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.
Randomness is an important research topic in the field of information security, especially in cryptography. Randomness test techniques are used to examine the quality of random numbers so that they meet the requirements of the application. The randomness of cryptographic algorithms is one of the key concerns in the algorithm design. People have put forward different standards and test requirements for the randomness of cryptographic algorithms, as well as developed corresponding randomness test kits. This paper analyzes the randomness test technologies of cryptographic algorithms and the general randomness test methods, and compares them on this basis. At the end, the actual application scenarios to apply these randomness test methods are discussed.
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