IMPrECISE: Good-is-good-enough data integration

Authors:
Ander de Keijzer;Maurice van Keulen
Affiliations:
University of Twente, Postbus 217, 7500AE Enschede, The Netherlands. a.dekeijzer@utwente.nl;University of Twente, Postbus 217, 7500AE Enschede, The Netherlands. m.vankeulen@utwente.nl
Venue:
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Year:
2008

Citing 0
Cited 5

Qualitative effects of knowledge rules and user feedback in probabilistic data integration

The VLDB Journal — The International Journal on Very Large Data Bases
A Survey on Uncertainty Management in Data Integration

Journal of Data and Information Quality (JDIQ)
Towards a version control model with uncertain data

Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management
Demonstrating ProApproX 2.0: a predictive query engine for probabilistic XML

Proceedings of the 21st ACM international conference on Information and knowledge management
Indeterministic Handling of Uncertain Decisions in Deduplication

Journal of Data and Information Quality (JDIQ) - Special Issue on Entity Resolution

Quantified Score

Hi-index	0.00

Visualization

Abstract

IMPrECISE is an XQuery module that adds probabilistic XML functionality to an existing XML DBMS, in our case MonetDB/XQuery. We demonstrate probabilistic XML and data integration functionality of IMPrECISE. The prototype is configurable with domain knowledge such that the amount of uncertainty arising during data integration is reduced to an acceptable level, thus obtaining a "good is good enough" data integration with minimal human effort.