Smart city big data analytics: An advanced review

• DOI: 10.1002/widm.1319
• Publication Date: 2019-06-19T10:01:41Z
• Authors: Kamran Soomro; Muhammad Nasir Mu...; Zaheer Khan; Muhammad A. Tahir
• Keywords: Knowledge management; Internet of Things; Social Sciences; Transportation; 02 engineering and technology; 7. Clean energy; Data science; Engineering; Sociology; Computer security; 11. Sustainability; 0202 electrical engineering, electronic engineering, information engineering; Government (linguistics); Business; Unstructured data; Corporate governance; Geography; Traffic Flow Prediction and Forecasting; Computer Science Research Centre; Urban Analysis; Social science; FOS: Philosophy, ethics and religion; FOS: Sociology; Physical Sciences; data mining, big data analytics, smart cities; Cartography; Analytics; Smart Card Data; Data analysis; Mathematical analysis; 12. Responsible consumption; Business intelligence; Big data; Field (mathematics); Qualitative research; Media Technology; FOS: Mathematics; Data mining; Smart city; Domain (mathematical analysis); 9. Industry and infrastructure; Pure mathematics; Linguistics; Building and Construction; Computer science; Thematic analysis; Philosophy; Smart Cities; 13. Climate action; FOS: Languages and literature; Thematic map; Intelligence analysis; Smart Cities: Innovations and Challenges; Mathematics; Finance; Understanding Human Mobility Patterns

AbstractWith the increasing role of ICT in enabling and supporting smart cities, the demand for big data analytics solutions is increasing. Various artificial intelligence, data mining, machine learning and statistical analysis‐based solutions have been successfully applied in thematic domains like climate science, energy management, transport, air quality management and weather pattern analysis. In this paper, we present a systematic review of the literature on smart city big data analytics. We have searched a number of different repositories using specific keywords and followed a structured data mining methodology for selecting material for the review. We have also performed a technological and thematic analysis of the shortlisted literature, identified various data mining/machine learning techniques and presented the results. Based on this analysis we also present a classification model that studies four aspects of research in this domain. These include data models, computing models, security and privacy aspects and major market drivers in the smart cities domain. Moreover, we present a gap analysis and identify future directions for research. For the thematic analysis we identified the themes smart city governance, economy, environment, transport and energy. We present the major challenges in these themes, the major research work done in the field of data analytics to address these challenges and future research directions.This article is categorized under:

Application Areas > Government and Public Sector Fundamental Concepts of Data and Knowledge > Big Data Mining