Benchmarking study on smart city data analytics

Mendili, Saida El; Idrissi, Younès El Bouzekri El; Hmina, Nabil

doi:10.1109/cist.2016.7805004

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…The above searched literature indicates that in the past few years there has been considerable increase in literature about smart city data analytics. Most noticeable articles which appeared in the above search criteria and highlighted smart city data, technology (e.g., IoT, sensors, cloud, fog, edge, GIS, AI, Big Data, data mining, machine learning, semantics, Human‐Computer‐Interaction, privacy and security) and system architectures applicable to variety of city applications are (PWC., ; Al Nuaimi, Al Neyadi, Mohamed, & Al‐Jaroodi, ; Barns, ; Bawany & Shamsi, ; Bibri, ; Bibri & Krogstie, ; Bilal, Khalid, Erbad, & Khan, ; Chen et al, ; El Mendili, El Idrissi, & Hmina, ; Faieq, Saidi, Elghazi, & Rahmani, ; Habibzadeh et al, ; Hashem et al, ; Khan et al, ; Osman, Elragal, & Bergvall‐Kåreborn, ; Saggi & Jain, ; Silva, Khan, & Han, ; Susmitha & Jayaprada, ; Yin et al, ; Zotano & Bersini, ).…”

Section: Big Data Analytics In Smart Citiesmentioning

confidence: 99%

Smart city big data analytics: An advanced review

Soomro

Bhutta

Khan

et al. 2019

WIREs Data Min & Knowl

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With 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.

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