The mediated data integration (MeDInt) : An approach to the integration of database and legacy systems

The information required for decision making by executives in organizations is normally scattered across disparate data sources including databases and legacy systems. To gain a competitive advantage, it is extremely important for executives to be able to obtain one unique view of information in an accurate and timely manner. To do this, it is necessary to interoperate multiple data sources, which differ structurally and semantically. Particular problems occur when applying traditional integration approaches, for example, the global schema needs to be recreated when the component schema has been modified. This research investigates the following heterogeneities between heterogeneous data sources: Data Model Heterogeneities, Schematic Heterogeneities and Semantic Heterogeneities. The problems of existing integration approaches are reviewed and solved by introducing and designing a new integration approach to logically interoperate heterogeneous data sources and to resolve three previously classified heterogeneities. The research attempts to reduce the complexity of the integration process by maximising the degree of automation. Mediation and wrapping techniques are employed in this research. The Mediated Data Integration (MeDint) architecture has been introduced to integrate heterogeneous data sources. Three major elements, the MeDint Mediator, wrappers, and the Mediated Data Model (MDM) play important roles in the integration of heterogeneous data sources. The MeDint Mediator acts as an intermediate layer transforming queries to sub-queries, resolving conflicts, and consolidating conflict-resolved results. Wrappers serve as translators between the MeDint Mediator and data sources. Both the mediator and wrappers arc well-supported by MDM, a semantically-rich data model which can describe or represent heterogeneous data schematically and semantically. Some organisational information systems have been tested and evaluated using the MeDint architecture. The results have addressed all the research questions regarding the interoperability of heterogeneous data sources. In addition, the results also confirm that the Me Dint architecture is able to provide integration that is transparent to users and that the schema evolution does not affect the integration

Schema architecture and their relationships to transaction processing in distributed database systems

We discuss the different types of schema architectures which could be supported by distributed database systems, making a clear distinction between logical, physical, and federated distribution. We elaborate on the additional mapping information required in architecture based on logical distribution in order to support retrieval as well as update operations. We illustrate the problems in schema integration and data integration in multidatabase systems and discuss their impact on query processing. Finally, we discuss different issues relevant to the cooperation (or noncooperation) of local database systems in a heterogeneous multidatabase system and their relationship to the schema architecture and transaction processing

Negotiation in Database Schema Integration

Databases are playing an increasingly important role in organizations. Timely, accurate access to information has become a critical component of gaining competitive advantage. Data availability is commonly perceived as a critical success factor for an organizationÕs long-term survival, and day-to-day operations can be crippled by failure of the database system to satisfy user requirements. However, a number of emerging issues complicate organizationsÕ ability to provide comprehensive and reliable access to disparate information resources. Further, data accessibility is often compromised due to the typically high cost associated with addressing these issues in practice. Examples of such issues which have emerged in the past decade include the proliferation and investment in autonomous databases within organizations, heterogeneity among data models and database management systems employed, the increasingly important role of distributed systems, and the increasing complexity and knowledge-intensive nature of integrating database schemas. All these factors contribute to the increasing importance of developing feasible options for providing interoperability among existing databases, and therefore, of pursuing research in the area of database schema integration. Indeed, this research focuses specifically on knowledge requirement problems involved in integrating the schema of existing databases in order to provide interoperability and transparent access to disparate information resources without the investment involved in complete systems redesig

Data access and integration in the ISPIDER proteomics grid

Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources

An algorithm for Finding a Relationship Between Entities : Semi-Automated Schema Integration Approach

학위논문 (석사)-- 서울대학교 대학원 경영대학 경영학과, 2017. 8. 박진수.Database schema integration is a very important issue in information systems. Since schema integration is a time-consuming and labor-intensive task, many studies have attempted to automate this task. In the meantime, the researchers used xml as the source schema and still left much of the work to be done through DBA intervention. For example, there are various naming conflicts related to relationship names in schema integration. In the past, the DBA had to intervene to resolve the naming conflict name. In this paper, we introduce an algorithm that automatically generates relationship names to resolve relationship names conflicts that occur during schema integration. This algorithm is based on Internet collocation dictionary and english sentence example dictionary. The relationship between the two entities is generated by analyzing examples extracted based on dictionary data through natural language processing. By building a semi-automated schema integration system and testing this algorithm, we found that it showed about 90% accuracy. Using this algorithm, we can resolve the problems related to naming conflicts that occur at schema integration automatically without DBA intervention.1. Introduction 1 2. Methodologies for semi-automated schema integration 3 3. An algorithm for finding a relationship between entities 14 4. Semi-Automated Schema Integration 20 5. Evaluation 23 6. Limitations 24 7. Conclusion 25 Reference 26Maste