Deriving a transparent dataspace-oriented entity associative algorithm

Thesis Submitted in total fulfillment of the requirements for the degree of Doctor of Philosophy in Information TechnologyOrganizations possess data residing in varied data sources though there is no effective way of integrating these repositories to provide information to end users transparently. This is primarily caused by the fact that the existing data is stored in databases that consist of varied models and techniques of both storage and access to data. The main aim of this research was to formulate a set of algorithms to support the development of a dataspace support platform that integrates data residing in divergent data stores. These techniques facilitate the asscociation of data entities in a dataspace by enabling entity coexistence for integrating data residing in divergent data stores. The research objectives were to analyze the state of dataspace implementation, to develop a model that outlines the criteria for successful dataspace design, to develop a dataspace support platform that integrates data residing in divergent data stores and to conduct experiments to validate the scalability of the implemented dataspace support platform. In order to achieve these objectives, the soft systems theory is applied. A literature survey approach is adopted and supplemented from the findings by use of brainstorming and further experiments. The findings have been used to identify facts pertaining to the principles, design and implementation of a dataspace support platform. The final outcome consists of a set of algorithms, models and a test dataspace support platform. Access to information is facilitated through a more scalable, flexible and transparent platform regardless of the underlying data models. This results to a O(log n + k ) query response time coupled with a O(n) build time on the entire dataspace. In conclusion, the triggers for enterprise systems integration are apparent, and compliance is only one of numerous drivers pushing organizations towards achieving a more integrated outlook of enterprise data. With the dataspace-oriented entity associative algorithm, users can have the ability to harness or filter informational requirements so as to enhance decision making in terms of time, accuracy and availability of information.Strathmore Universit