TY - GEN
T1 - Efficient data retrieval for large-scale smart city applications through applied Bayesian inference
AU - Koh, Jin Ming
AU - Sak, Marcus
AU - Tan, Hwee Xian
AU - Liang, Huiguang
AU - Folianto, Fachmin
AU - Quek, Tony
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2015/5/13
Y1 - 2015/5/13
N2 - Recent years have witnessed the proliferation of worldwide efforts towards developing technologies for enabling smart cities, to improve the quality of life for citizens. These smart city solutions are typically deployed across large spatial regions over long time scales, generating massive volumes of data. An efficient way of data retrieval is thus required, for post-processing of the data-such as for analytical and visualization purposes. In this paper, we propose a data prefetching and caching algorithm based on Bayesian inference, for the retrieval of data in large-scale smart city applications. A brute-force approach is used to determine the optimal weight correction factor in the proposed prefetching algorithm. We evaluate the optimized Bayesian prefetching algorithm against the Naïve and Random prefetch baselines, using both simulated and actual data usage patterns. Results show that the Bayesian approach can achieve up to 48.4% reductions in actual user-perceived application delays during data retrieval.
AB - Recent years have witnessed the proliferation of worldwide efforts towards developing technologies for enabling smart cities, to improve the quality of life for citizens. These smart city solutions are typically deployed across large spatial regions over long time scales, generating massive volumes of data. An efficient way of data retrieval is thus required, for post-processing of the data-such as for analytical and visualization purposes. In this paper, we propose a data prefetching and caching algorithm based on Bayesian inference, for the retrieval of data in large-scale smart city applications. A brute-force approach is used to determine the optimal weight correction factor in the proposed prefetching algorithm. We evaluate the optimized Bayesian prefetching algorithm against the Naïve and Random prefetch baselines, using both simulated and actual data usage patterns. Results show that the Bayesian approach can achieve up to 48.4% reductions in actual user-perceived application delays during data retrieval.
UR - http://www.scopus.com/inward/record.url?scp=84933564052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84933564052&partnerID=8YFLogxK
U2 - 10.1109/ISSNIP.2015.7106930
DO - 10.1109/ISSNIP.2015.7106930
M3 - Conference contribution
AN - SCOPUS:84933564052
T3 - 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015
BT - 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015
Y2 - 7 April 2015 through 9 April 2015
ER -