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VDDA: automatic visualization-driven data aggregation in relational databases


Contemporary RDBMS-based systems for visualization of high-volume numerical data have difficulty to cope with the hard latency requirements and high ingestion rates of interactive visualizations. Existing solutions for lowering the volume of large data sets disregard the spatial properties of visualizations, resulting in visualization errors. In this work, we introduce VDDA, a visualization-driven data aggregation that models visual aggregation at the pixel level as data aggregation at the query level. Based on the M4 aggregation for producing pixel-perfect line charts from highly reduced data subsets, we define a complete set of data reduction operators that simulate the overplotting behavior of the most frequently used chart types. Relying only on the relational algebra and the common data aggregation functions, our approach is generic and applicable to any visualization system that consumes data stored in relational databases. We demonstrate our visualization-driven data aggregation using real-world data sets from high-tech manufacturing, stock markets, and sports analytics, reducing data volumes by up to two orders of magnitude, while preserving pixel-perfect visualizations, as producible from the raw data.

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  1. 1.

    We use the relational algebra notations \(\pi \) for projection, \(\sigma \) for selection, and \(_{[GroupFunction]^{*}}G_{[Aggregation]^+}\) or \(G_{[GroupKey|Aggregation]^+}\) for aggregation.

  2. 2.

    Given w equally sized groups of a continuous range, we obtain \(2\cdot w\) equally sized groups by adding intersections in the center of each group. The original intersections of the value range and thus their corresponding first and last tuples are still part of the query result. Similarly, the original min and max tuples become min or max tuples in the new subgroups.


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Correspondence to Uwe Jugel.

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Jugel, U., Jerzak, Z., Hackenbroich, G. et al. VDDA: automatic visualization-driven data aggregation in relational databases. The VLDB Journal 25, 53–77 (2016).

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  • Relational databases
  • Data aggregation
  • Visual aggregation
  • Dimensionality reduction
  • Data visualization
  • Line rasterization
  • Overplotting