Management of Air Quality Monitor Data with Data Warehouse and GIS

Xiao, Yunhua; Ji, Cuiling

doi:10.1109/csie.2009.280

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…(Ahmed et al, 2005) also studied air pollution. Their SDW is similar to that proposed by (Xiao et al, 2009), but they extended SOLAP operators to work with the continuity of the pollution phenomenon. Indeed, they defined and implemented some spatiotemporal interpolation functions to represent spatial dimension members as continuous field data.…”

Section: Environmental Domainmentioning

confidence: 97%

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Current Approaches, Challenges, and Perspectives on Spatial OLAP for Agri-Environmental Analysis

Bimonte

2016

International Journal of Agricultural and Environmental Information Systems

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Spatial OLAP (SOLAP) systems are powerful GeoBusiness Intelligence tools for analysing massive volumes of geo-referenced datasets. Therefore, these technologies are receiving considerable attention in the research community and in the database industry as well. Applications of these technologies are current in several domains such as ad marketing, healthcare, and urban development, to name a few. Contrary to other application domains, in the context of agri-environmental data and analysis, SOLAP systems have been underexploited. Therefore, in this paper, the author makes an exhaustive survey of most of the published studies in the domain of the SOLAP analysis of agri-environmental data with an emphasis on the reasons why only few recent works investigate the use of SOLAP systems in the agri-environmental context. In particular, the author focuses on the complexity of the spatio-multidimensional model and its implementation. Moreover, based on surveying the state of the art in this domain, this paper identifies some general guidelines that must be considered by the scientific community to design and implement efficient SOLAP approaches to the analysis of geo-referenced agri-environmental datasets. Finally, open issues about warehousing and OLAPing agri-environmental data are also shown in the paper.

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Section: Environmental Domainmentioning

confidence: 97%

“…Air quality monitoring is the focus of the work presented by (Xiao et al, 2009). In this paper, a SOLAP application is presented with a classical spatial dimension, a temporal dimension and a thematic pollutant dimension.…”

Section: Environmental Domainmentioning

confidence: 99%

Current Approaches, Challenges, and Perspectives on Spatial OLAP for Agri-Environmental Analysis

Bimonte

2016

International Journal of Agricultural and Environmental Information Systems

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“…A parallel effort has been devoted to designing innovative business intelligence and/or data mining solutions to perform different targeted and interesting analyses on pollutant measurements to evaluate air quality. Authors in [3] studied the pollutant concentration in different cities, or in different areas of a city, its variation over time and the correlation degree between concentrations of pollutants and other information such as weather conditions. Pollutant measurements were collected through a network of fixed monitoring stations, integrated with meteorological data and stored in a data warehouse.…”

Section: Related Workmentioning

confidence: 99%

“…To monitor air quality different indicators were defined to perform the analyses at a different spatial-temporal granularity. The APA engine addresses the research issue discussed in [3]. However, APA exploits different technological solutions and supports a richer set of analyses because traffic data are also integrated in the system.…”

Section: Related Workmentioning

confidence: 99%

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Analyzing air pollution on the urban environment

Baralis

Cerquitelli

Chiusano

et al. 2016

2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

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Air pollution is one of the most important factor that can affect the quality of citizen life in the urban environment. Consequently, monitoring air pollution is currently a critical issue that needs to be addressed for enhancing the well being of citizens. This paper proposes a data analysis engine, based on business intelligence methodologies and open technologies, to support different targeted analysis on air pollution data. To analyse the problem from different facets, air pollution measurements are enriched with additional information such as meteorological and traffic data, which are collected through sensor networks available in the smart city context. This integrated dataset is periodically analyzed to generate informative dashboards based on a selection of Key Performance Indicators (KPIs). The informative dashboards can provide useful insights about pollutant concentration at different time granularity levels in the urban areas and support a joint evaluation of pollutant concentrations with climate conditions and traffic flow. As a reference use case, open data on air pollution in the urban area of a major Italian city is analyzed to demonstrate the effectiveness of the proposed approach in a real smart city context.

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