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A novel method of symbolic representation in diving data mining: A case study of highways in China
Author(s) -
Sun Chuan,
Liu Wei,
Chu Duanfeng,
Li Wushuang,
Lu Zhenji,
Wang Jianyu
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4976
Subject(s) - bottleneck , computer science , dimensionality reduction , normalization (sociology) , data mining , curse of dimensionality , symbolic data analysis , field (mathematics) , representation (politics) , sample (material) , time series , artificial intelligence , machine learning , theoretical computer science , mathematics , chemistry , chromatography , sociology , politics , anthropology , political science , pure mathematics , law , embedded system
Summary Vehicle field test can be conducted smoothly because of the automobile‐mounted monitoring system and abundant diving data have been collected. Driving data mining is in an urgent need of new thoughts introduced to break through the original technical bottleneck. This paper presented a novel method of symbolic representation in diving data mining and applied the idea of time series symbolization to traffic engineering. The sample data is processed by normalization, dimensionality reduction, discretization, and symbolization based on the three steps of symbolic aggregate approximation (SAX) with driving data characteristics taken into adequate consideration. The results showed that the high‐dimensionality miscellaneous driving time series data was rationally converted into highly readable, easy to search and locate symbolic series after semantic encoding, and the main characteristics of time series data was preserved after a substantial reduction of data dimensionality. Finally, the paper demonstrated the positive effects of this method on the analysis of actual vehicle driving safety based on case study, and it explored the application of SAX to speed and acceleration data from driving data set.