Poster (Scientific congresses, symposiums and conference proceedings)
Big Automotive Data Preprocessing: A Three Stages Approach
Tawakuli, Amal; Kaiser, Daniel; Engel, Thomas
2019ACM Computer Science in Cars Symposium
 

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Keywords :
Big Data; Data Prepocessing; Edge Computing; Connected Vehicles
Abstract :
[en] The automotive industry generates large datasets of various formats, uncertainties and frequencies. To exploit Automotive Big Data, the data needs to be connected, fused and preprocessed to quality datasets before being used for production and business processes. Data preprocessing tasks are typically expensive, tightly coupled with their intended AI algorithms and are done manually by domain experts. Hence there is a need to automate data preprocessing to seamlessly generate cleaner data. We intend to introduce a generic data preprocessing framework that handles vehicle-to-everything (V2X) data streams and dynamic updates. We intend to decentralize and automate data preprocessing by leveraging edge computing with the objective of progressively improving the quality of the dataflow within edge components (vehicles) and onto the cloud.
Disciplines :
Computer science
Author, co-author :
Tawakuli, Amal ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Kaiser, Daniel ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Engel, Thomas ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
no
Language :
English
Title :
Big Automotive Data Preprocessing: A Three Stages Approach
Publication date :
08 October 2019
Event name :
ACM Computer Science in Cars Symposium
Event date :
08-10-2019
Commentary :
Extended Abstract
Available on ORBilu :
since 17 January 2020

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