Abstract
In recent years, data stream processing algorithms have been actively proposed. In terms of computing performance, they mainly focus on the restriction of their memory usage and minimization of their processing time per data element. However, if the number of data elements in a time slot is greater than the number of those that can be processed for the time slot, some of them cannot be processed in real time even though the processing time per data element is minimized. In this paper, a selection method of frequent tuples over a data stream for efficient load shedding is proposed. Furthermore, considering the change of the data stream, a threshold for the tuples to be selected is adaptively controlled by a prediction mechanism for the frequency of a tuple. Through this mechanism, the number of selected tuples is maximized within the capacity of the main-processing operation.
| Original language | English |
|---|---|
| Pages (from-to) | 277-287 |
| Number of pages | 11 |
| Journal | Computer Systems Science and Engineering |
| Volume | 23 |
| Issue number | 4 |
| Publication status | Published - 2008 Jul 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
Adaptive selection of tuples over data streams for efficient load shedding. / Chang, Joong Hyuk; Park, Nam Hun; Lee, Won Suk.
In: Computer Systems Science and Engineering, Vol. 23, No. 4, 01.07.2008, p. 277-287.Research output: Contribution to journal › Article
TY - JOUR
T1 - Adaptive selection of tuples over data streams for efficient load shedding
AU - Chang, Joong Hyuk
AU - Park, Nam Hun
AU - Lee, Won Suk
PY - 2008/7/1
Y1 - 2008/7/1
N2 - In recent years, data stream processing algorithms have been actively proposed. In terms of computing performance, they mainly focus on the restriction of their memory usage and minimization of their processing time per data element. However, if the number of data elements in a time slot is greater than the number of those that can be processed for the time slot, some of them cannot be processed in real time even though the processing time per data element is minimized. In this paper, a selection method of frequent tuples over a data stream for efficient load shedding is proposed. Furthermore, considering the change of the data stream, a threshold for the tuples to be selected is adaptively controlled by a prediction mechanism for the frequency of a tuple. Through this mechanism, the number of selected tuples is maximized within the capacity of the main-processing operation.
AB - In recent years, data stream processing algorithms have been actively proposed. In terms of computing performance, they mainly focus on the restriction of their memory usage and minimization of their processing time per data element. However, if the number of data elements in a time slot is greater than the number of those that can be processed for the time slot, some of them cannot be processed in real time even though the processing time per data element is minimized. In this paper, a selection method of frequent tuples over a data stream for efficient load shedding is proposed. Furthermore, considering the change of the data stream, a threshold for the tuples to be selected is adaptively controlled by a prediction mechanism for the frequency of a tuple. Through this mechanism, the number of selected tuples is maximized within the capacity of the main-processing operation.
UR - http://www.scopus.com/inward/record.url?scp=49549108482&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49549108482&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:49549108482
VL - 23
SP - 277
EP - 287
JO - Computer Systems Science and Engineering
JF - Computer Systems Science and Engineering
SN - 0267-6192
IS - 4
ER -