Authors:
Jonathan Engélinus
and
Thierry Badard
Affiliation:
Laval University, Canada
Keyword(s):
Elcano, ISO-19125, Magellan, Spatial Spark, GeoSpark, Geomesa, Simba, Spark SQL, Big Data.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computational Geometry
;
Computer Vision, Visualization and Computer Graphics
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Databases and Data Security
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Image Formation and Preprocessing
;
Knowledge-Based Systems
;
Query Processing and Optimization
;
Symbolic Systems
Abstract:
Big data are in the midst of many scientific and economic issues. Furthermore, their volume is continuously increasing. As a result, the need for management and processing solutions has become critical. Unfortunately, while most of these data have a spatial component, almost none of the current systems are able to manage it. For example, while Spark may be the most efficient environment for managing Big data, it is only used by five spatial data management systems. None of these solutions fully complies with ISO standards and OGC specifications in terms of spatial processing, and many of them are neither efficient enough nor extensible. The authors seek a way to overcome these limitations. Therefore, after a detailed study of the limitations of the existing systems, they define a system in greater accordance with the ISO-19125 standard. The proposed solution, Elcano, is an extension of Spark complying with this standard and allowing the SQL querying of spatial data. Finally, the test
s demonstrate that the resulting system surpasses the current available solutions on the market.
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