scholarly journals Multidimensional Space Structure for Adaptable Data Model

2019 ◽  
Vol 8 (3) ◽  
pp. 7753-7758
Keyword(s):  
Data Model ◽  
Cartesian Product ◽  
Data Representation ◽  
Space Structure ◽  
Automated System ◽  
Relational Model ◽  
Multidimensional Data ◽  
Multidimensional Space ◽  
Model Based ◽  
Five Axes

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.

A multidimensional data model using the fuzzy model based on the semantic translation

2013 ◽  
Vol 15 (3) ◽  
pp. 351-370 ◽  
Author(s):  
Ramón A. Carrasco ◽  
Francisco Muñoz-Leiva ◽  
Miguel J. Hornos
Keyword(s):  
Data Model ◽  
Fuzzy Model ◽  
Multidimensional Data ◽  
Model Based ◽  
Multidimensional Data Model ◽  
Semantic Translation

scholarly journals Processing Archive Information in Digital University

2020 ◽  
Vol 35 ◽  
pp. 02003
Author(s):  
Alexander V. Baldin ◽  
Dmitriy V. Eliseev
Keyword(s):  
Human Resource ◽  
Relational Databases ◽  
Space Structure ◽  
Information Storage ◽  
Relational Model ◽  
Multidimensional Space ◽  
Data Archive ◽  
Temporal Data ◽  
Processing Data ◽  
Time Required

The article discusses methods of processing and storing data archive used in the digital university. Disadvantages of these methods are found. As a result, a fundamentally new method of processing and storing information archive in a constantly changing scheme database is proposed. This method uses mivar technologies. The multidimensional space structure has been developed to store the data archive. This multidimensional space describes the temporal relational model. For processing data, archive is proposed scheme for selecting the subspace and converting it into relations. A method of transformation of relational databases into multidimensional mivar space for efficient execution of operations on temporal data with changing structure is proposed. The transition to a multidimensional space allows us to describe the process of changing temporal data and their structure in a unified way. As a result, the time required to adapt the database schema and the redundancy of information storage are reduced. The results of this work are used in the human resource management database of BMSTU.

A Qualified Assertion Database for the History of Places

2019 ◽  
Vol 13 (1-2) ◽  
pp. 95-115
Author(s):  
Brandon Plewe
Keyword(s):  
Data Model ◽  
Temporal Change ◽  
Religious Congregations ◽  
Historical Gis ◽  
The Past ◽  
Model Based ◽  
Complex Information ◽  
Invaluable Tool ◽  
History Of ◽  
The Rich

Historical place databases can be an invaluable tool for capturing the rich meaning of past places. However, this richness presents obstacles to success: the daunting need to simultaneously represent complex information such as temporal change, uncertainty, relationships, and thorough sourcing has been an obstacle to historical GIS in the past. The Qualified Assertion Model developed in this paper can represent a variety of historical complexities using a single, simple, flexible data model based on a) documenting assertions of the past world rather than claiming to know the exact truth, and b) qualifying the scope, provenance, quality, and syntactics of those assertions. This model was successfully implemented in a production-strength historical gazetteer of religious congregations, demonstrating its effectiveness and some challenges.

scholarly journals A Design of a Data Model Based on Abstraction of Symbols

1985 ◽  
Vol 28 (3) ◽  
pp. 298-308 ◽  
Author(s):  
T. D. Kimura
Keyword(s):  
Data Model ◽  
Model Based

AN ARCHITECTURE FOR DATA WAREHOUSING SUPPORTING DATA INDEPENDENCE AND INTEROPERABILITY

2001 ◽  
Vol 10 (03) ◽  
pp. 377-397 ◽  
Author(s):  
LUCA CABIBBO ◽  
RICCARDO TORLONE
Keyword(s):  
Data Warehouse ◽  
Data Model ◽  
Data Warehousing ◽  
Heterogeneous Data ◽  
Multidimensional Data ◽  
Multidimensional Databases ◽  
Level Of Aggregation ◽  
High Level ◽  
Data Independence ◽  
Logical Architecture

We report on the design of a novel architecture for data warehousing based on the introduction of an explicit "logical" layer to the traditional data warehousing framework. This layer serves to guarantee a complete independence of OLAP applications from the physical storage structure of the data warehouse and thus allows users and applications to manipulate multidimensional data ignoring implementation details. For example, it makes possible the modification of the data warehouse organization (e.g. MOLAP or ROLAP implementation, star scheme or snowflake scheme structure) without influencing the high level description of multidimensional data and programs that use the data. Also, it supports the integration of multidimensional data stored in heterogeneous OLAP servers. We propose [Formula: see text], a simple data model for multidimensional databases, as the reference for the logical layer. [Formula: see text] provides an abstract formalism to describe the basic concepts that can be found in any OLAP system (fact, dimension, level of aggregation, and measure). We show that [Formula: see text] databases can be implemented in both relational and multidimensional storage systems. We also show that [Formula: see text] can be profitably used in OLAP applications as front-end. We finally describe the design of a practical system that supports the above logical architecture; this system is used to show in practice how the architecture we propose can hide implementation details and provides a support for interoperability between different and possibly heterogeneous data warehouse applications.

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