Statistical analysis with missing data
Statistical analysis with missing data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Bases for Association Rules Using Closed Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
CFI-Stream: mining closed frequent itemsets in data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Model-driven data acquisition in sensor networks
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Estimating missing data in data streams
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
The design space of wireless sensor networks
IEEE Wireless Communications
| Hi-index | 0.00 |
Data missing is a common problem in database query processing, which can cause bias or lead to inefficient analyses, and this problem happens more often in sensor databases. The reasons include power outage at the sensor node, sensors time synchronization, occurrences of local interferences, unstable wireless network communication, etc. Therefore, in sensor database applications, there is a need for data imputation, especially for those applications in which the query response time is tight, and the accuracy of the query results is important. In this paper, we present a data imputation application based on association rule mining of closed frequent itemsets. They are subsets of all frequent patterns but provide complete and condensed information since they do not include redundant patterns. Experimental results compared with the existing techniques using real-life sensor data show that our proposed technique effectively imputes missing sensor data as well as achieves time and space efficiency.