Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Database repairing using updates
ACM Transactions on Database Systems (TODS)
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
Event queries on correlated probabilistic streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Propositional Clausal Defeasible Logic
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
On generating near-optimal tableaux for conditional functional dependencies
Proceedings of the VLDB Endowment
Efficient Data Interpretation and Compression over RFID Streams
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic Event Extraction from RFID Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
A Sampling-Based Approach to Information Recovery
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Probabilistic Inference over RFID Streams in Mobile Environments
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Fast track article: A temporal RFID data model for querying physical objects
Pervasive and Mobile Computing
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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Recently, passive Radio Frequency Identification (RFID) systems have received an increased amount of attention as researchers have worked to implement a stable and reliable system. Unfortunately, despite vast improvements in the quality of RFID technology, a significant amount of erroneous data is still captured in the system. Currently, the problems associated with RFID have been addressed by cleaning algorithms to enhance the data quality. In this paper, we present X-CleLo, a means to intelligently clean and transform the dirty data into high-level events using Clausal Defeasible Logic. The extensive experimental study we have conducted has shown that the X-CleLo method has several advantages over currently utilised cleaning techniques and achieves a higher cleaning and event discovery rate.