%0 Generic %1 LeeBagheriKao14INDIN %A Lee, Jay %A Bagheri, Behrad %A Kao, Hung-An %D 2014 %K v1500 paper embedded ai factory process optimize data analysis zzz.cps zzz.sfit %R 10.13140/2.1.1464.1920 %T Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics %X In today's competitive business environment, companies are facing challenges in dealing with big data issues for rapid decision making for improved productivity. Many manufacturing systems are not ready to manage big data due to the lack of smart analytics tools. Germany is leading a transformation toward 4th Generation Industrial Revolution (Industry 4.0) based on Cyber-Physical System based manufacturing and service innovation. As more software and embedded intelligence are integrated in industrial products and systems, predictive technologies can further intertwine intelligent algorithms with electronics and tether-free intelligence to predict product performance degradation and autonomously manage and optimize product service needs. This article addresses the trends of industrial transformation in big data environment as well as the readiness of smart predictive informatics tools to manage big data to achieve transparency and productivity.

@misc{LeeBagheriKao14INDIN, abstract = {In today's competitive business environment, companies are facing challenges in dealing with big data issues for rapid decision making for improved productivity. Many manufacturing systems are not ready to manage big data due to the lack of smart analytics tools. Germany is leading a transformation toward 4th Generation Industrial Revolution (Industry 4.0) based on Cyber-Physical System based manufacturing and service innovation. As more software and embedded intelligence are integrated in industrial products and systems, predictive technologies can further intertwine intelligent algorithms with electronics and tether-free intelligence to predict product performance degradation and autonomously manage and optimize product service needs. This article addresses the trends of industrial transformation in big data environment as well as the readiness of smart predictive informatics tools to manage big data to achieve transparency and productivity.}, added-at = {2014-11-20T17:11:21.000+0100}, author = {Lee, Jay and Bagheri, Behrad and Kao, Hung-An}, biburl = {https://www.bibsonomy.org/bibtex/227b1366d7d6b21a82a8c2079889822a5/flint63}, doi = {10.13140/2.1.1464.1920}, file = {Research Gate:2014/LeeBagheriKao14INDIN.pdf:PDF}, groups = {public}, howpublished = {ResearchGate}, interhash = {e1175792840163aa492a460cd69acd3b}, intrahash = {27b1366d7d6b21a82a8c2079889822a5}, keywords = {v1500 paper embedded ai factory process optimize data analysis zzz.cps zzz.sfit}, note = {Keynote given at the 12th IEEE International Conference on Industrial Informatics (INDIN 2014), Porto Alegre, Brazil}, timestamp = {2018-04-16T12:34:51.000+0200}, title = {Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics}, username = {flint63}, year = 2014 }