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Data Driven Maturity Model for Assessing Smart Cities

Published: 18 October 2018 Publication History

Abstract

Smart cities provide the ability to improve the quality of the citizen's life. Transformation into a smart city consists of defining the way ICT (Information and Communication Technologies) can be used to improve the weaker aspects of the city and improve the quality of services provided by public sectors (education, health, transportation...). The growth of the urban population implies the growing needs of urban services (health, education...) and resources (water, energy...). ICT can be used to meet the growing population needs and solve many of today's problems in the private and public sectors (health, transportation, school...). Using mobile phones all citizens produce data and information every day and everywhere, this data will be used to improve the quality of services provided by the city. The quality of the generated data presents the key element that will impact the success of the transformation into a smart city.
This paper describes the proposed data quality driven smart cities model. The proposed model, called DQSC-MM (Data Quality Driven Smart Cities Maturity Model). DQSC-MM is used to evaluate the maturity of a smart city based on the quality of produced and consumed data. It suggests a way to measure the importance of data quality in a city's transformation into a smart city. The paper describes how the model was conceived, designed and developed. It describes also a JEE application conceived to support DQSC-MM. The developed application provides the ability to measure data quality and use these measurements for smart city evaluation.

References

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Megha Kumar. Building Agile Data Driven Smart Cities (October 2015)
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Solar, M., Sabattin, J., & Parada. A Maturity Model for Assessing the Use of ICT in School Education (Accepted November 25, 2011)
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Gonzalo Valdés, Mauricio Solar, Hernán Astudillo, Marcelo Iribarren, Gastón Concha, Marcello Visconti. Conception, development and implementation of an e-Government maturity model in public agencies (Available online 26 January 2011)
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Paolo Neirotti, Alberto De Marco, Anna Corinna Cagliano, Giulio Mangano, Francesco Scorrano. Current trends in Smart City initiatives: Some stylised facts (19 January 2014)
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Richard Y. Wang & Diane M. Strong. Beyond Accuracy: What Data Quality Means to Data Consumers (Published online: 11 Dec 2015)
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Reza Vaziri and Mehran Mohsenzadeh, A questionnaire-based data quality methodology. International Journal of Database Management Systems (IJDMS) Vol.4, No.2, April 2012.
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Antonio Vetrò, Lorenzo Canova, Marco Torchiano, Camilo Orozco Minotas, Raimondo Iemma, Federico Morando. Open data quality measurement framework: Definition and application to Open Government Data. (3 February 2016)
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Michael kusters, https://fr.slideshare.net/MichaelKuesters/dataquality-definitions. Data Quality Management Definitions, The Characteristics of Data Quality. (Published on 7 february 2010)
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Kehua Su, Jie Li, Hongbo Fu (School of Computer, Wuhan, UniversityWuhan, Hubei, China). Smart City and the Applications (2011)
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Yibin Li, Wenyun Dai, Student Member, IEEE, Zhong Ming, Meikang Qiu, Senior Member, IEEE. Privacy Protection for Preventing Data Over-Collection in Smart City (2015).
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Leo L. Pipino, Yang W. Lee, and Richard Y. Wang. Data Quality Assessment (2002)
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Mauricio Solar, Hernán Astudillo, Gonzalo Valdés, Marcelo Iribarren and Gastón Concha. Identifying Weaknesses for Chilean e-Government Implementation in Public Agencies with Maturity Model. https://web.stanford.edu/~gvaldesu/articles/IdentifyingWeaknessesForChileanEGov.pdf. (Published 2009 in EGOV.)
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Meryam Belhiah, Mohammed Salim Benqatla, and Bouchaïb Bounabat. Decision Support System for Implementing Data Quality Projects. Springer International Publishing Switzerland 2016. M. Helfert et al. (Eds.): DATA 2015, CCIS 584, pp. 1--16, 2016.
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BELHIAH, Meryam & BOUNABAT, Bouchaib. - A User-Centered Model for Assessing and Improving Open Government Data Quality. Massachusetts Institute of Technology (MIT) International Conference on Information Quality, 2017, UA Little Rock, USA.

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cover image ACM Other conferences
ICSDE'18: Proceedings of the 2nd International Conference on Smart Digital Environment
October 2018
214 pages
ISBN:9781450365079
DOI:10.1145/3289100
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • University of Houston

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 October 2018

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Author Tags

  1. DQSC-MM
  2. Smart city
  3. data
  4. data quality
  5. data quality measurement
  6. maturity model
  7. smart city evaluation

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ICSDE'18

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ICSDE'18 Paper Acceptance Rate 32 of 80 submissions, 40%;
Overall Acceptance Rate 68 of 219 submissions, 31%

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