Summary
"In the age of big data, geographic information has become a central means for data scientists of various disciplines to embed their analysis into a spatio-temporal context, from human mobility patterns and social inequality to the investigation of personal health. However, as the variety of data sources available on the Web increases, it becomes more and more impossible to comprehend and utilize all tools available to answer geo-analytical questions. The variety of formats and syntaxes required by Geographic Information System (GIS) toolboxes or statistical packages divides the research community into various tool expert groups. Hence, whenever a functionality is needed but not available in one tool, analysts are forced to reformulate their questions in terms of the technicalities of another tool. Furthermore, new tools are difficult to learn, and translations cause severe interoperability problems. Finally, this procedure does not scale with the increasing variety of analytic resources on the Web, preventing analysts from tapping its full potential, and making the promise of seamless big data analytics a mere distant dream. Consider, in contrast, how easy it is for a user of a digital smartphone assistant such as Amazon's Alexa to ask a question like ""What is the weather today?"" and get back an answer from the Web. It would mean a tremendous breakthrough in information science if analysts could similarly ask familiar questions in order to get the tools and data required to answer them. Unfortunately, analytic technology currently cannot handle such questions. To realize this vision, it is necessary to understand how analytic resources can be captured with the questions they answer. In this project, I will develop a novel theory of interrogative spatial concepts to turn geo-analytical questions into a machine-readable form using semantic queries. In this form, questions can directly be matched with the capacity of major analytic GIS tools and data on the Web."
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/803498 |
| Start date: | 01-01-2019 |
| End date: | 31-08-2024 |
| Total budget - Public funding: | 1 499 412,00 Euro - 1 499 412,00 Euro |
Cordis data
Original description
"In the age of big data, geographic information has become a central means for data scientists of various disciplines to embed their analysis into a spatio-temporal context, from human mobility patterns and social inequality to the investigation of personal health. However, as the variety of data sources available on the Web increases, it becomes more and more impossible to comprehend and utilize all tools available to answer geo-analytical questions. The variety of formats and syntaxes required by Geographic Information System (GIS) toolboxes or statistical packages divides the research community into various tool expert groups. Hence, whenever a functionality is needed but not available in one tool, analysts are forced to reformulate their questions in terms of the technicalities of another tool. Furthermore, new tools are difficult to learn, and translations cause severe interoperability problems. Finally, this procedure does not scale with the increasing variety of analytic resources on the Web, preventing analysts from tapping its full potential, and making the promise of seamless big data analytics a mere distant dream. Consider, in contrast, how easy it is for a user of a digital smartphone assistant such as Amazon's Alexa to ask a question like ""What is the weather today?"" and get back an answer from the Web. It would mean a tremendous breakthrough in information science if analysts could similarly ask familiar questions in order to get the tools and data required to answer them. Unfortunately, analytic technology currently cannot handle such questions. To realize this vision, it is necessary to understand how analytic resources can be captured with the questions they answer. In this project, I will develop a novel theory of interrogative spatial concepts to turn geo-analytical questions into a machine-readable form using semantic queries. In this form, questions can directly be matched with the capacity of major analytic GIS tools and data on the Web."Status
SIGNEDCall topic
ERC-2018-STGUpdate Date
27-04-2024
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