TY - GEN
T1 - Computational neuroscience breakthroughs through innovative data management
AU - Tauheed, Farhan
AU - Nobari, Sadegh
AU - Biveinis, Laurynas
AU - Heinis, Thomas
AU - Ailamaki, Anastasia
PY - 2013/9/5
Y1 - 2013/9/5
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-642-40683-6_2
DO - 10.1007/978-3-642-40683-6_2
M3 - Article in proceeding
AN - SCOPUS:84883266303
SN - 978-3-642-40682-9
T3 - Lecture Notes in Computer Science
SP - 14
EP - 27
BT - Advances in Databases and Information Systems
A2 - Catania, Barbara
A2 - Guerrini, Giovanna
A2 - Pokorný, Jaroslav
PB - Springer Publishing Company
T2 - 17th East European Conference, ADBIS 2013
Y2 - 1 September 2013 through 4 September 2013
ER -