Semantic Integration of Coastal Buoys Data using SPARQL

Currently, the data provided by the heterogeneous buoy sensors/networks (e.g. National Data Buoy center (NDBC), Gulf Of Maine Ocean Observing System (GoMoos) etc. is not amenable to the development of integrated systems due to conflicts in the data representation at syntactic and structural levels. With the rapid increase in the amount of information, the integration of heterogeneous resources is an important issue and requires integrative technologies such as semantic web. In distributed data dissemination system, normally querying on single database will not provide relevant information and requires querying across interrelated data sources to retrieve holistic information. In this thesis we develop system for integrating two different Resource Description Framework (RDF) data sources through intelligent querying using Simple Protocol and RDF Query Language (SPARQL). We use Semantic Web application framework from AllegroGraph that provides functionality for developing triple store for the ontological representations, forming federated stores and querying it through SPARQL

Resolving horizontal partitioning and schematic variances using metadatabase approach.

by Poon, Koon-hei.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 80-83).Abstracts in English and Chinese.Chapter CHAPTER 1 --- INTRODUCTION --- p.6Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.13Chapter 2.1. --- BACKGROUND --- p.13Chapter 2.2. --- example systems --- p.20Chapter 2.2.1 --- Multibase --- p.20Chapter 2.2.2. --- Mermai d --- p.23Chapter 2.2.3. --- The Metadatabase Approach --- p.26Chapter 2.3. --- SUMMARY --- p.29Chapter CHAPTER 3 --- THE METADATABASE APPROACH --- p.31Chapter 3.1. --- Two-Stage Entity Relationship (TSER) model --- p.31Chapter 3.2. --- The GIRD --- p.34Chapter 3.3. --- The Metadatabase system in action --- p.36Chapter 3.3. --- global query formulations and processing in the metadatabase system --- p.37Chapter CHAPTER 4 --- PROBLEM OUTLINES FOR HORIZONTAL PARTITIONING AND ITS VARIANTS --- p.39Chapter 4.1. --- Horizontal partitioning --- p.39Chapter 4.2. --- Level of abstraction --- p.41Chapter 4.3. --- Schematic variances --- p.42Chapter 4.4. --- Summary --- p.43Chapter 4.5. --- The Scenario --- p.44Chapter 4.6. --- Populating the Metadatabase --- p.48Chapter CHAPTER 5 --- THE ENHANCEMENTS FOR GLOBAL QUERY WITH HORIZONTAL PARTITIONED DATA OBJECTS --- p.51Chapter 5.1. --- Identifying partitioned data objects --- p.51Chapter 5.2. --- Additional metadata for the horizontal partitioned data objects --- p.52Chapter 5.3. --- Complications of horizontal partitioning problem --- p.54Chapter 5.3.1. --- Level of abstraction --- p.55Chapter 5.3.2. --- Schematic variances --- p.57Chapter 5.4. --- Global query with horizontal partitioning data objects --- p.59Chapter 5.5. --- Housing the new metadata --- p.68Chapter 5.6. --- Example --- p.72Chapter CHAPTER 6 --- ANALYSIS --- p.75Chapter CHAPTER 7 --- CONCLUSION AND FUTURE WORKS --- p.78REFERENCES --- p.80APPENDICES --- p.84Chapter A. --- GIRD Definitions --- p.84Chapter A1. --- GIRD Model --- p.84Chapter A2. --- GIRD/SER Contents --- p.84Chapter A3. --- GIRD/OER Constructs --- p.87Chapter A4. --- Definition of Meta-attributes --- p.89Chapter B. --- Problems Representations in Relation Algebra --- p.96Chapter B1. --- Horizontal problem --- p.96Chapter B2. --- Level of abstraction --- p.96Chapter B3. --- Schematic Variance --- p.97Chapter C. --- Details of local systems --- p.9