1. INTRODUCTION
Many organizations face the challenge of managing and presenting the sheer quantity of data being captured on a monthly, weekly, daily and hourly level. The introduction of business intelligence (BI) applications and technologies has helped organizations gather, provide access to, analyze, and present data and information easily to the decision makers. The applications utilize both relational and multidimensional technologies to form the overall BI infrastructure. From a historical perspective BI is a popularized umbrella term introduced by Howard Dresner of the Gartner Group in 1989 to describe a set of concepts and methods to improve business decision making by using fact-based support systems. BI is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI solutions include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting and data mining. Microsoft defines BI as: THE PROCESS OF EXTRACTING DATA FROM A DATABASE AND THEN ANALYZING THAT DATA FOR INFORMATION THAT YOU CAN USE TO MAKE INFORMED BSINESS DECISIONS AND TAKE ACTION. However, data is not always used to its full potential and part of its richness, the spatial component, is simply left out. It has been estimated that about 80% of the data stored in corporate databases integrates spatial information that can be characterized by position, shape, orientation or size (Frankin, April 1992). It is obvious that this meaningful data is worth being integrated in the decision making process to provide a complete operational picture.
To gain better advantage of the spatial dimension in decision making the appropriate tools must be used. Geographic Information Systems (GIS) are the obvious potential candidate for such a task. (Worboys, 1995) provide this typical definition of a conventional GIS: A GIS IS A COMPUTERBASED INFORMATION SYSTEM THAT ENABLES CAPTURE, MODELING, MANIPULATION, RETRIEVAL, AND PRESENTATION OF GEOGRAPHICALLY REFERENCED DATA. GIS provides functionalities like
Table of Contents
1. Introduction
1.1 Motivation
1.2 Research Problem
1.3 Research Approach
2. Application Scenarios
2.1 Scenario 1 – The Business Man
2.2 Scenario 2 – Knowledge is the Key
2.3 Scenario 3 – Deutsche Presse Agentur
2.4 Summary
3. General Concepts
3.1 Web Services – An Introduction
3.1.1 Extensible Markup Language
3.1.2 Geography Markup Language
3.1.3 Simple Object Access Protocol
3.1.4 Universal Description, Discovery and Integration
3.1.5 Web Services Description Language
3.2 OnLine Analytical Processing – An Overview
3.2.1 OLAP Concepts
3.2.2 OLAP Operations and Architecture
3.2.3 OLAP Querying Capabilities
3.3 OGC Web Services – Interoperable Geoprocessing
3.3.1 Web Map Service
3.3.2 Web Feature Service
3.3.3 Web Coverage Service
3.3.4 Summary
3.4 Research Approach – Revisited
4. Related Work
4.1 Introduction
4.2 Spatial OLAP Solutions – An Overview
4.3 Summary
5. Case Study: A strategy for Web Coverage Service and Online Analytical Processing Interoperability
5.1 Introduction
5.2 Platform and Software Requirements
5.2.1 GIS Domain
5.2.2 OLAP Domain
5.3 The Data Basis
5.3.1 Deutsche Presse Agentur Dataset
5.3.2 German Cities and State Geographic Data
5.4 Mediation Process – Conceptual Mapping
5.4.1 Mapping a Coverage to a Cube
5.4.2 Service Configuration
5.4.3 Mapping and Wrapping – GML to XMLA
5.5 Proof of Concept
5.6 Summary
6. Conclusion and Future Work
6.1 Conclusion
6.2 Recommendations and Future Work
Research Objectives and Themes
This thesis aims to develop an interoperable, web-based hybrid Spatial OLAP (SOLAP) solution that supports spatial and multidimensional information exploration. By integrating Web Coverage Service (WCS) technology with an OLAP server, the study seeks to overcome the challenges of proprietary, monolithic systems that impede data interoperability and flexible analysis in decision-support processes.
- Integration of Business Intelligence (BI) and Geographic Information Services (GIS).
- Development of a web-based, extensible SOLAP prototype using open standards.
- Abstracting the complexity of separately querying disparate spatial and multidimensional datasets.
- Evaluation of interoperability through a case study using the Deutsche Presse Agentur (dpa) dataset.
Excerpt from the book
1. Introduction
Many organizations face the challenge of managing and presenting the sheer quantity of data being captured on a monthly, weekly, daily and hourly level. The introduction of business intelligence (BI) applications and technologies has helped organizations gather, provide access to, analyze, and present data and information easily to the decision makers. The applications utilize both relational and multidimensional technologies to form the overall BI infrastructure. From a historical perspective BI is a popularized umbrella term introduced by Howard Dresner of the Gartner Group in 1989 to describe a set of concepts and methods to improve business decision making by using fact-based support systems.
BI is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI solutions include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting and data mining. Microsoft defines BI as: THE PROCESS OF EXTRACTING DATA FROM A DATABASE AND THEN ANALYZING THAT DATA FOR INFORMATION THAT YOU CAN USE TO MAKE INFORMED BUSINESS DECISIONS AND TAKE ACTION. However, data is not always used to its full potential and part of its richness, the spatial component, is simply left out. It has been estimated that about 80% of the data stored in corporate databases integrates spatial information that can be characterized by position, shape, orientation or size (Frankin, April 1992). It is obvious that this meaningful data is worth being integrated in the decision making process to provide a complete operational picture.
Summary of Chapters
1. Introduction: Presents the motivation and research problem regarding the integration of BI and GIS, focusing on the need for interoperable SOLAP solutions.
2. Application Scenarios: Describes various business scenarios, including retail and news agency datasets, to identify the requirements for a SOLAP solution.
3. General Concepts: Provides an overview of web service technologies (XML, WSDL, etc.), OLAP principles, and OGC standards, particularly the Web Coverage Service.
4. Related Work: Evaluates existing academic and commercial SOLAP implementations against the research requirements to highlight the niche of this study.
5. Case Study: A strategy for Web Coverage Service and Online Analytical Processing Interoperability: Details the architecture, software requirements, and implementation of the proposed prototype using the dpa dataset.
6. Conclusion and Future Work: Summarizes the study's achievements and offers recommendations for future improvements in interoperable geoprocessing.
Keywords
BI, DPA, GIS, GIS Web Services, GML, OGC, OLAP, SOLAP, WCS, Web Services, Spatiotemporal Analysis, Interoperability, XMLA, MDX, Data Cube.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on creating an interoperable, web-based hybrid SOLAP platform that integrates GIS and OLAP technologies to improve decision-making through better access to spatial and multidimensional data.
What are the primary thematic fields involved?
The work combines Business Intelligence (BI), Geographic Information Systems (GIS), and Web Services architecture to create a unified data exploration environment.
What is the primary research question?
The study asks how existing proprietary and siloed solutions can be moved toward an open, interoperable web-based platform that abstracts the complexity of querying spatial and multidimensional data simultaneously.
Which scientific method is applied?
The author uses a prototyping approach, specifically building an augmented Web Coverage Service (WCS) that interfaces with an OLAP server to demonstrate the feasibility of the proposed integration.
What does the main part of the thesis cover?
The main part includes a theoretical framework of web services and OLAP, an evaluation of existing related work, and a practical case study using the Deutsche Presse Agentur dataset to validate the prototype.
Which keywords best describe this study?
The work is best characterized by terms like SOLAP, WCS, OLAP, GIS, Interoperability, and Spatiotemporal Data.
Why is the Web Coverage Service (WCS) used in the prototype?
The WCS is chosen because, unlike WMS which only provides map images, WCS offers access to raw data that can be interpreted and used for multidimensional analysis, making it more suitable for a hybrid SOLAP solution.
What is the role of the Deutsche Presse Agentur (dpa) dataset?
The dpa dataset serves as the practical use case for evaluating the prototype, providing real-world data with spatial (city locations) and multidimensional (news categories, time, priority) components.
- Quote paper
- Anup Deshmukh (Author), 2007, Towards a web coverage service for efficient multidimensional information retrieval, Munich, GRIN Verlag, https://www.grin.com/document/87634
