VIDEO ABSTRACT.

Abstract-Neuroscientists increasingly use computational tools to build and simulate models of the brain. The amounts of data involved in these simulations are immense and efficiently managing this data is key.One particular problem in analyzing this data is the scalable execution of range queries on spatial models of the brain. Known indexing approaches do not perform well, even on today's small models containing only few million densely packed spatial elements. The problem of current approaches is that with the increasing level of detail in the models, the overlap in the tree structure also increases, ultimately slowing down query execution. The neuroscientists' need to work with bigger and more importantly, with increasingly detailed (denser) models, motivates us to develop a new indexing approach.To this end we developed FLAT, a scalable indexing approach for dense data sets. We based the development of FLAT on the key observation that current approaches suffer from overlap in case of dense data sets. We hence designed FLAT as an approach with two phases, each independent of density.Our experimental results confirm that FLAT achieves independence from data set size as well as density and also outperforms R-Tree variants in terms of I/O overhead from a factor of two up to eight. I. INTRODUCTIONScientists in various disciplines increasingly use computational tools to simulate, process and analyze experimental data. Computational tools make it substantially simpler for them to conduct scientific tasks. At the same time however, scientists are also increasingly buried in the data deluge produced by their tools. Being able to access the relevant parts of their data, i.e., their spatial models, quickly in order to analyze, understand, and prepare new experiments is pivotal for them.In this paper we thus develop a new index that efficiently supports scientists in executing range queries on dense data sets stemming from increasingly detailed spatial models.The work presented in this paper is motivated by our collaboration with the Blue Brain Project (BBP [17]). With data acquired in anatomical research on the cortex of the rat brain the neuroscientists in the BBP build biophysically realistic models, the most detailed computer models of the brain to date, for simulation based research in neuroscience. The project began by focusing on the elementary building block of the neocortex, a neocortical column of about 10,000 neurons. Morphologically speaking, each of these neurons has branches extending into large parts of the tissue in order to receive and send out information to other neurons. Figure 1 (left) shows a cell morphology, with cylinders modeling the branching of the dendrite and axon in three dimensions.

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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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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Abstract-Scientists in many disciplines use spatial mesh models to study physical phenomena. Simulating natural phenomena by changing meshes over time helps to understand and predict future behavior of the phenomena. The higher the precision of the mesh models, the more insight do the scientists gain and they thus continuously increase the detail of the meshes and build them as detailed as their instruments and the simulation hardware allow. In the process, the data volume also increases, slowing down the execution of spatial range queries needed to monitor the simulation considerably. Indexing speeds up range query execution, but the overhead to maintain the indexes is considerable because almost the entire mesh changes unpredictably at every simulation step. Using a simple linear scan, on the other hand, requires accessing the entire mesh and the performance deteriorates as the size of the dataset grows.In this paper we propose OCTOPUS, a strategy for executing range queries on mesh datasets that change unpredictably during simulations. In OCTOPUS we use the key insight that the mesh surface along with the mesh connectivity is sufficient to retrieve accurate query results efficiently. With this novel query execution strategy, OCTOPUS minimizes index maintenance cost and reduces query execution time considerably. Our experiments show that OCTOPUS achieves a speedup between 7.2 and 9.2× compared to the state of the art and that it scales better with increasing mesh dataset size and detail.