need to be analyzed and understood. In this overview paper, we describe scientific simulation data, its characteristics, and the way scientists generate and use the data. We then compare and contrast simulation data to data streams. Finally, we describe our approach to analyzing simulation data, present the AQSim (Ad-hoc Queries for Simulation data) system, and discuss some of the challenges that result from handling this kind of data.