Project topics and materials
Get Complete Project Material File(s) Now! »
An analysis of a data grid approach for spatial data infrastructures
Grid computing started in the 1990s as a future generation computing paradigm for high performance computing. The initial goals were to extend processing and data storage capacities from individual expensive machines to clusters of inexpensive commodity machines, mainly for use in the scientific domain. The vision was to create a ‘grid’ of networked computers into which anyone could tap for processing and data storage capacity, analogous to a power grid into which we tap for electrical power (Foster and Kesselman 1999). Some ideas originating from grid research have permeated into all areas of distributed computing, changing the way in which distributed systems are designed, developed and implemented by addressing the needs for flexible, secure, coordinated resources sharing among members of a virtual organization comprising individuals, institutions and resources from different administrative domains (Foster et al. 2001, Talia 2002, Ripeanu et al. 2008).
Most grids have a service-oriented architecture and there is close cooperation with the world of web services (Foster 2003, Baker et al. 2005, Cohen et al. 2008), which are software systems that support interoperable machine-to-machine interaction over a network (Haas and Brown 2004). Grid and web service technologies complement and influence each other, and since both are fairly young it is entirely possible that in future they will become fully compatible and the distinction between the two will fade (Plaszczak and Wellner 2006) so that at some point in future they might be known under a single name. Grid computing research has also been the breeding ground for new technologies known under different names, such as, cloud computing, the latest catchphrase in industry, which shares the same original vision of grid computing articulated in the 1990s by Foster, Kesselman and others, but with significant differences (Weiss 2007, Delic and Walker 2008).
Over the past few years ‘geobrowsers’, such as Google Earth, NASA World Wind and Virtual Earth along with in-vehicle navigation, handheld GPS devices and maps on mobile phones, have made interactive maps and geographic information an everyday experience. Behind these maps lies a wealth of spatial data that is often collated from a vast amount of different sources. Consolidating spatial data from distributed heterogeneous sources into a single centralized dataset that can be published online is a time consuming effort, requiring, among others, a considerable coordination effort, as well as syntactic and semantic data harmonization. A spatial data infrastructure (SDI) aims to make spatial data usable by people, and the technologies, systems (hardware and software),standards, policies, agreements, human and economic resources, institutions, and organizationa aspects have to be carefully orchestrated to make this possible (Jacoby 2002, Crompvoets et al. 2004, Georgiadou et al. 2005, De Man 2006, Rajabifard et al. 2006, Masser et al. 2007). SDI research provides insights into understanding and improving the consolidation of heterogeneous distributed databases and making these available to as wide an audience as possible (Williamson et al. 2006, Masser et al. 2007, Rajabifard 2008).
This dissertation spans two disciplines, namely Computer Science (CS) and Geographic Information Science (GISc). The data grid approach (CS) as the enabling technology for sharing geographic information, such as address data, in an SDI (GISc) is presented and analyzed. This first chapter introduces the reader to address data in an SDI (GISc) and to data grids (CS), and then presents two scenarios (developed by the author) that illustrate how data grids could in future enable the sharing of address data in an SDI. Subsequently, the research presented in this dissertation is related to current research agendas in the two disciplines. The chapter is concluded with an overview of the contributions from the work described in this dissertation to scientific research, and a guide for the reader to the remaining chapters of the dissertation. Figure 1 illustrates how the chapters in this dissertation relate to the two disciplines.
Chapter 1 Introduction
1.1 An analysis of a data grid approach for spatial data infrastructures .
1.2 Address data in an SDI .
1.3 Data grids
1.4 Enabling spatial data infrastructures with data grids
1.5 Computer Science and Geographic Information Science in this dissertation
1.6 Contributions to scientific research from this dissertation
1.7 Guide to the remaining chapters of this dissertation
Chapter 2 Address data in an SDI
2.1 Introduction
2.2 Address data
2.3 Spatial Data Infrastructure (SDI)
2.5 Standards and technologies for address data in an SDI
2.6 Related Work
Chapter 3 Data grids
3.1 Introduction
3.2 Grid computing
3.3 The Grid architecture
3.4 Data grids .
3.5 Examples of data grid implementations
3.6 Related work .
Chapter 4 Compartimos, a reference model for an address data grid in an SDI .
4.1 Introduction
4.2 Enterprise viewpoint .
4.3 Information viewpoint .
4.4 Computational viewpoint
4.5 Engineering viewpoint
4.6 Discussion .
Chapter 5 Implementation and evaluation of Compartimos .
5.1 Introduction .
5.2 Technology choices of specific Compartimos objects .
5.3 Overall technology choices for Compartimos
5.4 Proof of concept implementation of Compartimos
5.5 Evaluation of Compartimos
Chapter 6 Address databases for national SDI: Comparing the novel data grid approach to data harvesting and federated databases
Chapter 7 Conclusion
References .
Referenced Standards
Other references .
Appendix A. Acronyms and abbreviations
Appendix B. Compartimos data
Appendix C. Operations of the Compartimos service objects
Appendix E. Journal publications .
