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An Efficient Method for Exploratory Data Visualization of Big Spatial Data on Commodity Hardware

dc.contributor.authorBeilschmidt, Christian
dc.contributor.authorMattig, Michael
dc.contributor.authorFober, Thomas
dc.contributor.authorSeeger, Bernhard
dc.contributor.editorDavid, Klaus
dc.contributor.editorGeihs, Kurt
dc.contributor.editorLange, Martin
dc.contributor.editorStumme, Gerd
dc.date.accessioned2019-08-27T12:55:23Z
dc.date.available2019-08-27T12:55:23Z
dc.date.issued2019
dc.description.abstractThe exploratory and interactive visualization of big spatial data is becoming increasingly important in business, science, and many other application areas. In this paper, we discuss the Circle Merging Quadtree, an efficient method for aggregating and visualizing big spatial point data on commodity hardware.en
dc.identifier.doi10.18420/inf2019_34
dc.identifier.isbn978-3-88579-688-6
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/24982
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-294
dc.subjectData Visualization
dc.subjectBiodiversity Data Analytics
dc.subjectBig Spatial Data Analysis
dc.titleAn Efficient Method for Exploratory Data Visualization of Big Spatial Data on Commodity Hardwareen
dc.typeText/Conference Paper
gi.citation.endPage262
gi.citation.publisherPlaceBonn
gi.citation.startPage261
gi.conference.date23.-26. September 2019
gi.conference.locationKassel
gi.conference.sessiontitleData Science

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