Welcome to rasdaman -- the world's most flexible and scalable Array Enginerasdaman ("raster data manager") allows storing and querying massive multi-dimensional arrays, such as sensor, image, simulation, and statistics data appearing in domains like earth, space, and life science. This worldwide leading array analytics engine distinguishes itself by its flexibility, performance, and scalability. Rasdaman can process arrays residing in file system directories as well as in databases with key features:. Fast: parallel access to Exascale archives and Terabyte objects in fractions of a second.Scalable: seamlessly from laptop to high-parallel, high-availability clouds and server farms.Flexible: "Array SQL" for navigation, extraction, processing,...
Raster image data is the most voluminous data type encountered in remote sensing applications. With ...
Multidimensional array data come up in many application areas. In computer graphics and imaging, tho...
This accompanying document for deliverable D4.1 Methods and Tools for Scalable Distributed Processin...
Welcome to rasdaman -- the world's most flexible and scalable Array Engine rasdaman ("raster data m...
With GIS technology for Web-enabled vector and meta data access becoming mature, the next quest is t...
Multi-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not...
Big geospatial raster data pose a grand challenge to data management technologies for effective big ...
The Big DataCube project aims at advancing the innovative datacube paradigm - i.e ., analy...
This paper presents the design of a GeoSPARQL query processing solution for scientific raster array ...
The paper deals with a design of a new raster sub-system intended for modern GIS systems open for cl...
Spatial data processing frameworks in many cases are limited to vector data only. However, an import...
Thesis (Ph.D.)--University of Washington, 2014Scientists today are able to generate data at an unpre...
Advancements in remote sensing technology have resulted in petabytes of remote sensing data being ma...
The increasing quantity and use of high-resolution raster data has put its management in the forefro...
Over the past decade several products have been using enterprise database technology to store and ma...
Raster image data is the most voluminous data type encountered in remote sensing applications. With ...
Multidimensional array data come up in many application areas. In computer graphics and imaging, tho...
This accompanying document for deliverable D4.1 Methods and Tools for Scalable Distributed Processin...
Welcome to rasdaman -- the world's most flexible and scalable Array Engine rasdaman ("raster data m...
With GIS technology for Web-enabled vector and meta data access becoming mature, the next quest is t...
Multi-dimensional arrays (also known as raster data or gridded data) play a key role in many, if not...
Big geospatial raster data pose a grand challenge to data management technologies for effective big ...
The Big DataCube project aims at advancing the innovative datacube paradigm - i.e ., analy...
This paper presents the design of a GeoSPARQL query processing solution for scientific raster array ...
The paper deals with a design of a new raster sub-system intended for modern GIS systems open for cl...
Spatial data processing frameworks in many cases are limited to vector data only. However, an import...
Thesis (Ph.D.)--University of Washington, 2014Scientists today are able to generate data at an unpre...
Advancements in remote sensing technology have resulted in petabytes of remote sensing data being ma...
The increasing quantity and use of high-resolution raster data has put its management in the forefro...
Over the past decade several products have been using enterprise database technology to store and ma...
Raster image data is the most voluminous data type encountered in remote sensing applications. With ...
Multidimensional array data come up in many application areas. In computer graphics and imaging, tho...
This accompanying document for deliverable D4.1 Methods and Tools for Scalable Distributed Processin...