Clio Schema Matching System
A Clio Schema Matching System is a Schema Matching System.
- See: Ontology Matching System.
References
2008
- http://queens.db.toronto.edu/project/clio/
- The world today is full of information sources, all with their own ways of representing data. One common problem that arises is that data, which exists in one representation in some data source, is needed in a different representation for some other purpose. As a simple example, the owner of a data source may want to publish his data using a specific XML DTD, though it is stored in some different (legacy) format. As another example, data warehouses bring data from one or more sources together, in a new form that allows for efficient decision support queries. Today, such situations are for the most part dealt with manually, by an expert user who has knowledge of both the source and target representations. Converting from one data representation to another is a time-consuming and labor intensive project, with few tools available to ease the task.
The goal of Clio, a joint research project at the University of Toronto and IBM's Almaden Research Center, is to produce a tool for creating mappings between two data representations semi-automatically (i.e., with user input). Our tool will work with an associated "meta query engine" (e.g., Garlic), one that can query data in the source representation, and, if any exists, in the target representation as well. Clio will take as input a target schema and a source schema, and generate as output view definitions (queries). These queries, executed by the meta query engine, will take data from the source and transform it to match the target schema, cleansing and transforming it as needed to be compatible with existing data visible through that schema.
- The world today is full of information sources, all with their own ways of representing data. One common problem that arises is that data, which exists in one representation in some data source, is needed in a different representation for some other purpose. As a simple example, the owner of a data source may want to publish his data using a specific XML DTD, though it is stored in some different (legacy) format. As another example, data warehouses bring data from one or more sources together, in a new form that allows for efficient decision support queries. Today, such situations are for the most part dealt with manually, by an expert user who has knowledge of both the source and target representations. Converting from one data representation to another is a time-consuming and labor intensive project, with few tools available to ease the task.
2001
- (Miller et al., 2001) ⇒ Renée J. Miller, Mauricio A. Hernández, Laura M. Haas, Lingling Yan, C. T. Howard Ho, Ronald Fagin, and Lucian Popa. (2001). “The Clio Project: managing heterogeneity.” In: SIGMOD Record 30(1). doi:10.1145/373626.373713
- (Hernández et al., 2001) ⇒ Mauricio A. Hernández, Renée J. Miller, and Laura M. Haas. (2001). “Clio: A semi-automatic tool for schema mapping.” In: SIGMOD Record, 30(2). doi:10.1145/376284.375767