Towards a Platform for Urban Data Management, Integration and Processing

Badidi, Elarbi; Maheswaran, Muthucumaru

doi:10.5220/0006789602990306

Cited by 9 publications

(11 citation statements)

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“…Semantic DI technologies concern trajectories [109,110,125,126,167,225,239] or STID [22,23,25,149,230]. Semantic DI for trajectories aims to annotate raw location traces with concepts or complementary knowledge at particular timestamps or during time intervals, facilitating direct, concise, and explainable exploitation of trajectories.…”

Section: Data Integration (Di)mentioning

confidence: 99%

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

Jensen

et al. 2022

ACM Comput. Surv.

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With the continued deployment of the Internet of Things (IoT) , increasing volumes of devices are being deployed that emit massive spatially referenced data. Due in part to the dynamic, decentralized, and heterogeneous architecture of the IoT, the varying and often low quality of spatial IoT data (SID) presents challenges to applications built on top of this data. This survey aims to provide unique insight to practitioners who intend to develop IoT-enabled applications and to researchers who wish to conduct research that relates to data quality in the IoT setting. The survey offers an inventory analysis of major data quality dimensions in SID and covers significant data characteristics and associated quality considerations. The survey summarizes data quality related technologies from both task and technique perspectives. Organizing the technologies from the task perspective, it covers recent progress in SID quality management, encompassing location refinement, uncertainty elimination, outlier removal, fault correction, data integration, and data reduction; and it covers low-quality SID exploitation, encompassing querying, analysis, and decision-making techniques. Finally, the survey covers emerging trends and open issues concerning the quality of SID.

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Section: Data Integration (Di)mentioning

confidence: 99%

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

Jensen

et al. 2022

ACM Comput. Surv.

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“…This timeconsuming sequence is a brake on instant data visualization and real-time decisionmaking [4,45,46]. Data interoperability and integration constitutes one of the most difficult problems facing cities, as pointed out by [65]. With the purpose of addressing this issue, the large volumes of urban data generated by IoT devices and crowdsourcing activities need to be harnessed to help city applications make informed decisions on the fly.…”

Section: Cities As Data-driven Ecosystems: Urban Data Exploitation and Crowdsourcingmentioning

confidence: 99%

“…In this respect, the authors of Reference [45] presented the IES Cities platform, which was designed to streamline the development of urban applications that integrate heterogeneous datasets provided by different entities, such as citizens, municipality, IoT infrastructure and other data sources. In addition, the authors of [65] proposed a conceptual framework that aims to integrate data across the various systems of the city, urban data analytics and creation of value-added services using edge computing, cloud computing, data analytics and semantic integration. Of particular interest is the work of [46], which introduced a framework for a real-time decision support system for response during a crisis or disruption of critical infrastructure based on in-memory database technologies and urban data sources.…”

Section: Cities As Data-driven Ecosystems: Urban Data Exploitation and Crowdsourcingmentioning

confidence: 99%

Urban Data Dynamics: A Systematic Benchmarking Framework to Integrate Crowdsourcing and Smart Cities’ Standardization

et al. 2021

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Urbanization and knowledge economy have highly marked the new millennium. Urbanization brings new challenges which can be addressed by the knowledge economy, which opens up scientific and technical innovation opportunities. The enhancement of cities’ intelligence has heavily impacted city transformation and sustainable decision-making based on urban data knowledge extraction. This work is motivated by the strong demand for robust standardization efforts to steer and measure city performance and dynamics, given the growing tendency of conventional cities’ transformation into smart and resilient ones. This paper revises the earlier so-called “cityDNA” framework, which was designed to detect the interrelations between the six smart city dimensions, such that a city’s profile and capacities are recognized in a systematic manner. The updated framework implements the widely accepted smart city (ISO 37120:2018) standard, along with an adaptive Web service, which processes urban data and visualizes the city’s profile to facilitate decision-making. The proposed framework offers a solid benchmarking service, at which the value of crowdsourced data is exploited for the production of urban knowledge and city transformation empowerment. The proposed benchmarking approach is tested and validated through relevant case studies and a proof-of-concept scenario, in which open data and crowdsourced data are exploited. The outcomes revealed that cities should intensify their KPI-driven data production and exploitation along with a set of solid standards for cities to enable cities with customizable scenarios enriched with indicators that reflect each city’s vibrancy.

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“…Similarly, Hernández et al [4] see the end goal of the digitalisation of cities is to become more environmentally friendly, which as a process can be achieved via the Open Standardized Urban Platform with the main functionalities of data ingestion, analytics and services. Badidi & Maheswaran [5] and Chenget al [6] put the deployment of sensors as being more central in Urban Platforms' applications, with the end goal to improve the quality of life. The wider goal is to enable the transition of cities to more sustainable systems (less CO 2 , more energy efficient, more environmentally friendly, etc.-all better for the citizen).…”

Section: Introductionmentioning

confidence: 99%

“…The issue is brought out by Lee, Mackenzie, Smith, & Box [7], who mapped 100 urban data practices that contribute to "platform urbanism" by noting a concern dynamic of city administrators to be "locked in" to specific corporate products and interests. In addition, Badidi & Maheswaran [5] argue that data interoperability and integration is the most challenging smart city problem.…”

Section: Introductionmentioning

confidence: 99%

Urban Open Platform for Borderless Smart Cities

2022

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As a network of connected sensors to transform data into knowledge, Urban Platforms have been rooted in several smart city projects. However, this has often resulted in them being no more than IoT dashboards. More recently, there has been an increased interest in supporting the data governance and distributed architecture of Urban Platforms in order to adjust these with the administrative structure in a specific city. In addition, Urban Platforms also deal with data roaming between different stakeholders including other cities, different government levels, companies and citizens. Nevertheless, the first deployments have led to an inflexible “smart cities in a box” approach that does not help with building digital skills and causes vendor lock-in to products that do not scale. There is a need to start with simple and widespread urban services through a collaborative joint cross-border, hands-on effort. In order to meet the level of interoperability, international standards should be adopted. The aim of an Urban Open Platform (UOP), introduced in this paper, is to support not only data acquisition but also various types of data processing: data is aggregated, processed, manipulated and extended within the city context. Conceptually, special attention has been put on scalability, roaming and reliability in urban environments. This article introduces the UOP uniquely in the cross-border data exchange context of two European capital cities, Helsinki and Tallinn, and validates it with 10 real-life urban use cases.

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Towards a Platform for Urban Data Management, Integration and Processing

Cited by 9 publications

References 0 publications

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

Urban Data Dynamics: A Systematic Benchmarking Framework to Integrate Crowdsourcing and Smart Cities’ Standardization

Urban Open Platform for Borderless Smart Cities

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