2018年第106期“海大青年学术沙龙”由综合交通运输协同创新中心举办，沙龙主题为“Data Science for Business”和“Supply Chain 5.0: An Exploratory Look at Supply Chains from Small World to Big Network”。本次沙龙由综合交通运输协同创新中心邀请到日本关西大学Katsutoshi YADA教授和大连海事大学左毅副教授主讲。
第1位主讲人简介：Katsutoshi YADA is a professor in the Faculty of Commerce at Kansai University. His research interests include data mining for service, and information strategy concerning data mining. His work has been published in various international journals (including Soft Computing, Decision Support Systems, and Data Mining and Knowledge Discovery - the premier international journal in the field of data mining) and numerous conference proceedings. In addition to holding the post of director of the Data Science Laboratory, he also serves as the chair of Kansai University’s DSI (Data Mining and Service Science for Innovation) Program. He has previously been a visiting professor at Osaka University, visiting scholar at Columbia University, and chairman or committee member at several international conferences and workshops, including ICDM (IEEE International Conference on Data Mining) and technical committee chair of IEEE SMC (Systems, Man, and, Cybernetics Society).
第2位主讲人简介：Yi Zuo is an associate professor of Navigation College in Dalian Maritime University. He received his B.S. degree in Department of Computer Science in Northeast University at 2004, and received his M.S. degree and Ph.D. degree in School of Information Science of Nagoya University at 2009 and 2012, respectively. He majors in Machine Learning and Data Science also including Evolutionary Algorithm. His primary researches are the application of Bayesian network for prediction and classification to resolve practical issues in real world. He is IEEE Technical Committee Co-Chair of the SMC and has been Program Committee member for several international conferences such as MSNDS, MISNC and APWC on CSE. He also served as invited reviewer for several international journals such as Computers & Operations Research, IEEE Trans. on SMC: Systems and Technological Forecasting & Social Change.
沙龙摘要：Data science carries out interdisciplinary research in basic technology sectors and a diverse array of business sectors. Recently, the development of various information devices such as sensors and RFID has produced vast quantities of unstructured time-series data. This data, which is known as big data, is being accumulated in all sorts of settings, and has been receiving a great deal of attention. In order for businesses to harness this big data to create new value, they must answer questions such as: How was the data created? How do human beings behave? What sort of characteristics can be deduced from the unstructured data? In other words, what is required is the implementation of interdisciplinary research and data science that combines the sciences and humanities, in which research results from areas like computer science and social science are organically integrated. At the Data Science Laboratory, our goal is to apply various data science techniques to a variety of business sectors, and build a research structure for achieving the data science processes of developing basic techniques and applications, developing models for consumer behavior, and verifying these by putting them into practice. To accomplish this, it forms teams that oversee business application areas and modeling, and the construction of information infrastructure. Data Science Laboratory employs cutting-edge techniques for data science on multidimensional and time-series data that business sectors have failed to adequately grapple with to date. By doing so, it will continue to create theoretical models that understand complex phenomena and perform demonstration experiments through industry-academic partnerships.