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1
Randal Burns,
Kunal Lillaney,
Daniel R. Berger,
Logan Grosenick,
Karl Deisseroth,
R. Clay Reid,
William Gray Roncal,
Priya Manavalan,
Davi D. Bock,
Narayanan Kasthuri,
Michael Kazhdan,
Stephen J. Smith,
Dean Kleissas,
Eric Perlman,
Kwanghun Chung,
Nicholas C. Weiler,
Jeff Lichtman,
Alexander S. Szalay,
Joshua T. Vogelstein,
R. Jacob Vogelstein
July 2013
SSDBM: Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 4, Downloads (12 Months): 41, Downloads (Overall): 250
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We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes ---neural connectivity maps of the brain---using the parallel ...
Keywords:
connectomics, data-intensive computing
2
January 2012
Scientists are increasingly finding themselves in a paradoxical situation: on a never-ending quest to collect data, they are collecting more data than they can handle. This growth in data comes from three main areas: better instrumentation, improved simulations, and increased data sharing between scientists. This work describes a variety of ...
3
November 2011
SC '11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis
Publisher: ACM
Bibliometrics:
Citation Count: 2
Downloads (6 Weeks): 1, Downloads (12 Months): 8, Downloads (Overall): 191
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We describe a method for evaluating computational turbulence queries, including Lagrange Polynomial interpolation, based on partial sums that allows the underlying data to be accessed in any order and in parts. We exploit these properties to stream data from disk in a single pass and concurrently evaluate batch queries. The ...
Keywords:
data-intensive computing, query evaluation, I/O streaming, database clusters, software for high-throughput computing, query optimization
4
November 2010
SC '10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Publisher: IEEE Computer Society
Bibliometrics:
Citation Count: 4
Downloads (6 Weeks): 1, Downloads (12 Months): 3, Downloads (Overall): 128
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We present JAWS, a job-aware, data-driven batch scheduler that improves query throughput for data-intensive scientific database clusters. As datasets reach petabyte-scale, workloads that scan through vast amounts of data to extract features are gaining importance in the sciences. However, acute performance bottlenecks result when multiple queries execute simultaneously and compete ...
5
June 2010
SSDBM'10: Proceedings of the 22nd international conference on Scientific and statistical database management
Publisher: Springer-Verlag
We present a technique for organizing data in spatial databases with non-convex domains based on an automatic characterization using the medial-axis transform (MAT). We define a tree based on the MAT and enumerate its branches to partition space and define a linear order on the partitions. This ordering clusters data ...
6
August 2008
Proceedings of the VLDB Endowment: Volume 1 Issue 2, August 2008
Publisher: VLDB Endowment
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0, Downloads (12 Months): 1, Downloads (Overall): 62
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We demonstrate data indexing and query processing techniques that improve the efficiency of comparing, correlating, and joining data contained in non-convex regions. We use computational geometry techniques to automatically characterize the region of space from which data are drawn, partition the region based on that characterization, and create an index ...
7
November 2007
SC '07: Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Publisher: ACM
Bibliometrics:
Citation Count: 15
Downloads (6 Weeks): 1, Downloads (12 Months): 17, Downloads (Overall): 221
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We describe a new environment for the exploration of turbulent flows that uses a cluster of databases to store complete histories of Direct Numerical Simulation (DNS) results. This allows for spatial and temporal exploration of high-resolution data that were traditionally too large to store and too computationally expensive to produce ...
8
November 2006
SC '06: Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0, Downloads (12 Months): 0, Downloads (Overall): 46
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We describe a new environment for large-scale turbulence simulations that uses a cluster of database nodes to store the complete space-time history of fluid velocities. This allows for rapid access to high resolution data that were traditionally too large to store and too computationally expensive to produce on demand.We perform ...
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