Adaptive cleaning for RFID data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
A deferred cleansing method for RFID data analytics
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Propositional Clausal Defeasible Logic
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Probabilistic Event Extraction from RFID Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Activity recognition: linking low-level sensors to high-level intelligence
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Correcting missing data anomalies with clausal defeasible logic
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
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Within databases employed in various commercial sectors, anomalies continue to persist and hinder the overall integrity of data. Typically, Duplicate, Wrong and Missed observations of spatial-temporal data causes the user to be not able to accurately utilise recorded information. In literature, different methods have been mentioned to clean data which fall into the category of either deterministic and probabilistic approaches. However, we believe that to ensure the maximum integrity, a data cleaning methodology must have properties of both of these categories to effectively eliminate the anomalies. To realise this, we have proposed a method which relies both on integrated deterministic and probabilistic classifiers using fusion techniques. We have empirically evaluated the proposed concept with state-of-the-art techniques and found that our approach improves the integrity of the resulting data set.