A Comparison of Distributed Stream Processing Systems for Time Series Analysis
| dc.contributor.author | Gehring, Melissa | |
| dc.contributor.author | Charfuelan, Marcela | |
| dc.contributor.author | Markl, Volker | |
| dc.contributor.editor | Meyer, Holger | |
| dc.contributor.editor | Ritter, Norbert | |
| dc.contributor.editor | Thor, Andreas | |
| dc.contributor.editor | Nicklas, Daniela | |
| dc.contributor.editor | Heuer, Andreas | |
| dc.contributor.editor | Klettke, Meike | |
| dc.date.accessioned | 2019-04-15T11:40:34Z | |
| dc.date.available | 2019-04-15T11:40:34Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Given the vast number of data processing systems available today, in this paper, we aim to identify, select, and evaluate systems to determine the one that is better suited to use in conducting time series analysis. Published studies of performance are used to compare several open-source systems, and two systems are further selected for qualitative comparison and evaluation regarding the development of a time series analytics task. The main interest of this work lies in the investigation of the Ease of development. As a test scenario, a discrete Kalman filter is implemented to predict the closing price of stock market data in real-time. Basic functionality coverage is considered, and advanced functionality is evaluated using several qualitative comparison criteria. | en |
| dc.identifier.doi | 10.18420/btw2019-ws-21 | |
| dc.identifier.isbn | 978-3-88579-684-8 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/21808 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | BTW 2019 – Workshopband | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) – Proceedings, Volume P-290 | |
| dc.subject | Stream data | |
| dc.subject | Stream processing | |
| dc.subject | Time series analysis | |
| dc.subject | Predictive analytics | |
| dc.title | A Comparison of Distributed Stream Processing Systems for Time Series Analysis | en |
| gi.citation.endPage | 214 | |
| gi.citation.startPage | 205 | |
| gi.conference.date | 4.-8. März 2019 | |
| gi.conference.location | Rostock | |
| gi.conference.sessiontitle | Studierendenprogramm |
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