Paper Info
Title
Space Reduction for Extreme Aggregation of Data Stream over Time-Based Sliding Window
Abstract
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.
Year
DOI
Venue
2012
10.1109/CLOUD.2012.80
IEEE CLOUD
Keywords
Field
DocType
extreme aggregation algorithm,comprehensive experiment,data stream,time-based sliding window,extreme value,finite extreme candidate,data process,synopsis data structure,extreme aggregation,approximate solution,exact solution,space reduction,medical,cloud computing,reservoirs,internet of things,algorithm design and analysis,cloud,accuracy,data reduction,iot,finance,sampling,sensor network
Data structure,Data stream mining,Data processing,Sliding window protocol,Algorithm design,Extreme value theory,Data stream,Computer science,Real-time computing,Data reduction
Conference
Citations 
PageRank 
References 
0
0.34
1
Authors
4
Name
Order
Citations
PageRank
Weilong Ding12810.14
Yanbo Han250059.74
Jing Wang31038105.16
Zhuofeng Zhao46615.46