Communications of the ACM
Communications of the ACM - Supporting community and building social capital
AIMQ: a methodology for information quality assessment
Information and Management
Approximate Graph Schema Extraction for Semi-Structured Data
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Using Schema Matching to Simplify Heterogeneous Data Translation
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Estimating the Quality of Databases
FQAS '98 Proceedings of the Third International Conference on Flexible Query Answering Systems
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Making quality count in biological data sources
Proceedings of the 2nd international workshop on Information quality in information systems
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
| Hi-index | 0.00 |
Nowadays everybody uses a variety of different systems managing similar information, for example in the home entertainment sector. Unfortunately, these systems are largely heterogeneous, mostly with respect to the data model but at least with respect to the schema, making synchronization and propagation of data a daunting task. Our goal is to cope with this situation in a best-effort manner. To meet this claim, we introduce a symmetric instance-level matching approach that allows to establish mappings without any user interaction, schema information or dictionaries and ontologies. In awareness of dealing with inexact and incomplete mappings, the quality of the propagation has to be quantified. For this purpose, different quality dimensions like accuracy or completeness are introduced. Additionally, visualizing the quality allows users to evaluate the performance of the data propagation process.