Volltext-Downloads (blau) und Frontdoor-Views (grau)
  • Treffer 22 von 27
Zurück zur Trefferliste

Exploration & analysis of OSM wheelchair history & microm data

  • The topic for the thesis originated from the CAP4ACCESS project run by the European Commission and its partners, which deals towards the sensiti-zation of people and development of tools for awareness about people with movement disabilities. The explorative analysis is never ending and to explore and find interest-ing patterns and the results is a tedious task. Therefore, a scientific approach was very important. To start with, familiarizing the domain and the data sources were done. Thereafter, selection of methodology for data analysis was done which resulted in the use of CRISP-DM methodology. The data sources are the source of blood to the analysis methodology, and as there were two sources of data that is MICROM and OSM Wheelchair History(OWH), it was important to integrate them together to extract relevant datasets. Therefore a functional and technically impure data warehouse was created, from which the datasets are extracted and analysed.The next task was to select appropriate tools for analysis. This task was very important as the data set although was not big data but con-tained a large number of rows. After careful analysis, Apache spark and its machine learning library were utilized for building and testing supervised models. DataFrame API for Python, Pandas, the machine learning library Sci-kit learn provided unsupervised algorithms for analysis, the association rule analysis was performed using WEKA. Tableau[21] and Matplotlib[24] provide attractive visualizations for representation and analysis.

Volltext Dateien herunterladen

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar

Statistik

frontdoor_oas
Metadaten
Verfasserangaben:Aadesh Misra
URN:urn:nbn:de:hbz:832-epub4-8852
Gutachter:Heide Faeskorn-Woyke
Dokumentart:Masterarbeit/Diplomarbeit
Sprache:Englisch
Veröffentlichende Institution:Hochschulbibliothek der Technischen Hochschule Köln
Titel verleihende Institution:Technische Hochschule Köln
Datum des Hochladens:29.07.2016
GND-Schlagwort:Data Mining
Freies Schlagwort / Tag:Data Mining
Seitenzahl:77
Fakultäten und Zentrale Einrichtungen:Informatik und Ingenieurwissenschaften (F10) / Fakultät 10 / Institut für Informatik
CCS-Klassifikation:E. Data
DDC-Sachgruppen:000 Allgemeines, Informatik, Informationswissenschaft
JEL-Klassifikation:Z Other Special Topics
Open Access:Open Access
Lizenz (Deutsch):License LogoCreative Commons - Namensnennung-Keine Bearbeitung