Wu, Y.-H.Y.-H.WuSHENG-DE WANGChen, L.-J.L.-J.ChenYu, C.-J.C.-J.Yu2020-06-042020-06-042018https://scholars.lib.ntu.edu.tw/handle/123456789/497321https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047812615&doi=10.1109%2fBigData.2017.8258312&partnerID=40&md5=a4fd5303bbda1ce71d1af1ddcd0662fcHaving the capability to process live streaming data is now the fundamental requisite for the successful realization of Industrial Internet of Things (IoT) and poses huge benefits in terms of increased operational efficiency, lesser costs and diminished risk to the industrial world. The advent of IoT and big data analytics technology offers further opportunities in manufacturing business models and asset management. For industrial manufacturing processes that are typically fast-paced and ridden with sophisticated set of conditions, such on-the-fly, real-time, fine-tuning adjustment suggestions of a predictive nature are challenging to describe. However, when provided properly, streaming analytics is greatly useful in the pursuit of improved industrial performance. We developed a streaming analytics system that used to evaluate stable manufacturing efficiency of multiple production lines simultaneously. This paper illustrates an use case from semiconductor manufacturing industry in Taiwan to present the data-driven applicability of streaming analytics system that enables companies to collect a large number of real-time, heterogeneous plant data with steps of text extraction, causal correlation, statistical modeling, as well as real-time monitoring and anomaly detection, to improve overall equipment effectiveness (OEE) of industrial manufacturing. © 2017 IEEE.big data; overall equipment effectiveness; streaming analyticsAnomaly detection; Big data; Data Analytics; Efficiency; Semiconductor device manufacture; Industrial manufacturing process; Internet of Things (IOT); Manufacturing efficiency; Manufacturing performance; Multiple production lines; Operational efficiencies; Overall equipment effectiveness; Semiconductor manufacturing industry; Internet of thingsconference paper10.1109/BigData.2017.82583122-s2.0-85047812615