LNAI 2682 A Data Mining Query Language for Knowledge Discovery in a Geographical Information System 1st Edition by Donato Malerba, Annalisa Appice, Michelangelo Ceci ISBN 9783540224792 354022479X

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ISBN 10: 354022479X
ISBN 13: 9783540224792
Author: Donato Malerba, Annalisa Appice, Michelangelo Ceci

Spatial data mining is a process used to discover interesting but not explicitly available, highly usable patterns embedded in both spatial and non-spatial data, which are possibly stored in a spatial database. An important application of spatial data mining methods is the extraction of knowledge from a Geographic Information System (GIS). INGENS (INductive GEographic iNformation System) is a prototype GIS which integrates data mining tools to assist users in their task of topographic map interpretation. The spatial data mining process is aimed at a user who controls the parameters of the process by means of a query written in a mining query language. In this paper, we present SDMOQL (Spatial Data Mining Object Query Language), a spatial data mining query language used in INGENS, whose design is based on the standard OQL (Object Query Language). Currently, SDMOQL supports two data mining tasks: inducing classification rules and discovering association rules. For both tasks the language permits the specification of the task-relevant data, the kind of knowledge to be mined, the background knowledge and the hierarchies, the interestingness measures and the visualization for discovered patterns. Some constraints on the query language are identified by the particular mining task. The syntax of the query language is described and the application to a real repository of maps is briefly reported.

LNAI 2682 A Data Mining Query Language for Knowledge Discovery in a Geographical Information System 1st Edition Table of contents:

Chapter 1: Introduction to Data Mining and GIS

• Overview of Data Mining Techniques
• The Role of Geographic Information Systems (GIS)
• Data Mining in GIS: Opportunities and Challenges
• Structure of the Book

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Chapter 2: Fundamentals of Geographical Information Systems

• Components of GIS
• Data Types and Structures in GIS
• Spatial Data Representation and Management
• Spatial Analysis and Visualization

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Chapter 3: Introduction to Data Mining Query Languages

• What is a Data Mining Query Language?
• Query Languages for Data Analysis
• Overview of Existing Query Languages for GIS
• Designing Query Languages for Data Mining in GIS

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Chapter 4: A Data Mining Query Language for GIS

• Design Principles of the Proposed Query Language
• Syntax and Semantics of the Query Language
• Basic Query Operations in the Language
• Integrating GIS Data and Data Mining Tasks

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Chapter 5: Querying Spatial Data

• Techniques for Querying Spatial Data in GIS
• Spatial Data Types and Their Representation
• Spatial Query Operators in Data Mining
• Examples of Spatial Queries

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Chapter 6: Knowledge Discovery and Data Mining Techniques

• Classification and Regression in GIS
• Clustering and Association Rule Mining
• Predictive Analytics with Spatial Data
• Case Studies in Knowledge Discovery from GIS Data

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Chapter 7: Advanced Data Mining Queries in GIS

• Complex Query Handling
• Temporal and Multidimensional Data Mining
• Mining Large-Scale GIS Data
• Performance Optimization of Queries

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Chapter 8: Applications of Data Mining in GIS

• Applications in Environmental Science
• Applications in Urban Planning and Transportation
• GIS and Data Mining for Disaster Management
• Applications in Agriculture and Forestry

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Chapter 9: Case Studies

• Case Study 1: Mining Land Use Data for Urban Planning
• Case Study 2: Disaster Prediction and Management Using GIS
• Case Study 3: Agricultural Data Mining for Crop Prediction
• Lessons Learned from Real-World Applications

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Chapter 10: Future Directions and Research

• Emerging Trends in GIS and Data Mining
• Challenges and Opportunities in Integrating GIS with Data Mining
• Future Research Areas in Data Mining Query Languages
• The Evolution of GIS-Based Knowledge Discovery Systems

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