Abstract
| Original language | English |
|---|---|
| Title of host publication | 7th IEEE International Conference on E-Science, e-Science 2011 |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE Computer Society |
| Pages | 202-209 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-0-7695-4597-4 |
| ISBN (Print) | 978-1-4577-2163-2 |
| DOIs | |
| Publication status | Published - Dec 2011 |
| Event | 7th IEEE International Conference on e-Science 2011 - Stockholm, Sweden Duration: 5 Dec 2011 → 8 Dec 2011 Conference number: 7 |
Conference
| Conference | 7th IEEE International Conference on e-Science 2011 |
|---|---|
| Abbreviated title | e-Science |
| Country | Sweden |
| City | Stockholm |
| Period | 5/12/11 → 8/12/11 |
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Keywords
- METIS-285072
- IR-79577
- Inference
- EWI-21400
- Stream Data
- Storage
- Fine grained data provenance
Cite this
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Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs. / Huq, Mohammad Rezwanul; Wombacher, Andreas ; Apers, Peter M.G.
7th IEEE International Conference on E-Science, e-Science 2011. Piscataway, NJ : IEEE Computer Society, 2011. p. 202-209.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Adaptive Inference of Fine-grained Data Provenance to Achieve High Accuracy at Lower Storage Costs
AU - Huq, Mohammad Rezwanul
AU - Wombacher, Andreas
AU - Apers, Peter M.G.
N1 - eemcs-eprint-21400
PY - 2011/12
Y1 - 2011/12
N2 - In stream data processing, data arrives continuously and is processed by decision making, process control and e-science applications. To control and monitor these applications, reproducibility of result is a vital requirement. However, it requires massive amount of storage space to store fine-grained provenance data especially for those transformations with overlapping sliding windows. In this paper, we propose techniques which can significantly reduce storage costs and can achieve high accuracy. Our evaluation shows that adaptive inference technique can achieve almost 100% accurate provenance information for a given dataset at lower storage costs than the other techniques. Moreover, we present a guideline about the usage of different provenance collection techniques described in this paper based on the transformation operation and stream characteristics.
AB - In stream data processing, data arrives continuously and is processed by decision making, process control and e-science applications. To control and monitor these applications, reproducibility of result is a vital requirement. However, it requires massive amount of storage space to store fine-grained provenance data especially for those transformations with overlapping sliding windows. In this paper, we propose techniques which can significantly reduce storage costs and can achieve high accuracy. Our evaluation shows that adaptive inference technique can achieve almost 100% accurate provenance information for a given dataset at lower storage costs than the other techniques. Moreover, we present a guideline about the usage of different provenance collection techniques described in this paper based on the transformation operation and stream characteristics.
KW - METIS-285072
KW - IR-79577
KW - Inference
KW - EWI-21400
KW - Stream Data
KW - Storage
KW - Fine grained data provenance
U2 - 10.1109/eScience.2011.36
DO - 10.1109/eScience.2011.36
M3 - Conference contribution
SN - 978-1-4577-2163-2
SP - 202
EP - 209
BT - 7th IEEE International Conference on E-Science, e-Science 2011
PB - IEEE Computer Society
CY - Piscataway, NJ
ER -