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Institute
Multi-part Nanocubes
(2016)
This thesis describes the development of Multi-part Nanocubes. It is a further development of Nanocubes, an in-memory data structure for spatiotemporal data cubes. "Nanocubes provides you with real-time visualization of large datasets. Slice and dice your data with respect to space, time, or some of your data attributes, and view the results in real-time on a web browser over heatmaps, bar charts, and histograms." Partitioning the structure to parallelize the build process as well as merging query results is the principal part of this document. Furthermore, a new memory management (slab allocation with offset pointers) was implemented to enable 32-bit support and faster load times of already built nanocubes. Porting the project to Windows and implementing on-the-fly compression and decompression of nanocube files is also described.
