Auflistung nach Schlagwort "Data Science Challenge"
1 - 7 von 7
Treffer pro Seite
Sortieroptionen
- TextdokumentAssessing the Impact of Driving Bans with Data Analysis(BTW 2019 – Workshopband, 2019) Woltmann, Lucas; Hartmann, Claudio; Lehner, Wolfgang
- TextdokumentDie Data Science Challenge auf der BTW 2019 in Rostock(BTW 2019 – Workshopband, 2019) Grunert, Hannes; Meyer, HolgerZum zweiten Mal — nach der BTW 2017 in Stuttgart [Wa17] — findet auf der BTW-Konferenzreihe die Data Science Challenge statt. Die Teilnehmer der Challenge hatten die Möglichkeit, ihren eigenen Ansatz zur cloud-basierten Datenanalyse zu entwickeln und damit im direkten Vergleich gegen andere Teilnehmer anzutreten.
- TextdokumentDeep Learning zur Vorhersage von Feinstaubbelastung(BTW 2019 – Workshopband, 2019) Alkhouri, Georges; Wilke, MoritzFeinstaubbelastung steht seit einiger Zeit in der öffentlichen Debatte und stellt mir hoher Wahrscheinlichkeit ein großes Gesundheitsrisiko dar. Laut WHO [Or06] kann die Redu-zierung von Feinstaub zur Senkung verschiedener Krankheiten, wie bspw. Herzinfarkten, Lungenkrebs und asmathischen Erkankungen dienen. Deswegen werden von der Organisa-tion Tagesgrenzwerte von 25 μg/m 3 für Partikel um 2,5 μm (PM2,5) und 50 μg/m 3 für Partikel um 10 μm (PM10) empfohlen. In diesem Beitrag zur Data Science Challenge soll gezeigt werden, wie die vorhandenen Feinstaubsensoren in der Stadt Leipzig genutzt werden können, um zukünftige Werte vorherzusagen.3 Eine solche Vorhersage könnte nicht nur zur Warnung dienen, sondern auch Grundlage für kurzfristige Gegenmaßnahmen (bspw. den Wechsel auf ÖPNV) bilden.
- TextdokumentExplanation of Air Pollution Using External Data Sources(BTW 2019 – Workshopband, 2019) Esmailoghli, Mahdi; Redyuk, Sergey; Martinez, Ricardo; Abedjan, Ziawasch; Rabl, Tilmann; Markl, Volker
- ZeitschriftenartikelParticulate Matter Matters—The Data Science Challenge @ BTW 2019(Datenbank-Spektrum: Vol. 19, No. 3, 2019) Meyer, Holger J.; Grunert, Hannes; Waizenegger, Tim; Woltmann, Lucas; Hartmann, Claudio; Lehner, Wolfgang; Esmailoghli, Mahdi; Redyuk, Sergey; Martinez, Ricardo; Abedjan, Ziawasch; Ziehn, Ariane; Rabl, Tilmann; Markl, Volker; Schmitz, Christian; Serai, Dhiren Devinder; Gava, Tatiane EscobarFor the second time, the Data Science Challenge took place as part of the 18th symposium “Database Systems for Business, Technology and Web” (BTW) of the Gesellschaft für Informatik (GI). The Challenge was organized by the University of Rostock and sponsored by IBM and SAP. This year, the integration, analysis and visualization around the topic of particulate matter pollution was the focus of the challenge. After a preselection round, the accepted participants had one month to adapt their developed approach to a substantiated problem, the real challenge. The final presentation took place at BTW 2019 in front of the prize jury and the attending audience. In this article, we give a brief overview of the schedule and the organization of the Data Science Challenge. In addition, the problem to be solved and its solution will be presented by the participants.
- TextdokumentPeaks and the Influence of Weather, Traffic, and Events on Particulate Pollution(BTW 2019 – Workshopband, 2019) Hagedorn, Stefan; Sattler, Kai-UweThe task of the Data Science Challenge as part of the BTW 2019 conference is to analyze air quality data collected by the luftdaten2 project. This project provides sensor measurements recorded from volunteers around the world. With do-it-yourself setups people can deploy their own sensors and report various environmental values to the project’s servers, where they are made available as open data for further analyses. Thus, data is available only in regions where volunteers decided to participate in the project. Since in our city, Ilmenau, as well as in the state Thuringia only very few sensors are present, we decided to shift our focus to a broader area around Thuringia.
- TextdokumentPrediction of air pollution with machine learning(BTW 2019 – Workshopband, 2019) Schmitz, Christian; Serai, Dhiren Devinder; Escobar Gava, TatianeCities worldwide are facing air quality issues, leading to bans of vehicles and lower quality of life for inhabitants. We forecast the air quality for Stuttgart based on expected weather condition. For that purpose, we extract, cleanse, and integrate the DHT22 and SDS11 sensors’ data to feed two different machine learning models for predicting the particulate matter values for the near future.