Exploiting locality for query processing and compression in scientific databases
Proceedings of the Fourth SIGMOD PhD Workshop on Innovative Database Research
Performance analysis of a dual-tree algorithm for computing spatial distance histograms
The VLDB Journal — The International Journal on Very Large Data Bases
Distance histogram computation based on spatiotemporal uniformity in scientific data
Proceedings of the 15th International Conference on Extending Database Technology
Efficient SDH computation in molecular simulations data
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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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. In this paper, we propose a novel algorithm to compute SDH based on a data structure called density map, which can be easily implemented by augmenting a Quad-tree index. We also show the results of rigorous mathematical analysis of the time complexity of the proposed algorithm: our algorithm runs on O(N^{3/ 2}) for two-dimensional data and O(N^{5/3}) for three-dimensional data, respectively. We also propose an approximate SDH processing algorithm whose running time is unrelated to the input size N. Experimental results confirm our analysis and show that the approximate SDH algorithm achieves very high accuracy.