ArticleAdvances in database technology : proceedings. International Conference on Extending Database Technology2012
Distance Histogram Computation Based on Spatiotemporal Uniformity in Scientific Data.
Article in Advances in database technology : proceedings. International Conference on Extending Database Technology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Concurrent query processing in a GPU-based database system.PloS one · 2019Article
- Computing Spatial Distance Histograms for Large Scientific Datasets On-the-Fly.IEEE transactions on knowledge and data engineering · 2014Article
- Efficient SDH Computation In Molecular Simulations Data.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2012Article
- Approximate Algorithms for Computing Spatial Distance Histograms with Accuracy Guarantees.IEEE transactions on knowledge and data engineering · 2012Article
- Distance Histogram Computation Based on Spatiotemporal Uniformity in Scientific Data.Advances in database technology : proceedings. International Conference on Extending Database Technology · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Large data generated by scientific applications imposes challenges in storage and efficient query processing. Many queries against scientific data are analytical in nature and require super-linear computation time using straightforward methods. Spatial distance histogram (SDH) is one of the basic queries to analyze the molecular simulation (MS) data, and it takes quadratic time to compute using brute-force approach. Often, an SDH query is executed continuously to analyze the simulation system over a period of time. This adds to the total time required to compute SDH. In this paper, we propose an approximate algorithm to compute SDH efficiently over consecutive time periods. In our approach, data is organized into a Quad-tree based data structure. The spatial locality of the particles (at given time) in each node of the tree is acquired to determine the particle distribution. Similarly, the temporal locality of particles (between consecutive time periods) in each node is also acquired. The spatial distribution and temporal locality are utilized to compute the approximate SDH at every time instant. The performance is boosted by storing and updating the spatial distribution information over time. The efficiency and accuracy of the proposed algorithm is supported by mathematical analysis and results of extensive experiments using biological data generated from real MS studies.
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Registered trials
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