TY  - JOUR
AB  - Purpose The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand.Design/methodology/approach First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework.Findings Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable.Originality/value This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.
VL  - 10
IS  - 2
SN  - 1756-378X
DO  - 10.1108/IJICC-09-2016-0033
UR  - https://doi.org/10.1108/IJICC-09-2016-0033
AU  - Wu Kehe
AU  - Zhu Yayun
AU  - Li Quan
AU  - Wu Ziwei
PY  - 2017
Y1  - 2017/01/01
TI  - A distributed real-time data prediction framework for large-scale time-series data using stream processing
T2  - International Journal of Intelligent Computing and Cybernetics
PB  - Emerald Publishing Limited
SP  - 145
EP  - 165
Y2  - 2024/04/24
ER  -