Towards a Domain-Specific Language for geospatial data visualization maps with Big Data sets

Data visualization is an alternative for representing information and helping people gain faster insights. However, the programming/creating of a visualization for large data sets is still a challenging task for users with low-level of software development knowledge. Our goal is to increase the prod...

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Bibliographic Details
Published in:Proceedings / ACS/IEEE International Conference on Computer Systems and Applications pp. 1 - 8
Main Authors: Ledur, Cleverson, Griebler, Dalvan, Manssour, Isabel, Fernandes, Luiz Gustavo
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.11.2015
Subjects:
Big Data Analysis
Code Generation
Data visualization
Data Visualization Maps
Digital mapping
Domain-Specific Language
DSL
Electronics
Gain
Geospatial Data Visualization
Image color analysis
Information filters
Productivity
Programming languages
Raw Data Processing
Registers
Visualization
ISSN:2161-5330
Online Access:Get full text
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Summary:Data visualization is an alternative for representing information and helping people gain faster insights. However, the programming/creating of a visualization for large data sets is still a challenging task for users with low-level of software development knowledge. Our goal is to increase the productivity of experts who are familiar with the application domain. Therefore, we proposed an external Domain-Specific Language (DSL) that allows massive input of raw data and provides a small dictionary with suitable data visualization keywords. Also, we implemented it to support efficient data filtering operations and generate HTML or Javascript output code files (using Google Maps API). To measure the potential of our DSL, we evaluated four types of geospatial data visualization maps with four different technologies. The experiment results demonstrated a productivity gain when compared to the traditional way of implementing (e.g., Google Maps API, OpenLayers, and Leaflet), and efficient algorithm implementation.
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SourceType-Conference Papers & Proceedings-2
ISSN:2161-5330
DOI:10.1109/AICCSA.2015.7507178