Open Data from Earth Observation: from Big Data to Linked Open Data, through INSPIRE
The increasing availability of Earth Observation and geographic data implies great opportunities for those capable to efficiently address the problems arising from the management of this huge amount of data. An efficient management of these data means to respond to the paradigm of the 4 V that usu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Italian e-Learning Association
2014-05-01
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| Series: | Je-LKS: Journal of E-Learning and Knowledge Society |
| Subjects: | |
| Online Access: | https://www.je-lks.org/ojs/index.php/Je-LKS_EN/article/view/914 |
| _version_ | 1818522375824605184 |
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| author | Massimo Zotti Claudio La Mantia |
| author_facet | Massimo Zotti Claudio La Mantia |
| author_sort | Massimo Zotti |
| collection | DOAJ |
| description | The increasing availability of Earth Observation and geographic data implies great opportunities for those capable to efficiently address the problems arising from the management of this huge amount of data.
An efficient management of these data means to respond to the paradigm of the 4 V that usually applies to the problem of the Big Data: Volume - the sheer size of the “data at rest”, Velocity - the speed of new data arriving, Variety - the different manifold, and Veracity - trustworthiness and issues of provenance. The problem of Big Data is not only in the geospatial realm of Earth observation data, but in general in all the location-based data which basically are the main contributors to the deluge of big data. In addition to the need of managing (store, search & find when needed) these data efficiently, the problem arises from the analysis of these data. They need to be quickly processed in order to quickly extract the information content, then they must be analysed in conjunction with other data sources in order to express their real value in the construction of new knowledge.
These processes are hastened by the advent of an increasing machine-to-machine communication. The automation of the data analysis requires standardized and linked data so that they can be processed by machines without human intervention.
The problem with the standardization of geospatial data is solved by simply observing not only the best practices shared at European level, but mainly the regulatory scenario dictated by the INSPIRE Directive . The publication of spatial data as Linked Open Data may then leverage the reuse of common ontologies and vocabularies that allow the connection of geospatial data with other heterogeneous information. This way new scenarios and business opportunities may arise, as in the case of the real estate market that is mentioned in this article.
This contribution aims to identify some business opportunities, related to Linked Open Data and arising from the imminent availability of the Sentinel satellite data, with the European program Copernicus, for companies operating in the so-called downstream services of Earth observation. |
| first_indexed | 2024-12-11T05:32:27Z |
| format | Article |
| id | doaj.art-443cd839ea5a46778bac6fff0e91d84c |
| institution | Directory Open Access Journal |
| issn | 1826-6223 1971-8829 |
| language | English |
| last_indexed | 2024-12-11T05:32:27Z |
| publishDate | 2014-05-01 |
| publisher | Italian e-Learning Association |
| record_format | Article |
| series | Je-LKS: Journal of E-Learning and Knowledge Society |
| spelling | doaj.art-443cd839ea5a46778bac6fff0e91d84c2022-12-22T01:19:23ZengItalian e-Learning AssociationJe-LKS: Journal of E-Learning and Knowledge Society1826-62231971-88292014-05-0110210.20368/1971-8829/914Open Data from Earth Observation: from Big Data to Linked Open Data, through INSPIREMassimo ZottiClaudio La Mantia0Planetek Italia srlThe increasing availability of Earth Observation and geographic data implies great opportunities for those capable to efficiently address the problems arising from the management of this huge amount of data. An efficient management of these data means to respond to the paradigm of the 4 V that usually applies to the problem of the Big Data: Volume - the sheer size of the “data at rest”, Velocity - the speed of new data arriving, Variety - the different manifold, and Veracity - trustworthiness and issues of provenance. The problem of Big Data is not only in the geospatial realm of Earth observation data, but in general in all the location-based data which basically are the main contributors to the deluge of big data. In addition to the need of managing (store, search & find when needed) these data efficiently, the problem arises from the analysis of these data. They need to be quickly processed in order to quickly extract the information content, then they must be analysed in conjunction with other data sources in order to express their real value in the construction of new knowledge. These processes are hastened by the advent of an increasing machine-to-machine communication. The automation of the data analysis requires standardized and linked data so that they can be processed by machines without human intervention. The problem with the standardization of geospatial data is solved by simply observing not only the best practices shared at European level, but mainly the regulatory scenario dictated by the INSPIRE Directive . The publication of spatial data as Linked Open Data may then leverage the reuse of common ontologies and vocabularies that allow the connection of geospatial data with other heterogeneous information. This way new scenarios and business opportunities may arise, as in the case of the real estate market that is mentioned in this article. This contribution aims to identify some business opportunities, related to Linked Open Data and arising from the imminent availability of the Sentinel satellite data, with the European program Copernicus, for companies operating in the so-called downstream services of Earth observation.https://www.je-lks.org/ojs/index.php/Je-LKS_EN/article/view/914Linked Open DataEarth ObservationBig DataInspire |
| spellingShingle | Massimo Zotti Claudio La Mantia Open Data from Earth Observation: from Big Data to Linked Open Data, through INSPIRE Je-LKS: Journal of E-Learning and Knowledge Society Linked Open Data Earth Observation Big Data Inspire |
| title | Open Data from Earth Observation: from Big Data to Linked Open Data, through INSPIRE |
| title_full | Open Data from Earth Observation: from Big Data to Linked Open Data, through INSPIRE |
| title_fullStr | Open Data from Earth Observation: from Big Data to Linked Open Data, through INSPIRE |
| title_full_unstemmed | Open Data from Earth Observation: from Big Data to Linked Open Data, through INSPIRE |
| title_short | Open Data from Earth Observation: from Big Data to Linked Open Data, through INSPIRE |
| title_sort | open data from earth observation from big data to linked open data through inspire |
| topic | Linked Open Data Earth Observation Big Data Inspire |
| url | https://www.je-lks.org/ojs/index.php/Je-LKS_EN/article/view/914 |
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