A Multidimensional Methodology with Support for Spatio-Temporal Multigranularity in the Conceptual and Logical Phases

A Multidimensional Methodology with Support for Spatio-Temporal Multigranularity in the Conceptual and Logical Phases

Concepción M. Gascueña (Polytechnic of Madrid University, Spain) and Rafael Guadalupe (Polytechnic of Madrid University, Spain)
DOI: 10.4018/978-1-60566-232-9.ch010
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The Multidimensional Databases (MDB) are used in the Decision Support Systems (DSS) and in Geographic Information Systems (GIS); the latter locates spatial data on the Earth’s surface and studies its evolution through time. This work presents part of a methodology to design MDB, where it considers the Conceptual and Logical phases, and with related support for multiple spatio-temporal granularities. This will allow us to have multiple representations of the same spatial data, interacting with other, spatial and thematic data. In the Conceptual phase, the conceptual multidimensional model—FactEntity (FE)—is used. In the Logical phase, the rules of transformations are defined, from the FE model, to the Relational and Object Relational logical models, maintaining multidimensional semantics, and under the perspective of multiple spatial, temporal, and thematic granularities. The FE model shows constructors and hierarchical structures to deal with the multidimensional semantics on the one hand, carrying out a study on how to structure “a fact and its associated dimensions.” Thus making up the Basic factEnty, and in addition, showing rules to generate all the possible Virtual factEntities. On the other hand, with the spatial semantics, highlighting the Semantic and Geometric spatial granularities.
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The traditional databases methodologies propose to design these in three phases: Conceptual, Logical and Physical.

In the Conceptual phase, the focus is on the data types of the application, their relationships and constraints. The Logical phase is related to the implementation of the conceptual data model in a commercial System Manager Databases (DBMS), using a model more near to implementation, as for example the Relational, R model. In the Physical phase, the model of the physical design is totally dependent on the commercial DBMS chosen for the implementation.

In the design of Multidimensional databases (MDB), from a Conceptual focus, most of the models proposed use extensions to operational models such as Entity Relation (ER) or Unified Modeling Language (UML). But these models do not reflect the multidimensional or spatial semantics, because they were created for other purposes. From a Logical focus, the models gather less semantics that conceptual models. The MDB, as commented (Piattini, Marcos, Calero & Vela, 2006), have an immature technology, which suggests that there is no model accepted by the Scientific Community to model these databases.

The MDB allow us to store the data in an appropriate way for its analysis. How to structure data in the analysis and design stage, gives guidelines for physical storage. The data should be ready for the analysis to be easy and fast.

On the other hand the new technologies of databases, allow us the management of terabytes of data in less time than ever. It is now possible, to store space in databases, not as photos or images but as thousands of points and to store the evolution of space over time. But the spatial data cannot be treated as the rest of the data, as they have special features. The same spatial data can be observed and handled with different shapes and sizes. The models must enable us to represent this feature. It is of interest to get multiple interconnected representations of the same spatial object, interacting with other spatial and thematic data.

This proposal seeks to resolve these shortcomings, providing a conceptual model multidimensional, with support for multiple spatial, temporal and thematic related granularities, and rules for converting it into logical models without losing this semantics.

We propose to deal the spatial data in MDB as a dimension, and its different representations with different granularities. But we ask:

  • How to divide the spatial area of interest?

  • How to represent this area in a database?

  • In what way?

  • How big?

We answer, with the adequate space granularities. We study the spatial data and we distinguish two spatial granularity types, Semantic and Geometric. Next we define briefly these concepts, for more details read (Gascueña & Guadalupe, 2008), (Gascueña & Guadalupe, 2008c).

In the Semantic spatial granularity the area of interest is classified by means of semantic qualities such as: administrative boundaries, political, etc. A set of Semantic granularities consider the space divided into units that are part of a total, “parts-of”. These parts only change over time. And each Semantic granularity is considered a different spatial element.

A Geometric spatial granularity is defined as the unit of measurement in a Spatial Reference System, (SRS) according to which the properties of space are represented, along with geometry of representation associated with that unit. The geometry of representation can be points, lines and surfaces, or combinations of these. A spatial data can be stored and represented with different granularities. In Figure 1 we see a spatial zone divided into Plot and represented with surface and point geometric types.

Figure 1

Represented zones in surface and points geometries.

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Editorial Advisory Board
Table of Contents
David Taniar
Chapter 1
Laila Niedrite, Maris Solodovnikova Treimanis, Liga Grundmane
There are many methods in the area of data warehousing to define requirements for the development of the most appropriate conceptual model of a data... Sample PDF
Development of Data Warehouse Conceptual Models: Method Engineering Approach
Chapter 2
Stefano Rizzi
In the context of data warehouse design, a basic role is played by conceptual modeling, that provides a higher level of abstraction in describing... Sample PDF
Conceptual Modeling Solutions for the Data Warehouse
Chapter 3
Hamid Haidarian Shahri
Entity resolution (also known as duplicate elimination) is an important part of the data cleaning process, especially in data integration and... Sample PDF
A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses
Chapter 4
Maurizio Pighin, Lucio Ieronutti
Data Warehouses are increasingly used by commercial organizations to extract, from a huge amount of transactional data, concise information useful... Sample PDF
Interactive Quality-Oriented Data Warehouse Development
Chapter 5
Dirk Draheim, Oscar Mangisengi
Nowadays tracking data from activity checkpoints of unit transactions within an organization’s business processes becomes an important data resource... Sample PDF
Integrated Business and Production Process Data Warehousing
Chapter 6
Jorge Loureiro, Orlando Belo
OLAP queries are characterized by short answering times. Materialized cube views, a pre-aggregation and storage of group-by values, are one of the... Sample PDF
Selecting and Allocating Cubes in Multi-Node OLAP Systems: An Evolutionary Approach
Chapter 7
Jorge Loureiro, Orlando Belo
Globalization and market deregulation has increased business competition, which imposed OLAP data and technologies as one of the great enterprise’s... Sample PDF
Swarm Quant' Intelligence for Optimizing Multi-Node OLAP Systems
Chapter 8
Franck Ravat, Olivier Teste, Ronan Tournier
With the emergence of Semi-structured data format (such as XML), the storage of documents in centralised facilities appeared as a natural adaptation... Sample PDF
Multidimensional Anlaysis of XML Document Contents with OLAP Dimensions
Chapter 9
Hanene Ben-Abdallah, Jamel Feki, Mounira Ben Abdallah
Despite their strategic importance, the wide-spread usage of decision support systems remains limited by both the complexity of their design and the... Sample PDF
A Multidimensional Pattern Based Approach for the Design of Data Marts
Chapter 10
Concepción M. Gascueña, Rafael Guadalupe
The Multidimensional Databases (MDB) are used in the Decision Support Systems (DSS) and in Geographic Information Systems (GIS); the latter locates... Sample PDF
A Multidimensional Methodology with Support for Spatio-Temporal Multigranularity in the Conceptual and Logical Phases
Chapter 11
Francisco Araque, Alberto Salguero, Cecilia Delgado
One of the most complex issues of the integration and transformation interface is the case where there are multiple sources for a single data... Sample PDF
Methodology for Improving Data Warehouse Design using Data Sources Temporal Metadata
Chapter 12
Shi-Ming Huang, John Tait, Chun-Hao Su, Chih-Fong Tsai
Data warehousing is a popular technology, which aims at improving decision-making ability. As the result of an increasingly competitive environment... Sample PDF
Using Active Rules to Maintain Data Consistency in Data Warehouse Systems
Chapter 13
Marcin Gorawski, Wojciech Gebczyk
This chapter describes realization of distributed approach to continuous queries with kNN join processing in the spatial telemetric data warehouse.... Sample PDF
Distributed Approach to Continuous Queries with kNN Join Processing in Spatial Telemetric Data Warehouse
Chapter 14
Maria Luisa Damiani, Stefano Spaccapietra
This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been... Sample PDF
Spatial Data Warehouse Modelling
Chapter 15
Jérôme Darmont
Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with... Sample PDF
Data Warehouse Benchmarking with DWEB
Chapter 16
Lars Frank, Christian Frank
A Star Schema Data Warehouse looks like a star with a central, so-called fact table, in the middle, surrounded by so-called dimension tables with... Sample PDF
Analyses and Evaluation of Responses to Slowly Changing Dimensions in Data Warehouses
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