In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious N...
详细信息
In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious Network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy. (C) 2018 The Authors. Published by Elsevier Ltd.
RFID technology has gained popularity to address localization problem in the manufacturing shopfloor by tracking the manufacturing object location to increase the production's efficiency. However, the signals (dat...
详细信息
ISBN:
(纸本)9781538666500
RFID technology has gained popularity to address localization problem in the manufacturing shopfloor by tracking the manufacturing object location to increase the production's efficiency. However, the signals (data) used for localization task is not easy to analyze because it is generated from the non-stationary environment. It also continuously arrive over time and yields the large-volume of data. Therefore, an advanced big data analytic is required to overcome this problem. We propose a distributed big data analytic framework based on PANFIS (Scalable PANFIS), where PANFIS is an evolvingalgorithm which has capability to learn data stream in the single pass mode. Scalable PANFIS can learn big data stream by processing many chunks/partitions of data stream. Scalable PANFIS is also equipped with rule' structure merging to eliminate the redundancy among rules. Scalable PANFIS is validated by measuring its performance against single PANFIS and other Spark's scalable machine learning algorithms. The result shows that Scalable PANFIS performs running time more than 20 times faster than single PANFIS. The rule merging process in Scalable PANFIS shows that there is no significant reduction of accuracy in classification task with 96.67 percent of accuracy in comparison with single PANFIS of 98.71 percent. Scalable PANFIS also generally outperforms some Spark MLib machine learnings to classify RFID data with the comparable speed in running time.
In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious N...
详细信息
In this paper, a big data analytic framework is introduced for processing high-frequency data stream. This framework architecture is developed by combining an advanced evolving learning algorithm namely Parsimonious Network Fuzzy Inference System (PANFIS) with MapReduce parallel computation, where PANFIS has the capability of processing data stream in large volume. Big datasets are learnt chunk by chunk by processors in MapReduce environment and the results are fused by rule merging method, that reduces the complexity of the rules. The performance measurement has been conducted, and the results are showing that the MapReduce framework along with PANFIS evolving system helps to reduce the processing time around 22 percent in average in comparison with the PANFIS algorithm without reducing performance in accuracy.
暂无评论