Fields as a Generic Data Type for Big Spatial Data

Câmara, Gilberto; Egenhofer, Max J.; Ferreira, Karine Reis; Andrade, Pedro R.; Queiroz, Gilberto Ribeiro de; Sánchez, Alber; Jones, J. J.; Vinhas, Lúbia

doi:10.1007/978-3-319-11593-1_11

Cited by 15 publications

(21 citation statements)

References 24 publications

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“…The Digital Earth approach also allows solving questions, which gain significance in the context of big Earth data. This includes linking data sets or combining multiple sensor types (Dhu et al 2017), development of abstract data types, logical views and data models (Camara et al 2014;Ferreira, Camara, and Monteiro 2014) as well as Web-based processing (Sudmanns et al 2018;Wagemann et al 2018).…”

Section: Possible Future Directionsmentioning

confidence: 99%

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Big Earth data: disruptive changes in Earth observation data management and analysis?

Sudmanns

Tiede

Lang

et al. 2019

International Journal of Digital Earth

142

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Turning Earth observation (EO) data consistently and systematically into valuable global information layers is an ongoing challenge for the EO community. Recently, the term 'big Earth data' emerged to describe massive EO datasets that confronts analysts and their traditional workflows with a range of challenges. We argue that the altered circumstances must be actively intercepted by an evolution of EO to revolutionise their application in various domains. The disruptive element is that analysts and end-users increasingly rely on Web-based workflows. In this contribution we study selected systems and portals, put them in the context of challenges and opportunities and highlight selected shortcomings and possible future developments that we consider relevant for the imminent uptake of big Earth data.

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Section: Possible Future Directionsmentioning

confidence: 99%

“… 2017 ), development of abstract data types, logical views and data models (Camara et al. 2014 ; Ferreira, Camara, and Monteiro 2014 ) as well as Web-based processing (Sudmanns et al. 2018 ; Wagemann et al.…”

Section: Possible Future Directionsmentioning

confidence: 99%

Big Earth data: disruptive changes in Earth observation data management and analysis?

Sudmanns

Tiede

Lang

et al. 2019

International Journal of Digital Earth

142

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“…Finally, GIScience's proposal of field as generic data type for big spatial data [105] is worth considering when dealing with issues of efficient data representation in a big data context. Sensors are crucial for intelligent systems like smart cities [110,111] and are well covered by the OGC Sensor Web Enablement (SWE) [112].…”

Section: Research Challenges Existing Giscience Contributions To Tackmentioning

confidence: 99%

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

Degbelo

Granell

Trilles

et al. 2016

IJGI

134

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Abstract:The holy grail of smart cities is an integrated, sustainable approach to improve the efficiency of the city's operations and the quality of life of citizens. At the heart of this vision is the citizen, who is the primary beneficiary of smart city initiatives, either directly or indirectly. Despite the recent surge of research and smart cities initiatives in practice, there are still a number of challenges to overcome in realizing this vision. This position paper points out six citizen-related challenges: the engagement of citizens, the improvement of citizens' data literacy, the pairing of quantitative and qualitative data, the need for open standards, the development of personal services, and the development of persuasive interfaces. The article furthermore advocates the use of methods and techniques from GIScience to tackle these challenges, and presents the concept of an Open City Toolkit as a way of transferring insights and solutions from GIScience to smart cities.

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“…In the past, algebras have proven useful for specifying relational (Codd 1970) and geo-relational databases (Güting 1988), as well as fundamental GIS 4 operations (Tomlin 1990, Frank andKuhn 1995). Recently, algebraic definitions of spatial data types (Camara et al 2014, Ferreira et al 2014 were proposed.…”

Section: Introductionmentioning

confidence: 99%

Modeling spatiotemporal information generation

Scheider

Gräler

Pebesma

et al. 2016

International Journal of Geographical Information Science

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Maintaining knowledge about the provenance of datasets, that is, about how they were obtained, is crucial for their further use. Contrary to what the overused metaphors of 'data mining' and 'big data' are implying, it is hardly possible to use data in a meaningful way if information about sources and types of conversions is discarded in the process of data gathering. A generative model of spatiotemporal information could not only help automating the description of derivation processes but also assessing the scope of a dataset's future use by exploring possible transformations. Even though there are technical approaches to document data provenance, models for describing how spatiotemporal data are generated are still missing. To fill this gap, we introduce an algebra that models data generation and describes how datasets are derived, in terms of types of reference systems. We illustrate its versatility by applying it to a number of derivation scenarios, ranging from field aggregation to trajectory generation, and discuss its potential for retrieval, analysis support systems, as well as for assessing the space of meaningful computations.

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Fields as a Generic Data Type for Big Spatial Data

Cited by 15 publications

References 24 publications

Big Earth data: disruptive changes in Earth observation data management and analysis?

Big Earth data: disruptive changes in Earth observation data management and analysis?

Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience

Modeling spatiotemporal information generation

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