A FRAMEWORK FOR CONCEPTUAL INTEGRATION OF HETEROGENEOUS DATABASES

Autonomy of operations combined with decentralised management of data has given rise to a number of heterogeneous databases or information systems within an enterprise. These systems are often incompatible in structure as well as content and hence difficult to integrate. This thesis investigates the problem of heterogeneous database integration, in order to meet the increasing demand for obtaining meaningful information from multiple databases without disturbing local autonomy. In spite of heterogeneity, the unity of overall purpose within a common application domain, nevertheless, provides a degree of semantic similarity which manifests itself in the form of similar data structures and common usage patterns of existing information systems. This work introduces a conceptual integration approach that exploits the similarity in meta level information in existing systems and performs metadata mining on database objects to discover a set of concepts common to heterogeneous databases within the same application domain. The conceptual integration approach proposed here utilises the background knowledge available in database structures and usage patterns and generates a set of concepts that serve as a domain abstraction and provide a conceptual layer above existing legacy systems. This conceptual layer is further utilised by an information re-engineering framework that customises and packages information to reflect the unique needs of different user groups within the application domain. The architecture of the information re-engineering framework is based on an object-oriented model that represents the discovered concepts as customised application objects for each distinct user group

Toxicity of landfill leachate and seasonal performance of wetlands : final report.

Report examines the water quality of a landfill site. A number of issues are addressed in the report including: effluent characteristics, the presence or absence of bacteria for the removal of ammonia, and results from water samples that were taken

An Overview of the Challenges in Designing, Integrating, and Delivering BARD

Recent industry-academic partnerships involve collaboration across disciplines, locations, and organizations using publicly funded “open-access” and proprietary commercial data sources. These require effective integration of chemical and biological information from diverse data sources, presenting key informatics, personnel, and organizational challenges. BARD (BioAssay Research Database) was conceived to address these challenges and to serve as a community-wide resource and intuitive web portal for public-sector chemical biology data. Its initial focus is to enable scientists to more effectively use the NIH Roadmap Molecular Libraries Program (MLP) data generated from 3-year pilot and 6-year production phases of the Molecular Libraries Probe Production Centers Network (MLPCN), currently in its final year. BARD evolves the current data standards through structured assay and result annotations that leverage the BioAssay Ontology (BAO) and other industry-standard ontologies, and a core hierarchy of assay definition terms and data standards defined specifically for small-molecule assay data. We have initially focused on migrating the highest-value MLP data into BARD and bringing it up to this new standard. We review the technical and organizational challenges overcome by the inter-disciplinary BARD team, veterans of public and private sector data-integration projects, collaborating to describe (functional specifications), design (technical specifications), and implement this next-generation software solution