att_abstract={{Consider real-time exploration of large multi-dimensional spatiotemporal datasets with billions of entries, each defined by a location, a time, and other attributes. Are certain attributes correlated spatially or temporally? Are there trends or outliers in the data? Answering these questions requires aggregation over arbitrary regions of the domain and attributes of the data. Data cubes are a well-known aggregration operation in relational databases. In a sense, they precompute every possible aggregate query over the database. Data cubes are sometimes assumed to take a prohibitively large amount of space, and to consequently require disk storage. In contrast, we show how to construct a data cube that fits in a modern laptop’s main memory, even for billions of entries; we call this data structure a nanocube. We present algorithms to compute and query a nanocube, and show how it can be used to generate well-known visual encodings such as heatmaps, histograms, and parallel coordinate plots. When compared to exact visualizations created by scanning the entire datasets, nanocube plots have bounded screen error across a variety of scales, thanks to its hierarchical structure in space and time. We demonstrate the effectiveness of our technique on a variety of real-world datasets, and present memory, timing, and network bandwidth measurements. We find that the timings for the queries in our examples are dominated by network and user-interaction latencies.}},
	att_authors={ll447y, jk140f, cs929g},
	att_categories={C_BB.1, C_IIS.7},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2013 {{, 2013-10-01}}
	att_projects={Information_Visualization, IV_INFOVIS},
	att_tags={data cube, data structures, interactive exploration},
	author={Lauro Lins and James Klosowski and Carlos Scheidegger},
	institution={{IEEE InfoVis}},
	title={{Nanocubes for Real-Time Exploration of Spatiotemporal Datasets}},