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Processing Large Raster and Vector Data in Apache Spark

dc.contributor.authorHagedorn, Stefan
dc.contributor.authorBirli, Oliver
dc.contributor.authorSattler, Kai-Uwe
dc.contributor.editorGrust, Torsten
dc.contributor.editorNaumann, Felix
dc.contributor.editorBöhm, Alexander
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorHärder, Theo
dc.contributor.editorRahm, Erhard
dc.contributor.editorHeuer, Andreas
dc.contributor.editorKlettke, Meike
dc.contributor.editorMeyer, Holger
dc.date.accessioned2019-04-11T07:21:35Z
dc.date.available2019-04-11T07:21:35Z
dc.date.issued2019
dc.description.abstractSpatial data processing frameworks in many cases are limited to vector data only. However, an important type of spatial data is raster data which is produced by sensors on satellites but also by high resolution cameras taking pictures of nano structures, such as chips on wafers. Often the raster data sets become large and need to be processed in parallel on a cluster environment. In this paper we demonstrate our STARK framework with its support for raster data and functionality to combine raster and vector data in filter and join operations. To save engineers from the burden of learning a programming language, queries can be formulated in SQL in a web interface. In the demonstration, users can use this web interface to inspect examples of raster data using our extended SQL queries on a Apache Spark cluster.en
dc.identifier.doi10.18420/btw2019-43
dc.identifier.isbn978-3-88579-683-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/21730
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2019
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) – Proceedings, Volume P-289
dc.titleProcessing Large Raster and Vector Data in Apache Sparken
gi.citation.endPage554
gi.citation.startPage551
gi.conference.date4.-8. März 2019
gi.conference.locationRostock
gi.conference.sessiontitleDemonstrationen

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