Abstract
More and more emerging applications are involved in monitoring multiple data streams concurrently. In these applications, the data flow out of multiple concurrent sources continuously. In such large-scale real-time monitoring applications, continuously identifying representatives out of massive streams is an important task which aims to capture key trends to support online monitoring and analysis. In this paper, we present a framework for continuously extracting representatives out of massive streams. Our framework identifies and traces representatives based on core clustering technique. We adapt the core clustering model under streaming condition and propose a method of extracting representatives by utilizing the advantage characteristic of core clusters that core set is tight. In order to continuously identify the representatives in an efficient way, we apply online representatives adjust processes only when significant clustering evolution happens. As shown in our experimental studies, our algorithm is effective and efficient.
This work is supported by the National Natural Science Foundation of China under Grant No.61103025.
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Li, Q., Ma, X., Tang, S., Xie, S. (2011). Continuously Identifying Representatives Out of Massive Streams. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7120. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25853-4_18
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DOI: https://doi.org/10.1007/978-3-642-25853-4_18
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