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A massive amount of data is available in distributed fashion on various networks, including Internet, peer-to-peer networks, and wireless sensor networks. Users are often interested in monitoring interesting patterns or abnormal events hidden in these data. Transferring all the raw data from each host node to a central coordinator for processing is costly and unnecessary. In this study, we investigate the problem of monitoring changes on the data distribution in the networks (MCDN). To address this problem, we propose a technique, called wavenet, by compressing the local item set in each host node into a compact yet accurate summary, called local wavelet, for communication with the coordinator. We also propose adaptive monitoring to address the issues of local wavelet propagation in wavenet. An extensive performance evaluation has been conducted to validate our proposal and demonstrates the efficiency of wavenet.