Honolulu, HI, USA USA
June 24, 2012 to June 29, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CLOUD.2012.80
Data process in Cloud or IoT (Internet of Things) sometimes implies continuous real-time queries as data streams. In order to acquire extreme value of data stream over time-based sliding window, traditional approaches computed the exact solution through vast space especially under ultra circumstances like high-rate or high-concurrency. In this paper, we design space-bounded synopsis data structure and extreme aggregation algorithm to get approximate solution by finite extreme candidates over time sliding window, whose validity can be theoretically guaranteed. Comprehensive experiments over synthetic and real data set are designed to analyze the tradeoff between accuracy and overhead, which also illustrate the efficiency.
Reservoirs, Accuracy, Complexity theory, Algorithm design and analysis, Conferences, Cloud computing, Educational institutions, sampling, extreme aggregation, synopsis data structure
Weilong Ding, Yanbo Han, Jing Wang, Zhuofeng Zhao, "Space Reduction for Extreme Aggregation of Data Stream over Time-Based Sliding Window", CLOUD, 2012, 2013 IEEE Sixth International Conference on Cloud Computing, 2013 IEEE Sixth International Conference on Cloud Computing 2012, pp. 1002-1003, doi:10.1109/CLOUD.2012.80