Open AccessMultidimensional Space Structure for Adaptable Data Model
Author(s) -
Oleksandr Terentyev,
Svitlana Tsiutsiura,
Tetyana Honcharenko,
Tamara Liashchenko
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c6318.098319
Subject(s) - representation (politics) , computer science , identifier , data model (gis) , relational model , space (punctuation) , cartesian product , relational database , data structure , table (database) , set (abstract data type) , multidimensional analysis , data mining , granularity , theoretical computer science , online analytical processing , logical data model , database , data modeling , mathematics , data warehouse , artificial intelligence , programming language , discrete mathematics , statistics , politics , political science , law , operating system
The article presents an adaptable data model based on multidimensional space. The main difference between a multidimensional data representation and a table representation used in relational Database Management Systems (DBMSs) is that it is possible to add new elements to sets defining the axes of multidimensional space at any time. This changes the data model. The tabular representation of the relational model does not allow you to change the model itself during the operation of an automated system. Three levels of multidimensional data presentation space are considered. There are axis of multidimensional space, the Cartesian product of the sets of axis values and the values of space points. The five axes of multidimensional space defined in the article (entities, attributes, identifiers, time, modifiers) are basic for the design of an adaptable automated system. It is shown that it is possible to use additional axes for greater granularity of the stored data. The multidimensional space structure defined in the article for an adaptable data model is a flexible set for storing a relational domain model. Two types of operations in multidimensional information space are defined. Relations of the relational model are formed dynamically depending on the conditions imposed on the coordinates of the points. Thus, an adaptable data representation model based on multidimensional space can be used to create flexible dynamic automated information systems.