Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
TinyDB: an acquisitional query processing system for sensor networks
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Infrequent Item Mining in Multiple Data Streams
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Using Data Mining to Estimate Missing Sensor Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Real-time data mining of non-stationary data streams from sensor networks
Information Fusion
Detecting movement patterns with wireless sensor networks: application to bird behavior
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
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In this paper, we present a data mining framework to estimate missing or corrupted data in sensor network applications - a frequently occurring phenomenon in this domain. The framework is naturally germane to the spatio-temporal analysis of relational data stream evolution. Our method utilizes association rules to capture spatio-temporal correlations in multivariate, dynamically evolving, and unbounded sensor data streams. Existing approaches that tackled this problem do not account for the multi-dimensionality of the node data and their relationship; furthermore they entail simplistic and/or premature assumptions on the temporal and spatial factors to overcome the complexity of the streaming environment. Our technique, called Mining Autonomously Spatio-Temporal Environmental Rules (MASTER), comprehensively formulates the problem of mining patterns in sensor data streams, and yet remains provably adaptive to bounded time and space costs while probabilistically assuring a bounded estimation error. Simulation experiments show MASTER's efficiency in terms of overhead as well as the quality of estimation.