A Data Imputation Model in Sensor Databases

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, the authors present a data imputation application based on association rule mining of closed frequent item sets. They are subsets of all frequent patterns but provide complete and condensed information since they do not include redundant patterns.