Large scale distributed data repository: design of a molecular dynamics trajectory database

Feig, Michael; Abdullah, Matin; Johnsson, Lennart; Pettitt, B. Montgomery

doi:10.1016/s0167-739x(99)00039-4

Cited by 26 publications

(22 citation statements)

References 3 publications

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“…Recent efforts have been dedicated to building simulation data management systems on top of relational databases, as represented by the BioSimGrid [4] and SimDB [24] projects developed for MSs. However, such systems are still in short of efficient query processing strategies.…”

Section: Related Work and Our Contributionsmentioning

confidence: 99%

Approximate Algorithms for Computing Spatial Distance Histograms with Accuracy Guarantees

Grupcev

Yuan

et al. 2013

IEEE Trans. Knowl. Data Eng.

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Particle simulation has become an important research tool in many scientific and engineering fields. Data generated by such simulations impose great challenges to database storage and query processing. One of the queries against particle simulation data, the spatial distance histogram (SDH) query, is the building block of many high-level analytics, and requires quadratic time to compute using a straightforward algorithm. Previous work has developed efficient algorithms that compute exact SDHs. While beating the naive solution, such algorithms are still not practical in processing SDH queries against large-scale simulation data. In this paper, we take a different path to tackle this problem by focusing on approximate algorithms with provable error bounds. We first present a solution derived from the aforementioned exact SDH algorithm, and this solution has running time that is unrelated to the system size N. We also develop a mathematical model to analyze the mechanism that leads to errors in the basic approximate algorithm. Our model provides insights on how the algorithm can be improved to achieve higher accuracy and efficiency. Such insights give rise to a new approximate algorithm with improved time/accuracy tradeoff. Experimental results confirm our analysis.

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Section: Related Work and Our Contributionsmentioning

confidence: 99%

Approximate Algorithms for Computing Spatial Distance Histograms with Accuracy Guarantees

Grupcev

Yuan

et al. 2013

IEEE Trans. Knowl. Data Eng.

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“…The management of such large amounts of data, in particular, permanent storage and network transfer, is a major challenge. This is an especially serious issue hindering efforts to develop public simulation databases, such as SimDB [4], Dynameomics [5], the Ascona B-DNA Consortium [6], BioSimGrid [7], and Molecular Dynamics Extended Library [8]. …”

Section: Introductionmentioning

confidence: 99%

Molecular Dynamics Trajectory Compression with a Coarse-Grained Model

Cheng

Gopal

Law

et al. 2012

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Molecular dynamics trajectories are very data-intensive thereby limiting sharing and archival of such data. One possible solution is compression of trajectory data. Here, trajectory compression based on conversion to the coarse-grained model PRIMO is proposed. The compressed data is about one third of the original data and fast decompression is possible with an analytical reconstruction procedure from PRIMO to all-atom representations. This protocol largely preserves structural features and to a more limited extent also energetic features of the original trajectory.

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“…As a result, scientific data management has gained much momentum in the database research community. Recent years have witnessed increasing interest in developing database systems for the management of scientific data [11,13,15,19,23,33,39,40]. While taking advantage of the optimized I/O and querying power of relational DBMSs, such systems still fall short of algorithms and strategies to satisfy the special needs of scientific applications, which are very different from those in traditional databases in their data types and query patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Scientific data analysis often requires computation of mathematical (statistical) functions [11,17] whose complexity goes beyond simple aggregates, which are the only analytics supported by modern DBMSs. Many complex analytics in scientific applications are found to be hierarchical in that they are often defined on top of a small number of low-level analytics as building blocks.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of a dual-tree algorithm for computing spatial distance histograms

Chen

Xia

2010

The VLDB Journal

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Many scientific and engineering fields produce large volume of spatiotemporal data. The storage, retrieval, and analysis of such data impose great challenges to database systems design. Analysis of scientific spatiotemporal data often involves computing functions of all point-to-point interactions. One such analytics, the Spatial Distance Histogram (SDH), is of vital importance to scientific discovery. Recently, algorithms for efficient SDH processing in large-scale scientific databases have been proposed. These algorithms adopt a recursive tree-traversing strategy to process point-to-point distances in the visited tree nodes in batches, thus require less time when compared to the brute-force approach where all pairwise distances have to be computed. Despite the promising experimental results, the complexity of such algorithms has not been thoroughly studied. In this paper, we present an analysis of such algorithms based on a geometric modeling approach. The main technique is to transform the analysis of point counts into a problem of quantifying the area of regions where pairwise distances can be processed in batches by the algorithm. From the analysis, we conclude that the number of pairwise distances that are left to be processed decreases exponentially with more levels of the tree visited. This leads to the proof of a time complexity lower than the quadratic time needed for a brute-force algorithm and builds the foundation for a constant-time approximate algorithm. Our model is also general in that it works for a wide range of point spatial distributions, histogram types, and space-partitioning options in building the tree.

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Large scale distributed data repository: design of a molecular dynamics trajectory database

Cited by 26 publications

References 3 publications

Approximate Algorithms for Computing Spatial Distance Histograms with Accuracy Guarantees

Approximate Algorithms for Computing Spatial Distance Histograms with Accuracy Guarantees

Molecular Dynamics Trajectory Compression with a Coarse-Grained Model

Performance analysis of a dual-tree algorithm for computing spatial distance histograms

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