Hand-OLAP: Semantics-Aware Compression of Data Cubes for Effective and Efficient OLAP in Mobile Enviroments

2011 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Domenico Sacca
Keyword(s):  
Data Cubes

Structuring and visualization of indicators in multidimensional data cubes

2018 ◽  
pp. 79-87
Author(s):  
E. E. Akimkina
Keyword(s):  
Decision Making ◽  
Data Processing ◽  
Data Management ◽  
Multidimensional Data ◽  
End User ◽  
Processing Technologies ◽  
Data Cubes ◽  
Multidimensional Visualization ◽  
Multidimensional Data Cubes ◽  
Practical Recommendations

The problems of structuring of indicators in multidimensional data cubes with their subsequent processing with the help of end-user tools providing multidimensional visualization and data management are analyzed; the possibilities of multidimensional data processing technologies for managing and supporting decision making at a design and technological enterprise are shown; practical recommendations on the use of domestic computer environments for the structuring and visualization of multidimensional data cubes are given.

scholarly journals Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 - iMap World 1.0

2021 ◽  
Vol 258 ◽  
pp. 112364
Author(s):  
Han Liu ◽  
Peng Gong ◽  
Jie Wang ◽  
Xi Wang ◽  
Grant Ning ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Change ◽  
Data Cubes ◽  
Global Land Cover ◽  
Global Land

scholarly journals 2D–1D Wavelet Reconstruction as a Tool for Source Finding in Spectroscopic Imaging Surveys

2012 ◽  
Vol 29 (3) ◽  
pp. 244-250 ◽  
Author(s):  
L. Flöer ◽  
B. Winkel
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Spectroscopic Data ◽  
Wavelet Decomposition ◽  
Spectroscopic Imaging ◽  
Basic Method ◽  
Spectral Dimension ◽  
Wavelet Coefficients ◽  
Data Cubes ◽  
Spatial Dimensions

AbstractToday, image denoising by thresholding of wavelet coefficients is a commonly used tool for 2D image enhancement. Since the data product of spectroscopic imaging surveys has two spatial dimensions and one spectral dimension, the techniques for denoising have to be adapted to this change in dimensionality. In this paper we will review the basic method of denoising data by thresholding wavelet coefficients and implement a 2D–1D wavelet decomposition to obtain an efficient way of denoising spectroscopic data cubes. We conduct different simulations to evaluate the usefulness of the algorithm as part of a source finding pipeline.

Range queries in dynamic OLAP data cubes

2000 ◽  
Vol 34 (1) ◽  
pp. 21-38 ◽  
Author(s):  
Weifa Liang ◽  
Hui Wang ◽  
Maria E. Orlowska
Keyword(s):  
Range Queries ◽  
Data Cubes

Maintenance of data cubes and summary tables in a warehouse

1997 ◽  
Author(s):  
Inderpal Singh Mumick ◽  
Dallan Quass ◽  
Barinderpal Singh Mumick
Keyword(s):  
Data Cubes

Spatial data cubes based on shared dimensions and neighbourhood relationship concepts

2021 ◽  
Vol 37 (3) ◽  
pp. 308
Author(s):  
Tarik De Melo e Silva Rocha ◽  
Rodrigo Rocha Silva ◽  
Tiago Garcia De Senna Carneiro ◽  
Joubert De Castro Lima
Keyword(s):  
Spatial Data ◽  
Data Cubes

Privacy preservation for data cubes

2005 ◽  
Vol 9 (1) ◽  
pp. 38-61 ◽  
Author(s):  
Sam Y. Sung ◽  
Yao Liu ◽  
Hui Xiong ◽  
Peter A. Ng
Keyword(s):  
Privacy Preservation ◽  
Data Cubes

scholarly journals The Austrian Semantic EO Data Cube Infrastructure

2021 ◽  
Vol 13 (23) ◽  
pp. 4807
Author(s):  
Martin Sudmanns ◽  
Hannah Augustin ◽  
Lucas van der Meer ◽  
Andrea Baraldi ◽  
Dirk Tiede
Keyword(s):  
Land Cover ◽  
Knowledge Engineering ◽  
A Priori ◽  
Data Cube ◽  
Color Information ◽  
Data Cubes ◽  
Semantic Enrichment ◽  
Knowledge Based ◽  
Geographical Regions ◽  
Semantic Models

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic reflectance values that lack inherent semantic information (meaning) and require interpretation. However, interpretation is an ill-posed problem that is difficult for many users to solve. Our semantic EO data cube architecture aims to implement computer vision in EO data cubes as an explainable artificial intelligence approach. Automatic semantic enrichment provides semi-symbolic spectral categories for all observations as an initial interpretation of color information. Users graphically create knowledge-based semantic models in a convergence-of-evidence approach, where color information is modelled a-priori as one property of semantic concepts, such as land cover entities. This differs from other approaches that do not use a-priori knowledge and assume a direct 1:1 relationship between reflectance values and land cover. The semantic models are explainable, transferable, reusable, and users can share them in a knowledgebase. We provide insights into our web-based architecture, called Sen2Cube.at, including semantic enrichment, data models, knowledge engineering, semantic querying, and the graphical user interface. Our implemented prototype uses all Sentinel-2 MSI images covering Austria; however, the approach is transferable to other geographical regions and sensors. We demonstrate that explainable, knowledge-based big EO data analysis is possible via graphical semantic querying in EO data cubes.

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