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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1670/paper-06.pdf</pdf>
<pre>
         Visualization-Driven Data Aggregation


                        Uwe Jugel1 and Zbigniew Jerzak2
                       1
                           LOVOO GmbH, Dresden, Germany
                                uwe.jugel@lovoo.com
                             2
                               SAP SE, Berlin, Germany
                              zbigniew.jerzak@sap.com

     Visual analysis of high-volume, numerical data is traditionally required for
understanding sensor data in manufacturing and engineering scenarios. However,
today the visual analysis of any kind of big data has become ubiquitous and is a
most-wanted feature for visual analytics tools, required for commerce, nance,
sales, and an ever-growing number of industries, whose data is prevalently stored
in a relational database management system (RDBMS).
     Unfortunately, contemporary RDBMS-based data visualization and analysis
systems have diculties to cope with the hard latency requirements and high
ingestion rates required for interactive visualizations of big data. These systems
are particularly not able to eectively sample or aggregate the data, inevitably
failing to visualize the millions of acquired records. A general-purpose solution for
visualization-related data reduction in RDBMS-based systems has been missing.
Thereby, custom solutions are tailored to specic domains, supporting only a
few custom types of visualizations, while general approaches to data reduction
disregard the spatial properties of data visualizations, resulting in measurable
and perceivable visualization errors.
     To facilitate truly interactive visualizations of the growing volume and variety
of big data, computer systems need to change the way they acquire data for the
purpose of data visualizations. Visualization-Driven Data Aggregation (VDDA)
facilitates up to error-free visualizations of high-volume data sets, at high data
reduction rates. Dened as data reduction at the query level and leveraged in
a transparent query rewriting system, VDDA is applicable to any visualization
system that consumes data stored in relational databases.
     This is a resubmission of previously published papers by Jugel et al. [1,2].

Keywords:     data visualization, data aggregation, relational databases, query
processing, sampling methods

References
1. U. Jugel, Z. Jerzak, G. Hackenbroich, and V. Markl. M4: A visualization-oriented
   time series data aggregation. PVLDB, 7(10):797808, 2014. (VLDB Best Paper).
2. U. Jugel, Z. Jerzak, G. Hackenbroich, and V. Markl.            VDDA: Automatic
   visualization-driven data aggregation in relational databases. The VLDB Journal,
   25(1):5377, February 2016.

</pre>