CITIESData: a smart city data management framework

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Smart city data come from heterogeneous sources including various types of the
Internet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper,we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.
Original languageEnglish
JournalKnowledge and Information Systems
Volume53
Issue number3
Pages (from-to)699-722
ISSN0219-1377
DOIs
Publication statusPublished - 2017

Keywords

  • Data framework
  • Smart cities
  • Dara privacy
  • Data quality
  • Data sensitivity

Cite this

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title = "CITIESData: a smart city data management framework",
abstract = "Smart city data come from heterogeneous sources including various types of theInternet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper,we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.",
keywords = "Data framework, Smart cities, Dara privacy, Data quality, Data sensitivity",
author = "Xiufeng Liu and Alfred Heller and Nielsen, {Per Sieverts}",
year = "2017",
doi = "10.1007/s10115-017-1051-3",
language = "English",
volume = "53",
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publisher = "Springer U K",
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}

CITIESData: a smart city data management framework. / Liu, Xiufeng; Heller, Alfred; Nielsen, Per Sieverts.

In: Knowledge and Information Systems, Vol. 53, No. 3, 2017, p. 699-722.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - CITIESData: a smart city data management framework

AU - Liu, Xiufeng

AU - Heller, Alfred

AU - Nielsen, Per Sieverts

PY - 2017

Y1 - 2017

N2 - Smart city data come from heterogeneous sources including various types of theInternet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper,we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.

AB - Smart city data come from heterogeneous sources including various types of theInternet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper,we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.

KW - Data framework

KW - Smart cities

KW - Dara privacy

KW - Data quality

KW - Data sensitivity

U2 - 10.1007/s10115-017-1051-3

DO - 10.1007/s10115-017-1051-3

M3 - Journal article

VL - 53

SP - 699

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JO - Knowledge and Information Systems

JF - Knowledge and Information Systems

SN - 0219-1377

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