Objects of interest detection by earth remote sensing data analysis

In information systems based on a multidimensional approach, a data model is a multidimensional data cube. If one uses a large set of aspects for the analysis of data domain the data cubes are characterized by substantial sparseness. This makes it difficult to describe the metadata of the information system and complicates the organization of data storage. To describe the structure of a sparse data cube, a cluster method can be used. This method is based on the construction of groups of members which are semantically connected with other groups of members. Connected groups related to different dimensions describe the cluster of cells. Classification schemes that correspond to the structural components of the observed phenomenon can be used to describe it’s semantics. Every classification scheme is a graph describing the hierarchy of members that are associated with a separate structural component of the observed phenomenon. The coupling between several classification schemes related to different structural components helps to describe the metadata of the multidimensional information system. Classification schemes are a source of classification of information objects of a multidimensional cube related to the structural components of the observed phenomenon. Copyright © 2018 for the individual papers by the papers’ authors.

Conference proceedings
Publisher
CEUR-WS
Language
English
Pages
65-71
Status
Published
Volume
2236
Year
2018
Organizations
  • 1 Department of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya str., Moscow, 117198, Russian Federation
  • 2 Department of Information Technologies Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya str., Moscow, 117198, Russian Federation
Keywords
Cluster of member combinations; Data warehouse; Multidimensional data model; OLAP; Set of possible member combinations; Sparse data cube
Date of creation
19.07.2019
Date of change
19.07.2019
Short link
https://repository.rudn.ru/en/records/article/record/38410/
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