Enhancing spatial data warehouse exploitation: A SOLAP recommendation approach

Aissi, Saida; Gouider, Mohamed Salah; Sboui, Tarek; Saïd, Lamjed Ben

doi:10.1109/snpd.2016.7515940

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…OLAP queries are formulated as sequences called OLAP sessions. For analyzing data with the spatial and georeferenced components the Spatial OLAP (SOLAP) technology is used, which allows rapid and easy navigation within spatial databases and that offers many levels of information granularity, many themes, many epochs and many display modes synchronized or not: maps, tables and diagrams [14]. It allows tight integration of GIS and OLAP systems.…”

Section: Data Analyticsmentioning

confidence: 99%

Weather Data Visualization and Analytical Platform

Astsatryan

Grogoryan

Gyulgyulyan

et al. 2018

SCPE

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This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period.

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Section: Data Analyticsmentioning

confidence: 99%

Weather Data Visualization and Analytical Platform

Astsatryan

Grogoryan

Gyulgyulyan

et al. 2018

SCPE

View full text Add to dashboard Cite

show abstract

“…OLAP queries are formulated as sequences called OLAP sessions. For analyzing data with spatial and georeferenced components the Spatial OLAP (SOLAP) technology is used, which allows rapid and easy navigation within spatial databases and that offers many levels of information granularity, many themes, many epochs and many display modes synchronized or not: maps, tables and diagrams [12]. It allows a tight integration of GIS and OLAP systems.…”

Section: Data Managementmentioning

confidence: 99%

Cloud Service for Analysis and Interactive Visualization of Weather Data in Armenia

Grigoryan

Abrahamyan²

2018

Mathematical Problems of Computer Science

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The Lesser Caucasus Mountains are crossing through the territory of Armenia, creating vast differences in altitude, terrain, temperature and precipitation in provinces and towns. Even Armenia’s lowlands are 500 to 1500m above sea level. Armenias highlands extend up to Aragats mountain at 4090m where, 75% of the territory is above 1000m, 50% is above 2000m, and 3.4% is above 3000m. This paper presents a cloud service with interactive visualization and analytical capabilities for weather data in Armenia by integrating the two existing infrastructures for observational data and numerical weather prediction. The weather data used in the platform consist of near-surface atmospheric elements including air temperature, relative humidity, pressure, wind and precipitation. The visualization and analitycs have been implemented for 2m air temperature. Cloud service provides the Armenian State Hydrometeorological and Monitoring Service with analytical capabilities to make a comparative analysis between the observation data and the results of a numerical weather prediction model for per station and region for a given period.

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