Preserving Recomputability of Results from Big Data Transformation Workflows
| dc.contributor.author | Kricke, Matthias | |
| dc.contributor.author | Grimmer, Martin | |
| dc.contributor.author | Schmeißer, Michael | |
| dc.contributor.editor | Mitschang, Bernhard | |
| dc.contributor.editor | Nicklas, Daniela | |
| dc.contributor.editor | Leymann, Frank | |
| dc.contributor.editor | Schöning, Harald | |
| dc.contributor.editor | Herschel, Melanie | |
| dc.contributor.editor | Teubner, Jens | |
| dc.contributor.editor | Härder, Theo | |
| dc.contributor.editor | Kopp, Oliver | |
| dc.contributor.editor | Wieland, Matthias | |
| dc.date.accessioned | 2017-06-21T11:24:39Z | |
| dc.date.available | 2017-06-21T11:24:39Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | The ability to recompute results from raw data at any time is important for data-driven companies to ensure data stability and to selectively incorporate new data into an already delivered data product. When external systems are used or data changes over time this becomes even more challenging. In this paper, we propose a system architecture which ensures recomputability of results from big data transformation workflows on internal and external systems by using distributed key-value data stores. | en |
| dc.identifier.isbn | 978-3-88579-660-2 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-266 | |
| dc.subject | BigData | |
| dc.subject | recomputability | |
| dc.subject | bitemporality | |
| dc.subject | time-to-consistency | |
| dc.title | Preserving Recomputability of Results from Big Data Transformation Workflows | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 236 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 227 | |
| gi.conference.date | 6.-10. März 2017 | |
| gi.conference.location | Stuttgart | |
| gi.conference.sessiontitle | Scalable Cloud Data Management Workshop (SCDM 2017) |
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