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The majority of spatial processing techniques rely heavily on the idea of approximating each group of spatial objects by their minimum bounding box (MBB). As each MBB is compact to store (requiring only two multi-dimensional points) and intersection tests ...
Efficiently querying multiple spatial data sets is a growing challenge for scientists. Astronomers query data sets that contain different types of stars (e.g., dwarfs, giants, stragglers) while neuroscientists query different data sets that model different ...
With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can ...
2018
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Advances in data acquisition—through more powerful supercomputers for simulation or sensors with better resolution—help scientists tremendously to understand natural phenomena. At the same time, however, it leaves them with a plethora of data and the chall ...
A recent PVLDB paper reports on experimental analyses of ten spatial join techniques in main memory. We build on this comprehensive study to raise awareness of the fact that empirical running time performance findings in main-memory settings are results of ...
The skyline is an important query operator for multi-criteria decision making. It reduces a dataset to only those points that offer optimal trade-offs of dimensions. In general, it is very expensive to compute. Recently, multicore CPU algorithms have been ...
