Works (7)
Updated: July 5th, 2023 15:45
2014 journal article
DIRAQ: scalable in situ data- and resource-aware indexing for optimized query performance
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 17(4), 1101–1119.
author keywords: Exascale computing; Indexing; Query processing; Compression
TL;DR:
DIRAQ is proposed, a parallel in situ, in network data encoding and reorganization technique that enables the transformation of simulation output into a query-efficient form, with negligible runtime overhead to the simulation run.
(via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018
2013 journal article
ISABELA for effective in situ compression of scientific data
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 25(4), 524–540.
author keywords: lossy compression; B-spline; in situ processing; data-intensive application; high performance computing
TL;DR:
The random nature of real‐valued scientific datasets renders lossless compression routines ineffective, and these techniques also impose significant overhead during decompression, making them unsuitable for data analysis and visualization, which require repeated data access.
(via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018
2012 conference paper
Byte-precision level of detail processing for variable precision analytics
International conference for high performance computing networking.
TL;DR:
A precision level of detail (APLOD) library is developed, which partitions double-precision datasets along user-defined byte boundaries, and finds a strong applicability for the use of varying degrees of precision to reduce the cost of analyzing extreme-scale data.
(via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018
2012 article
Multi-level Layout Optimization for Efficient Spatio-temporal Queries on ISABELA-compressed Data
2012 IEEE 26TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS), pp. 873–884.
TL;DR:
This work presents a parallel query-processing engine that can handle both range queries and queries with spatio-temporal constraints, on B-spline compressed data with user-controlled accuracy, and shows it to be efficient with respect to storage, computation, and I/O compared to existing database technologies optimized for query processing on scientific data.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
2012 conference paper
On the path to sustainable, scalable, and energy-efficient data analytics: Challenges, promises, and future directions
2012 International Green Computing Conference (IGCC).
TL;DR:
This paper proposes a number of future directions that could be pursued on the path to sustainable data analytics at scale, including transformative approaches to efficient data reduction, analytics-driven query processing, scalable analytical kernels, approximate analytics, among others.
(via Semantic Scholar)
citeseerx.ist.psu.edu (repository)
Source: NC State University Libraries
Added: August 6, 2018
conference paper
ALACRITY: Analytics-driven lossless data compression for rapid in-situ indexing, storing, and querying
Jenkins, J., Arkatkar, I., Lakshminarasimhan, S., Boyuka, D. A., Schendel, E. R., Shah, N., … Samatova, N. F. Transactions on large-scale data- and knowledge- centered systems x: special issue on database- and expert-systems applications, 8220, 95–114.
Source: NC State University Libraries
Added: August 6, 2018
report
Compressing the Incompressible with ISABELA: In-situ Reduction of Spatio-Temporal Data
Lakshminarasimhan, S., Shah, N., Ethier, S. J., Klasky, S., Latham, R., Ross, R., & N.F., S. In Technical Report- Not held in TRLN member libraries.
Source: NC State University Libraries
Added: August 6, 2018
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