Schema Mapping Tools Research Articles

Schema Mapping and Data Exchange Tools: Time for the Golden Age

Abstract In the last 10 years, schema mapping management has become an important research area in data transformation, exchange, and integration systems. The reasons for its success can be found in the declarative nature of its building block (thus enabling clean semantics and easy to use design tools) paired with the efficiency and modularity in the deployment step. In this paper we sketch a line of evolution in schema-mappings and data exchange systems, through what we identify as three main ages. We start presenting the foundations of schema mapping tools and the first tools aimed at translating data from a source to a target schema in the first, heroic age. We then discuss the silver age, when schema mapping tools have grown their way into complex systems and have been translated into both commercial and open-source tools. Finally, we show how recent results in schema-mapping and data-exchange research may be considered the starting point for a forthcoming golden age, with novel research opportunities and a new generation of systems capable of dealing with a significantly larger class of real-life applications.

Bridging the data integration gap

The integration of multiple autonomous and heterogeneous data sources (both across the web and via a company intranet) has received much attention throughout the years, particularly due to its many applications in the fields of Artificial Intelligence and medical research data sharing. Data integration systems embody this work and have come very far in the past twenty years. The problem of designing such systems is characterized by a number of issues that are interesting from a theoretical point of view: answering queries using logical views, query containment and completeness, automatic integration of existing data sources via schema mapping tools, etc. In this work we discuss these issues, compare and contrast various proposed solutions (federated database systems and data warehouses), and finally propose a novel extension of the MVC (model, view, controller) web-based framework that allows for the rapid development and implementation of data integration systems solutions suitable for use on the web.

Bridging the data integration gap

The integration of multiple autonomous and heterogeneous data sources (both across the web and via a company intranet) has received much attention throughout the years, particularly due to its many applications in the fields of Artificial Intelligence and medical research data sharing. Data integration systems embody this work and have come very far in the past twenty years. The problem of designing such systems is characterized by a number of issues that are interesting from a theoretical point of view: answering queries using logical views, query containment and completeness, automatic integration of existing data sources via schema mapping tools, etc. In this work we discuss these issues, compare and contrast various proposed solutions (federated database systems and data warehouses), and finally propose a novel extension of the MVC (model, view, controller) web-based framework that allows for the rapid development and implementation of data integration systems solutions suitable for use on the web.