Abstract
Simulations have become key in many scientific disciplines to better understand natural phenomena. Neuroscientists, for example, build and simulate increasingly fine-grained models (including subcellular details, e.g., neurotransmitter) of the neocortex to understand the mechanisms causing brain diseases and to test new treatments in-silico.
The sheer size and, more importantly, the level of detail of their models challenges today’s spatial data management techniques. In collaboration with the Blue Brain project (BBP) we develop new approaches that efficiently enable analysis, navigation and discovery in spatial models of the brain. More precisely, we develop an index for the scalable and efficient execution of spatial range queries supporting model building and analysis. Furthermore, we enable navigational access to the brain models, i.e., the execution of of series of range queries where he location of each query depends on the previous ones. To efficiently support navigational access, we develop a method that uses previous query results to prefetch spatial data with high accuracy and therefore speeds up navigation. Finally, to enable discovery based on the range queries, we conceive a novel in-memory spatial join.
The methods we develop considerably outperform the state of the art, but more importantly, they enable the neuroscientists to scale to building, simulating and analyzing massively bigger and more detailed brain models.
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References
Gnanakaran, S., Nymeyer, H., Portman, J., Sanbonmatsu, K.Y., Garcia, A.E.: Peptide folding simulations. Current Opinion in Structural Biology 13(2), 168–174 (2003)
Gray, J., Szalay, A., Thakar, A., Kunszt, P., Stoughton, C., Slutz, D., Vandenberg, J.: Data Mining the SDSS SkyServer Database. Technical Report, MSR-TR-2002-01, Microsoft Research (2002)
Komatitsch, D., Tsuboi, S., Ji, C., Tromp, J.: A 14.6 Billion Degrees of Freedom, 5 Teraflops, 2.5 Terabyte Earthquake Simulation on the Earth Simulator. In: International Conference on Supercomputing, SC 2003 (2003)
Markram, H.: The Blue Brain Project. Nature Reviews Neuroscience 7(2), 153–160 (2006)
Gaede, V., Guenther, O.: Multidimensional Access Methods. ACM Computing Surveys 30(2) (1998)
Guttman, A.: R-trees: a Dynamic Index Structure for Spatial Searching. In: SIGMOD 1984 (1984)
Kozloski, J., Sfyrakis, K., Hill, S., Schürmann, F., Peck, C., Markram, H.: Identifying, Tabulating, and Analyzing Contacts Between Branched Neuron Morphologies. IBM Journal of Research and Development 52(1/2), 43–55 (2008)
Brinkhoff, T., Kriegel, H., Seeger, B.: Efficient Processing of Spatial Joins R-Trees. In: SIGMOD 1993 (1993)
Lo, M.L., Ravishankar, C.V.: Spatial Joins Using Seeded Trees. In: SIGMOD 1994 (1994)
Koudas, N., Sevcik, K.C.: Size Separation Spatial Join. In: SIGMOD 1997 (1997)
Patel, J.M., DeWitt, D.J.: Partition Based Spatial-Merge Join. In: SIGMOD 1996 (1996)
Tauheed, F., Biveinis, L., Heinis, T., Schürmann, F., Markram, H., Ailamaki, A.: Accelerating range queries for brain simulations. In: ICDE 2012 (2012)
Tauheed, F., Heinis, T., Schürmann, F., Markram, H., Ailamaki, A.: Scout: Prefetching for latent structure following queries. In: VLDB 2012 (2012)
Nobari, S., Tauheed, F., Heinis, T., Karras, P., Bressan, S., Ailamaki, A.: TOUCH: In-Memory Spatial Join by Hierarchical Data-Oriented Partitioning. In: SIGMOD 2013 (2013)
Sellis, T.K., Roussopoulos, N., Faloutsos, C.: The R+-Tree: A Dynamic Index for Multi-Dimensional Objects. In: VLDB 1987 (1987)
Leutenegger, S.T., Lopez, M.A., Edgington, J.: STR: a Simple and Efficient Algorithm for R-tree Packing. In: ICDE 1997 (1997)
Arge, L., Berg, M.D., Haverkort, H., Yi, K.: The Priority R-tree: A Practically Efficient and Worst-case Optimal R-Tree. ACM Transactions on Algorithms 4(1), 1–30 (2008)
Kamel, I., Faloutsos, C.: Hilbert R-Tree: An Improved R-Tree using Fractals. In: VLDB 1994 (1994)
Park, D.J., Kim, H.J.: Prefetch policies for large objects in a web-enabled gis application. Data & Knowledge Engineering 37(1), 65–84 (2001)
Chan, A., Lau, R.W.H., Si, A.: A motion prediction method for mouse-based navigation. In: International Conference on Computer Graphics, pp. 139–146.
Lee, D., Kim, J., Kim, S., Kim, K., Yoo-Sung, K., Park, J.: Adaptation of a Neighbor Selection Markov Chain for Prefetching Tiled Web GIS Data. In: Yakhno, T. (ed.) ADVIS 2002. LNCS, vol. 2457, pp. 213–222. Springer, Heidelberg (2002)
Chim, J., et al.: On caching and prefetching of virtual objects in distributed virtual environments. In: MULTIMEDIA 1998 (1998)
Mishra, P., Eich, M.H.: Join Processing in Relational Databases. ACM Computing Surveys 24(1), 63–113 (1992)
Edelsbrunner, H.: Algorithms in combinatorial geometry. Springer (1987)
Jacox, E.H., Samet, H.: Spatial join techniques. ACM TODS 32(1), Article 7, 44 (2007)
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Tauheed, F., Nobari, S., Biveinis, L., Heinis, T., Ailamaki, A. (2013). Computational Neuroscience Breakthroughs through Innovative Data Management. In: Catania, B., Guerrini, G., Pokorný, J. (eds) Advances in Databases and Information Systems. ADBIS 2013. Lecture Notes in Computer Science, vol 8133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40683-6_2
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DOI: https://doi.org/10.1007/978-3-642-40683-6_2
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