Edge Computing Targeted Data Profiling for Assessing the Quality of Sensor Data
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Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Jemth, Rasmus
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis investigated how the data quality of sensor data is determined and if it
could be determined efficiently via a framework deployed on an edge computing device. The purpose of this was to enable data quality assessment to be done locally or
close to the source of sensor data, i.e., utilize edge computing, in order to overcome
the limitations of relying on cloud computing to assess large volumes of data. To determine how data quality should be defined, a literature study was conducted, which
resulted in a selection of data quality dimensions deemed relevant for sensor data.
The selection from the literature study contains the dimensions: Completeness, Accuracy, Timeliness, Consistency, Currency, Duplicates, and Precision. Based on this
list, techniques based on data profiling was developed to assess the quality of sensor
data, each technique assessed the quality regarding one specific data quality dimension. These techniques were implemented as part of an extendable framework. Each
technique was evaluated by its time and space complexity, and the actual time it
took for it to analyze datasets provided by Volvo Car Corporation, containing sensor
data created by autonomous vehicles. Finally, approaches to improving the quality
of sensor data based on the selected data quality dimensions were investigated.
Beskrivning
Ämne/nyckelord
Data Profiling , Data Quality , Data Quality Assessment , Edge Computing , Sensor Data