Efficient data retrieval for large-scale smart city applications through applied bayesian interference
Publication Type
Conference Paper
Publication Date
4-2015
Abstract
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.
Discipline
Databases and Information Systems
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE ISSNIP 2015, 2015 April 7-9
Identifier
10.1109/ISSNIP.2015.7106930
Publisher
Elsevier
City or Country
Singapore
Citation
KOH, Jin Ming; SAK, Marcus; TAN, Hwee Xian; LIANG, Huiguang; FOLIANTO, Fachmin; and QUEK, Tony.
Efficient data retrieval for large-scale smart city applications through applied bayesian interference. (2015). IEEE ISSNIP 2015, 2015 April 7-9.
Available at: https://ink.library.smu.edu.sg/sis_research/4245
Additional URL
https://doi.org/10.1109/ISSNIP.2015.7106930