A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Novel Data Representation Method for Smart Cities’ Big Data
In the past decades, the evolution of big cities was followed by the emergence of smart city technologies. These new technologies enable in-depth analysis, optimization of public city services, and new modes of governance. However, as the cities’ infrastructure develops, the emerging data sources generate massive datasets. Currently, the efficient and accurate processing of smart cities’ enormous time series datasets poses a particular challenge to data scientists.
To overcome this problem, many high-level representations of time series have been proposed, including Fourier transform, wavelet transform, or symbolic representation. Applying fundamental symbolization techniques for time series with multiple variables, such as Symbolic Aggregate Approximation (SAX), results in distinct sequences of symbols for each variable.
A novel multivariate extension of SAX will be presented, which allows to express a multivariate time series with one sequence of symbols. Integrating individual sequences in one symbolic sequence provides better expressive power, while our modified SAX distance measure can be applied for clustering and classification tasks in smart cities, decreasing the enormous dataset storage and speeding up the big data processing. Performance evaluation shows that our multivariate symbolic representation results in better accuracy and dimension reduction than the classical SAX method.
A Novel Data Representation Method for Smart Cities’ Big Data
In the past decades, the evolution of big cities was followed by the emergence of smart city technologies. These new technologies enable in-depth analysis, optimization of public city services, and new modes of governance. However, as the cities’ infrastructure develops, the emerging data sources generate massive datasets. Currently, the efficient and accurate processing of smart cities’ enormous time series datasets poses a particular challenge to data scientists.
To overcome this problem, many high-level representations of time series have been proposed, including Fourier transform, wavelet transform, or symbolic representation. Applying fundamental symbolization techniques for time series with multiple variables, such as Symbolic Aggregate Approximation (SAX), results in distinct sequences of symbols for each variable.
A novel multivariate extension of SAX will be presented, which allows to express a multivariate time series with one sequence of symbols. Integrating individual sequences in one symbolic sequence provides better expressive power, while our modified SAX distance measure can be applied for clustering and classification tasks in smart cities, decreasing the enormous dataset storage and speeding up the big data processing. Performance evaluation shows that our multivariate symbolic representation results in better accuracy and dimension reduction than the classical SAX method.
A Novel Data Representation Method for Smart Cities’ Big Data
Springer Optimization
Pardalos, Panos M. (editor) / Rassia, Stamatina Th. (editor) / Tsokas, Arsenios (editor) / Nagy, Attila M. (author) / Simon, Vilmos (author)
Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities ; Chapter: 6 ; 97-122
2021-08-11
26 pages
Article/Chapter (Book)
Electronic Resource
English
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