TY - GEN
T1 - Extracting information from driving data using k-means clustering
AU - Chetouane, Nour
AU - Klampfl, Lorenz
AU - Wotawa, Franz
N1 - Funding Information:
∗The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged. †Authors are listed in alphabetical order. ‡DOI reference number: 10.18293/SEKE2021-118
Publisher Copyright:
© 2021 Knowledge Systems Institute Graduate School. All rights reserved.
PY - 2021
Y1 - 2021
N2 - There is an increasing availability of data, but for making decisions and other tasks we need information. Hence, we require to analyze the data and extract parts or come up with relations between different pieces. In this paper, we focus on information extraction within the automotive industry. In particular, we report on applying k-means clustering for identifying episodes in vehicle data. An episode is considered to be a time interval where a vehicle is performing an activity worth being distinguished. The underlying idea is to cluster the data such that we are able to extract such similar situations like breaking before a crossing only considering vehicle data. We discuss a method that allows extracting such episodes capturing actuator and sensor readings over time. Besides introducing the underlying method, we present obtained empirical results making use of a freely available dataset showing that the extracted episodes have indeed a meaningful interpretation.
AB - There is an increasing availability of data, but for making decisions and other tasks we need information. Hence, we require to analyze the data and extract parts or come up with relations between different pieces. In this paper, we focus on information extraction within the automotive industry. In particular, we report on applying k-means clustering for identifying episodes in vehicle data. An episode is considered to be a time interval where a vehicle is performing an activity worth being distinguished. The underlying idea is to cluster the data such that we are able to extract such similar situations like breaking before a crossing only considering vehicle data. We discuss a method that allows extracting such episodes capturing actuator and sensor readings over time. Besides introducing the underlying method, we present obtained empirical results making use of a freely available dataset showing that the extracted episodes have indeed a meaningful interpretation.
UR - http://www.scopus.com/inward/record.url?scp=85114282820&partnerID=8YFLogxK
U2 - 10.18293/SEKE2021-118
DO - 10.18293/SEKE2021-118
M3 - Conference paper
AN - SCOPUS:85114282820
T3 - Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
SP - 610
EP - 615
BT - Proceedings - SEKE 2021
PB - Knowledge Systems Institute Graduate School
T2 - 33rd International Conference on Software Engineering and Knowledge Engineering
Y2 - 1 July 2021 through 10 July 2021
ER -