Name
Abstract | ||
|---|---|---|
Data processing 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 worse circumstances. In this paper, we design space-bounded synopsis data structure and random algorithm for extreme aggregation to get non-exact solution by finite extrema candidates over time sliding window, whose validity can be theoretically guaranteed. Comprehensive experiments on synthetic and real data set are designed to analyze the tradeoff between accuracy and overhead, which also illustrate the effectiveness. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/EDOCW.2012.34 | EDOC Workshops |
Keywords | Field | DocType |
comprehensive experiment,extreme value,exact solution,non-exact solution,random algorithm,data structures,iot,data processing,extreme aggregation,real-time queries,space-bounded extreme aggregation,synopsis data structure,finite extrema,space-bounded synopsis data structure,time-based sliding window,continuous real-time query,cloud computing,sampling,data stream,query processing,finite extrema candidate,internet of things | Data structure,Randomized algorithm,Data mining,Data stream mining,Sliding window protocol,Data stream,Computer science,Extreme value theory,Maxima and minima,Bounded function | Conference |
ISSN | ISBN | Citations |
2325-6583 | 978-1-4673-5005-1 | 1 |
PageRank | References | Authors |
0.36 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weilong Ding | 1 | 28 | 10.14 |
| Yanbo Han | 2 | 500 | 59.74 |
| Jing Wang | 3 | 1038 | 105.16 |
| Zhuofeng Zhao | 4 | 66 | 15.46 |