Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

Souza, Jovani Taveira de; Francisco, Antônio Carlos de; Piekarski, Cassiano Moro; Prado, Guilherme

doi:10.3390/su11041077

Cited by 71 publications

(31 citation statements)

References 66 publications

(150 reference statements)

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“…As a basis for the bibliometric study, the 170 articles obtained in the first phase of the methodology described in the previous section were used. The search for publications in this phase was restricted to the last five years (2015-2019) after analyzing previous work on bibliometrics in the field of Machine Learning [36,37] due to the growing interest in this field in recent years, as well as the direct relationship between the publication of the SDGs in 2015 [38] and their applications to date.…”

Section: Bibliometric Analysismentioning

confidence: 99%

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

2020

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The unprecedented urban growth of recent years requires improved urban planning and management to make urban spaces more inclusive, safe, resilient and sustainable. Additionally, humanity faces the COVID pandemic, which especially complicates the management of Smart Cities. A possible solution to address these two problems (environmental and health) in Smart Cities may be the use of Machine Learning techniques. One of the objectives of our work is to thoroughly analyze the link between the concepts of Smart Cities, Machine Learning techniques and their applicability. In this work, an exhaustive study of the relationship between Smart Cities and the applicability of Machine Learning (ML) techniques is carried out with the aim of optimizing sustainability. For this, the ML models, analyzed from the point of view of the models, techniques and applications, are studied. The areas and dimensions of sustainability addressed are analyzed, and the Sustainable Development Goals (SDGs) are discussed. The main objective is to propose a model (EARLY) that allows us to tackle these problems in the future. An inclusive perspective on applicability, sustainability scopes and dimensions, SDGs, tools, data types and Machine Learning techniques is provided. Finally, a case study applied to an Andalusian city is presented.

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Section: Bibliometric Analysismentioning

confidence: 99%

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

2020

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“…The systematic literature review used the methodology Methodi Ordinatio, proposed by Pagani et al [125,126] and adopted in other works [127][128][129]. The purpose of this methodology is to select articles according to their scientific relevance, taking into account the main factors to be considered in a scientific paper: the impact factor of the journal on which the document was published, the number of citations, and the year of publication.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Knowledge and Technology Transfer Influencing the Process of Innovation in Green Supply Chain Management: A Multicriteria Model Based on the DEMATEL Method

et al. 2019

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After performing a systematic review of the literature, it was noticeable that the discussion regarding Green Supply Chain Management (GSCM) along with Knowledge and Technology Transfer (KTT) and Innovation is scarce. To address this issue, the objective of this paper is to analyze the relationship between GSCM, KTT, and Innovation. To achieve this purpose, we developed a conceptual framework based on the literature review, which raised some hypotheses. In order to verify the hypotheses and validate the framework, we designed a multicriteria model called the KTT-GSCM Multicriteria Model, composed of two stages. The first stage describes factors that are relevant to GSCM, KTT, and Innovation, ascertaining which ones have influence over the others and which ones are influenced by the others, through the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The second stage of the model analyzes the level of each perspective in each company and in the supply chain as a whole, thus verifying the hypotheses. This model was applied in 13 firms of a supply chain that has a rotomolding firm as a focal company. Through the development and application of the model, it was possible to verify the hypotheses raised and conclude that the perspectives of GSCM, KTT, and Innovation are, in fact, influenced by one another and that both Innovation and KTT promote greater development in GSCM.

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“…According to the e-government literature, open data challenges and success factors can be categorized into managerial, institutional, and environmental factors. Open data challenges and success factors have been explored through an analysis of direct relationships between a dependent variable and diverse independent variables [14][15][16][17][18][19][20]. Managerial factors are composed of organizational characteristics, processes, structures, and relations which have an influence on open data success [5,12,[21][22][23].…”

Section: Determinants Of Open Data Successmentioning

confidence: 99%

The Managerial Dimension of Open Data Success: Focusing on the Open Data Initiatives in Korean Local Governments

Kim

Eom

2019

Sustainability

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Open government data (open data) initiatives have been at the forefront of the strategy to make more transparent, responsive, and accountable government, and thereby lead to open innovation across the public and private sector. Governments around the world often understand that open data is disclosing their data to the public as much as possible and that open data success is the result of a data and technology-related endeavor rather than the result of organizational, institutional, and environmental attributes. According to the resource-based theory, however, managerial capability to mobilize tangible and intangible resources and deploy them in adequate places or processes under the leadership of capable leaders during the information technology (IT) project is a core factor leading to organizational performance such as open data success. In this vein, this study aims to analyze managerial factors as drivers and challenges of open data success from the resource-based theory. Findings illustrate that managerial factors are the driving forces that often boost or hinder open data success when institutional, socio-economic, and demographic factors are controlled. Discussion illustrates theoretical and practical implications for the managerial factors as drivers and challenges of open data success in terms of the comparison between technological determinism and the socio-technical perspective.

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Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018

Cited by 71 publications

References 66 publications

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

Machine Learning Technologies for Sustainability in Smart Cities in the Post-COVID Era

Knowledge and Technology Transfer Influencing the Process of Innovation in Green Supply Chain Management: A Multicriteria Model Based on the DEMATEL Method

The Managerial Dimension of Open Data Success: Focusing on the Open Data Initiatives in Korean Local Governments

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