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http://dx.doi.org/10.3745/KIPSTD.2006.13D.6.755

Load Shedding via Predicting the Frequency of Tuple for Efficient Analsis over Data Streams  

Chang, Joong-Hyuk (연세대학교 소프트웨어응용연구소)
Publication Information
The KIPS Transactions:PartD / v.13D, no.6, 2006 , pp. 755-764 More about this Journal
Abstract
In recent, data streams are generated in various application fields such as a ubiquitous computing and a sensor network, and various algorithms are actively proposed for processing data streams efficiently. They mainly focus on the restriction of their memory usage and minimization of their processing time per data element. However, in the algorithms, if data elements of a data stream are generated in a rapid rate for a time unit, some of the data elements cannot be processed in real time. Therefore, an efficient load shedding technique is required to process data streams effcientlv. For this purpose, a load shedding technique over a data stream is proposed in this paper, which is based on the predicting technique of the frequency of data element considering its current frequency. In the proposed technique, considering the change of the data stream, its threshold for tuple alive is controlled adaptively. It can help to prevent unnecessary load shedding.
Keywords
Data Stream; Load Shedding; Significant Tuple; Prediction of Frequency;
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