A Novel Data Representation Method for Smart Cities’ Big Data

Attila M. Nagy () and Vilmos Simon ()
Additional contact information
Attila M. Nagy: Department of Networked Systems and Services
Vilmos Simon: Department of Networked Systems and Services

A chapter in Artificial Intelligence, Machine Learning, and Optimization Tools for Smart Cities, 2022, pp 97-122 from Springer

Abstract: Abstract 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.

Keywords: Multivariate time series; SAX; Data mining; Symbolic representation; Parameter optimization (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-84459-2_6

Ordering information: This item can be ordered from
http://www.springer.com/9783030844592

DOI: 10.1007/978-3-030-84459-2_6

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().