Accelerating Range Queries for Brain Simulations

Tauheed, Farhan; Biveinis, Laurynas; Heinis, Thomas; Schürmann, Felix; Markram, Henry; Ailamaki, Anastasia

doi:10.1109/icde.2012.56

Cited by 34 publications

(32 citation statements)

References 20 publications

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“…The GIPSY [4] spatial join uses data-oriented partitioning but minimizes the impact of overlap by using a crawling strategy [8], [9]. GIPSY is efficient for joining a sparse dataset with a dense one.…”

Section: A Data-oriented Partitioningmentioning

confidence: 99%

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TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Pavlović

Heinis

Tauheed³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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Abstract-Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio among the joined datasets. More generally, no single proposed method can efficiently join two spatial datasets in a robust manner with respect to their data distributions. Some approaches do well for datasets with contrasting densities while others do better with similar densities. None of them does well when the datasets have locally divergent data distributions.In this paper we develop TRANSFORMERS, an efficient and robust spatial join approach that is indifferent to such variations of distribution among the joined data. TRANSFORM-ERS achieves this feat by departing from the state-of-the-art through adapting the join strategy and data layout to local density variations among the joined data. It employs a join method based on data-oriented partitioning when joining areas of substantially different local densities, whereas it uses big partitions (as in space-oriented partitioning) when the densities are similar, while seamlessly switching among these two strategies at runtime. We experimentally demonstrate that TRANSFORMERS outperforms state-of-the-art approaches by a factor of between 2 and 8.

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Section: A Data-oriented Partitioningmentioning

confidence: 99%

“…The area a used for navigation is called pivot. Once TRANSFORMERS arrives at the location of a pivot a, it uses crawling based on the connectivity information [8], [9] to detect all spatial elements of the follower that intersect with pivot a, and then continues exploration towards a neighboring area in the guide dataset.…”

Section: Transformers Overviewmentioning

confidence: 99%

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Pavlović

Heinis

Tauheed³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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“…The considerable time spent on intersection tests within the tree structure degrades performance and indicates overlap of the bounding boxes, a well known problem of the R-Tree [10] and data-oriented tree structures. With increasing density of the spatial datasets, the overlap will also increase in future datatsets [29], thereby further increasing the intersection tests in the tree structure.…”

Section: Potential For Improvementmentioning

confidence: 99%

Block

Olma

Tauheed²,

Heinis

et al. 2017

Proceedings of the 29th International Conference on Scientific and Statistical Database Management

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The execution of spatial range queries is at the core of many applications, particularly in the simulation sciences but also in many other domains. Although main memory in desktop and supercomputers alike has grown considerably in recent years, most spatial indexes supporting the efficient execution of range queries are still only optimized for disk access (minimizing disk page reads). Recent research has primarily focused on the optimization of known disk-based approaches for memory (through cache alignment etc.) but has not fundamentally revisited index structures for memory.In this paper we develop BLOCK, a novel approach to execute range queries on spatial data featuring volumetric objects in main memory. Our approach is built on the key insight that in-memory approaches need to be optimized to reduce the number of intersection tests (between objects and query but also in the index structure). Our experimental results show that BLOCK outperforms known in-memory indexes as well as in-memory implementations of disk-based spatial indexes up to a factor of 7. The experiments show that it is more scalable than competing approaches as the data sets become denser.

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“…To enable the neuroscientists in the BBP to build and analyze models of the brain on an unprecedented detailed level, we develop FLAT [12] with a two phased query execution at its core. The key insight we use is that while finding all elements in a particular range query in an R-Tree-like index suffers from overlap, finding an arbitrary element in a range query on the other hand is independent of overlap and therefore is a comparatively cheap operation.…”

Section: Flat Query Executionmentioning

confidence: 99%

“…We describe the three approaches we develop, FLAT [12] for efficient range query execution, SCOUT [13] for the accurate prefetching of spatial data and TOUCH [14] for efficient and scalable in-memory joins, discuss how neuroscientists use them and we demonstrate the considerable impact they have on the process of building the models.…”

Section: Introductionmentioning

confidence: 99%

Computational Neuroscience Breakthroughs through Innovative Data Management

Tauheed

Nobari

Biveinis

et al. 2013

Advances in Databases and Information Systems

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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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Accelerating Range Queries for Brain Simulations

Cited by 34 publications

References 20 publications

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Block

Computational Neuroscience Breakthroughs through Innovative Data Management

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