Conceptual model for spatial data cubes: A UML profile and its automatic implementation

2015 ◽  
Vol 38 ◽  
pp. 113-132 ◽  
Author(s):  
Kamal Boulil ◽  
Sandro Bimonte ◽  
Francois Pinet
Keyword(s):  
Conceptual Model ◽  
Spatial Data ◽  
Uml Profile ◽  
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

An UML Profile and SOLAP Datacubes Multidimensional Schemas Transformation Process for Datacubes Risk-Aware Design

2015 ◽  
Vol 11 (4) ◽  
pp. 64-83 ◽  
Author(s):  
Elodie Edoh-Alove ◽  
Sandro Bimonte ◽  
François Pinet
Keyword(s):  
Spatial Data ◽  
New Technologies ◽  
Design Method ◽  
Transformation Process ◽  
Spatial Data Analysis ◽  
Uml Profile ◽  
Previous Proposal ◽  
On Line ◽  
Definition Of ◽  
Spatial Vagueness

Spatial Data Warehouses (SDWs) and Spatial On-Line Analytical Processing (SOLAP) systems are new technologies for the integration and the analysis of huge volume of data with spatial reference. Spatial vagueness is often neglected in these types of systems and the data and analysis results are considered reliable. In a previous work, the authors provided a new design method for SOLAP datacubes that allows the handling of vague spatial data analysis issues. The method consists of tailoring SOLAP datacubes schemas to end-users tolerance levels to identified potential risks of misinterpretation they encounter when exploiting datacubes containing vague spatial data. It this paper, the authors further their previous proposal by presenting different formal tools to support their method: it is an UML profile providing stereotypes needed to add vague, risks and tolerance levels information on datacubes schemas plus the formal definition of SOLAP datacubes schemas transformation process and functions.

From Databases to Ontologies

2009 ◽  
pp. 2360-2383
Author(s):  
Guntis Barzdins ◽  
Janis Barzdins ◽  
Karlis Cerans
Keyword(s):  
Conceptual Model ◽  
Relational Database ◽  
Relational Databases ◽  
Database Design ◽  
Database Schema ◽  
Design Problems ◽  
Uml Profile ◽  
Domain Experts ◽  
Owl Ontology ◽  
Rdf Database

This chapter introduces the UML profile for OWL as an essential instrument for bridging the gap between the legacy relational databases and OWL ontologies. We address one of the long-standing relational database design problems where initial conceptual model (a semantically clear domain conceptualization ontology) gets “lost” during conversion into the normalized database schema. The problem is that such “loss” makes database inaccessible for direct query by domain experts familiar with the conceptual model only. This problem can be avoided by exporting the database into RDF according to the original conceptual model (OWL ontology) and formulating semantically clear queries in SPARQL over the RDF database. Through a detailed example we show how UML/OWL profile is facilitating this new and promising approach.

scholarly journals A Conceptual Modeling of spatio-temporal database to Estimate Runoff Changes in Urbanized Watersheds

2014 ◽  
Vol XL-2 ◽  
pp. 21-27
Author(s):  
A. L. Schäfer
Keyword(s):  
Conceptual Model ◽  
Spatial Data ◽  
Semantic Analysis ◽  
Conceptual Modeling ◽  
Curve Number ◽  
Temporal Database ◽  
Natural Resource Conservation ◽  
Natural Resource Conservation Service ◽  
The Impact ◽  
Over Time

This paper presents the development of a conceptual model of a database that allows the monitoring of changes in watersheds over time and verifies the impact of these changes on runoff. The conceptual model was developed using ER modeling techniques. ER diagrams were constructed from the semantic analysis of the variables involved in the issue of runoff in watersheds using the Curve Number method of Natural Resource Conservation Service. The conceptual model was developed based on the concepts of states and events, and the use of time as a basis for organizing spatial data allowed to record the time and place of any changes. The model of representation of spatial data based on object was employed. Through the proposed conceptual model, it is possible to store the results of several simulations for a watershed, linking each simulation to a specific event and identifying which scenario is valid at the time. Thus, it is possible to identify quantitative changes related to runoff over time and relate them to the events that caused them and the entities involved in such events. The conceptual model supports the existence of alternate realities, allowing the simulation and comparison of past and future scenarios.

scholarly journals A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions

2021 ◽  
Vol 10 (2) ◽  
pp. 87
Author(s):  
Jean-Paul Kasprzyk ◽  
Guénaël Devillet
Keyword(s):  
Spatial Data ◽  
Unified Modeling Language ◽  
Social Economy ◽  
Original Data ◽  
Heterogeneous Data ◽  
Data Cube ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Data Cubes ◽  
Geographic Analysis

Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called “Racines”, gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration.

Spatial Data Cubes based on Shared Dimensions and Neighborhood Relationship Concepts

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Joubert De Castro Lima ◽  
Tiago Garcia De Senna Carneiro ◽  
Tarik De Melo e Silva Rocha ◽  
Rodrigo Rocha Silva
Keyword(s):  
Spatial Data ◽  
Data Cubes ◽  
Neighborhood Relationship

Modeling vague spatial data warehouses using the VSCube conceptual model

2014 ◽  
Vol 18 (2) ◽  
pp. 313-356 ◽  
Author(s):  
Thiago Luís Lopes Siqueira ◽  
Cristina Dutra de Aguiar Ciferri ◽  
Valéria Cesário Times ◽  
Ricardo Rodrigues Ciferri
Keyword(s):  
Conceptual Model ◽  
Spatial Data ◽  
Data Warehouses ◽  
Spatial Data Warehouses

Merging Spatial Data Cubes using the GIS Overlay Operator

2010 ◽  
Vol 19 (3) ◽  
pp. 261-290 ◽  
Author(s):  
Sandro Bimonte ◽  
Michel Schneider ◽  
Hadj Mahboubi ◽  
Francois Pinet ◽  
Jean-Pierre Chanet
Keyword(s):  
Spatial Data ◽  
Data Cubes
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