It is important that this reintroduction of the spatial information market does not automatically presuppose the demise of conventional GIS. The bond between these two groups of users is their common interest in the state of the art of spatial database systems.
INTRODUCTION
THE CURRENT STATUS OF SPATIAL INFORMATION TECHNOLOGY
- INTRODUCTION
- ADVANCES OF SPATIAL INFORMATION CONCEPTS AND TECHNOLOGY
- A New Metaphor of Spatial Information
- The Merging of Spatial Information with Mainstream Information Technology
- Institutionalisation of Spatial Database Systems
- A Data-based and User-centric Approach to Spatial Information
- KNOWLEDGE AND SKILLS FOR SPATIAL DATABASE SYSTEMS
- ORGANISATION AND OVERVIEW OF THIS BOOK
- REFERENCES
The spatial database component plays a central and critical role in the new spatial information metaphor. The merging of spatial information with mainstream IT has naturally led to the institutionalization of spatial database systems.
DATABASE PRINCIPLES AND ARCHITECTURE
CONCEPTS AND ARCHITECTURE OF DATABASE SYSTEMS
DATABASES AND DATABASE SYSTEMS
- Database Terminology
- Computer data organisation and database
- Classification of database systems
In database implementation projects, the development of the data model and database schema is part of a design process. Another way of classifying database systems is to use the characteristics of the data in the database as the primary criterion for classification.
DATABASE OPERATIONS
- Database Storage and Manipulation
- Database Security and Integrity Constraints
- Database Query
- Database Transactions
- Database Backup and Recovery
- Database Replication and Synchronisation
- Structured Query Language (SQL)
Database integrity constraints are enforced by enforcing certain rules, called business rules, that govern the structure and use of data in the database. A hot backup can be used to recover the database in the event of a system failure.
HARDWARE AND SOFTWARE ARCHITECTURE
- Centralised and Distributed Database Architecture
- Client/server Computing
- Database Software
- Web-based Database Architecture
Typically, in the three-tier case, the application server is more powerful than the client(s), and system performance improves as a result. It is possible to enhance the functionality of the web browser on the client computer by adding client-side extensions.
DATA STRUCTURE
- Logical Data Structure
- Physical Data Structure
- Database Indexing
Java, an object-oriented language that can be used to develop applications to run on top of a web browser. An index is an element of a data structure used to speed up access to a specific part of a database.
SUMMARY
It is important to understand and distinguish between the concepts of logical and physical data structures, and to identify the impacts that database structure and indexing have on database system performance. The structure of tables in relational database systems and the B-tree indexing method were explained, as well as the relationship between the logical data structure of a table space, the physical data structure of database blocks, and the structure of computer operating system data storage.
The architecture of client/server systems was explained along with the construction of database systems using two-tier and three-tier client/server architectures. Knowledge of the general concepts and methods of database systems discussed in this chapter is necessary to study the database models and spatial database systems discussed in the next two chapters.
DATABASE MODELS AND DATA MODELLING
DEFINITIONS AND CONCEPTS
- Definition of a Database Model
- Database Model, Schema and Instance
- Conceptual, Logical and Physical Data Modelling
- The Importance of Database Models and Data Modelling
As the data modeling process evolves, an identified concept becomes an "entity" in the language of a relational database model or an "object" in the language of an object-oriented database model. This stage of data modeling is aptly called conceptual data modeling or conceptual database design. All activities involved in generating a logical schema are collectively referred to as logical data modeling or logical database.
Hardware requirements in data modeling include the computer and system architecture of the database system, the physical location of data files as well as the specific allocation of storage space to data objects in the respective data files. All database projects, large and small, can benefit from the use of a database model and data modeling to some extent.
COMMON DATABASE MODELS
- Entity-relationship (E/R) Model
- Relational Model
- Object-oriented (OO) Model
- Object-relational Model
The central concept of the E/R model is an entity, also called a data object or simply an object. As mentioned above, the central concept of a relational model is a table or relation (Figure 3.7). The word "entity" is a classic example of the lack of standardized terminology in the database world.
When calling a method, the sender of the message accesses the object's actions, not its internal structure. This hiding of the internal details of an object, known as encapsulation, is designed to protect the integrity of the object's state.
PRINCIPLES AND TECHNIQUES OF DATA MODELLING
- The Four Principles of Data Modelling
- The Systems and Database Development Life Cycle
- Case Tools
- User-centred Database Design
- Data Modelling Documentation
Consequently, this will lead to changes to the database implementation plan as proposed. Conceptual, logical and physical modeling in the context of the database development life cycle (DBDLC). This class of tools enables a database designer to create a model of the database to be constructed.
The previous subsections discussed the principles and techniques of data modeling used to create practical and realistic representations of the real world. This means that capturing user requirements is only part of the goals of data modeling.
SUMMARY
However, it is important to understand the value of good documentation to the quality of any data modeling task and the consequences of poor documentation to a database project. Experience has shown that a database designer who is unwilling to take the time to create appropriate documentation in the data modeling phase will often inadvertently create unforeseen problems in later implementation and usage phases. of the database.
1997) A First Course in Database Systems, Upper Saddle River, NJ: Prentice-Hall, Inc. 2001) User-Centered Design: An Interactive Approach, Upper Saddle River, NJ: Prentice-Hall, Inc.
SPATIAL DATA AND SPATIAL DATABASE SYSTEMS
DEFINITION AND CLASSIFICATION OF SPATIAL DATA
- Spatial Data and Pseudo-spatial Data
- A Functional Perspective of Spatial Data
Spatial data is data that can be displayed, manipulated, and analyzed using a spatial attribute that indicates a location on or near the Earth's surface. Spatial data is collected and stored in two basic forms, called vector and raster (Figure 4.1). Pseudospatial data is an important and valuable data source for creating spatial databases.
However, data conversion technologies are now considerably more mature than in the earlier days of spatial database construction. The type of data in a spatial data layer is of secondary importance because the conventional differences between.
SPATIAL DATA STRUCTURE AND DATABASE MODELS
- The Concept of a “Geometry” of Spatial Data
- The Concept of Topology and Topological Data Structures
- Non-topological Data Structure
- The Geo-relational Model
- The Geodatabase Model
A spatial data set represented in this way is said to have a “full polygon” data structure. A spatial data set represented with explicitly stored spatial relationships has a topological data structure. a) Non-topological (cartographic) data structure (b) Topological data structure. In the geo-relational model, attribute data associated with spatial data in each of the layers is stored in separate relational tables (also known as attribute tables).
In the new generation of systems, spatial data that share the same attributes (ie, data of the same feature class) are stored in a single table. The structure of a spatial database using a DBMS for storing spatial data and topological relationships.
SPATIAL DATABASE SYSTEMS
- Definition and Classification of Spatial Database Systems
- Characteristics of Spatial Database Systems
- Spatial Data Types (user-defined or abstract data types)
- Spatial Data Indexing and Access Method
- Spatial Data Integrity and Constraints
- Long Transaction Management
- Spatial Data Processing
- Classification of Spatial Operators
- Spatial Operations and Filtering
- Topological Relations and Predicates
- Spatial Joins
- Spatial SQL
In contemporary spatial data processing environments, the division of labor between spatial database systems and GIS is quite clear (Table 4.1). The focus is on managing spatial data through a systematic approach to database implementation and project management. The features discussed include spatial data types, spatial database indexing, spatial data integrity constraints, and transactions in spatial data processing.
A knowledge of the properties of different types of spatial operators is required to form acceptable spatial queries. However, most of the early efforts focused on image databases in general rather than geographic data in particular.
SPATIAL DATA STANDARDS AND METADATA
STANDARDS AND STANDARDISATION
- Definition of Standards
- Forms of Standards
- Approaches to Implementation
- Level of Granularity
- Classification of Standards
- Standards Organisations
- Standards Development
- Standards Implementation
Proprietary standards are developed by organizations for internal use in producing products or providing services. It is important to understand that there are two types of standards organizations that play relatively distinct roles in the standards development process, namely accrediting standards organizations and standards development. An open standards development project is usually initiated with a need for a standard that, in turn, results in a proposal being submitted to a standards development organization.
Consensus in standards development is defined as substantial agreement among those involved in the process. Failure to comply with mandatory standards will also result in inefficiency or incompatibility in the use of products and services.
SPATIAL DATA STANDARDS
- The Importance of Spatial Data Standards
- Standards for Spatial Database Systems
- Hardware Standards
- Software Standards
- Telecommunications Network and Web Services Standards These standards include the transfer of data over global and local
- Data Standards
- Examples of Spatial Data Standards
- Spatial Data Standards in Canada
- Spatial Data Standards in the United States
- International Spatial Data Standards
- OGC Spatial Data Standards
SDTS has been designated as a mandatory spatial data standard for agencies of the United States government. Data standards are expressly declared as a cornerstone of the Global Spatial Data Infrastructure (GSDI), the Canadian Geospatial Data Infrastructure (CGDI) and the American National Spatial Data Infrastructure (NSDI). Development of spatial data standards in the United States is coordinated by the ANSI.
As noted above, international spatial data standards are conventionally developed under the umbrella of the ISO 19100 series of standards. ISO TC211 standards are widely used and adopted for use as national spatial data standards.
THE CONCEPTS AND METHODS OF METADATA
- A Definition of Metadata
- The Importance of Metadata
- Uniformity of Data Collection
- Data Management
- Data Use
- Data Understanding
- Data Sharing
- Data Archiving and Warehousing
- Spatial Metadata Standards
- Z39.50 Application Profile for Geospatial Metadata
- Content Standard for Digital Geospatial Metadata (CSDGM) This standard, as noted earlier, was developed by the United States
- ISO 19115 - Metadata
- Spatial Metadata Tools
- Metadata Capture and Documentation Tools
- Metadata Utilities
- Metadata Solution Toolboxes
- Encoding Tools
- The Process of Implementing Spatial Metadata
Spatial metadata is a special type of metadata that is associated with a spatial database, a set of spatial data, or a specific class or instance of spatial features. New developments in spatial repositories and the use of metadata for legacy data mining are discussed in more detail in Chapter 6. The goal is to define and standardize an extensive set of metadata elements and their characteristics, as well as a scheme, which is necessary for comprehensive and comprehensive documentation of spatial data.
Numerous tools exist to assist in capturing and formatting metadata, and providing metadata services. There are four main types of metadata capture and editing tools, which vary depending on the degree of user intervention required to extract the metadata elements.
DATA STANDARDS AND METADATA IN SPATIAL DATABASE SYSTEMS
- Issues with Implementing Standards and Metadata in Spatial Database Systems
- A Model of Using Standards and Metadata in Spatial Database Design and Implementation
One of the most controversial concerns the relationship between metadata and the datasets they describe. The advantage of separating metadata from datasets is to manage the metadata without affecting the contents of the dataset. Based on the clear importance of standards in all aspects of geospatial data activities, a standards-based approach to spatial database design and implementation can be proposed, as shown in Figure 5.13.
In this context, data standards are one of the main sets of standards to be implemented. CGDI (2003) Geospatial Standards Thrust of the Canadian Geospatial Data Infrastructure, Ottawa, ON: Canadian Geospatial Data Infrastructure.
SPATIAL DATA SHARING, DATA WAREHOUSING AND DATABASE
THE CONCEPTS AND METHODS OF SPATIAL DATA SHARING
- The Definition and Nature of Spatial Data Sharing
- The Importance of Spatial Data Sharing
- Barriers to Spatial Data Sharing
- A Standards-based Framework for Spatial Data Sharing
- Object Linking and Embedding
- Open Database Connectivity
- Java Database Connectivity
- Web Services Protocols
Functional spatial data sharing is essentially file-based and performed by standalone computer programs. The greatest interest in modern spatial data sharing focuses on enterprise and infrastructural data sharing. Infrastructural sharing of geographic data is based on the concept of a geographic data infrastructure (SDI), the purpose of which is not to establish a single central database, but to establish a distributed network of databases, based on accepted standards, managed and controlled by individual organizations in the public, academic and industrial sectors at local, state/provincial, national, regional and global levels (Nebert, 2001).
The nature of spatial data sharing is fundamentally different than it was in the past. Important implementation and institutional issues of spatial data sharing are discussed in Part 3 of this book.
DATABASE HETEROGENEITY AND ITS SOLUTIONS
- The Nature and Characteristics of Database Heterogeneity
