Keynote Speaker I
Professor David L. Olson
University of Nebraska, USA
Speech Title: Knowledge Management in the Big Data Era
Abstract: We live in an era of ubiquitous information. Knowledge management is interested in gathering, storing, and retrieving information to turn it into knowledge. This involves big data and data mining. In healthcare risk management, there are a number of aspects to risk, to include disaster management, public health risk management (as in pandemic management), food safety, and social welfare. In supply chain management, risks are found in sourcing, logistics, production, and retail. Similar processes apply to management of businesses and governmental agencies. Computer technology provides business intelligence enabling quantitative analysis in the form of business analytics. The era of big data has seen the evolution of automation. The Internet of Things provides access to big data. Automation enables application of artificial intelligence to more and more applications.
Knowledge management is thus gathering appropriate data, with the major task of filtering out noise. The next function is to store data (database management), which can be drawn upon to enable data interpretation and modeling (data mining). We discuss application fields, analytic techniques, and analytic strategies, seeking ways to leverage real-time data obtained through surveillance systems, and use of integrated information systems to more comprehensively support knowledge access.
Future research is needed to study the impact of real-time streaming and processing. We need better understanding of risk assessment tools, and how disruptive technologies such as artificial intelligence and blockchain impact decision making systems.
Bio: He has published research in over 200 refereed journal articles, primarily on the topic of multiple objective decision-making, information technology, supply chain risk management, and data mining. He has authored over 50 books, to include Decision Aids for Selection Problems, Introduction to Information Systems Project Management, Managerial Issues of Enterprise Resource Planning Systems, Supply Chain Risk Management, and Supply Chain Information Technology. Additionally, he has co-authored the books Introduction to Business Data Mining, Enterprise Risk Management, Advanced Data Mining Techniques, Enterprise Information Systems, Enterprise Risk Management Models, and Financial Enterprise Risk Management. He has served as associate editor of Service Business, Decision Support Systems, and Decision Sciences and co-editor in chief of International Journal of Services Sciences. He is a member of the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was a Lowry Mays endowed Professor at Texas A&M University from 1999 to 2001. He was named the Raymond E. Miles Distinguished Scholar award for 2002, and was a James C. and Rhonda Seacrest Fellow from 2005 to 2006. He was named Best Enterprise Information Systems Educator by IFIP in 2006. He received the Herbert Simon Award for Outstanding Contribution in Information Technology and Decision Making in 2021. He is a Fellow of the Decision Sciences Institute.
Keynote Speaker II
Professor Christopher W. Clifton
Purdue University, USA
Speech Title: Machine Learning Induced Unfairness
Abstract: Machine Learning is being widely adopted to support a variety of decision-making processes. With this increase in use, there has been numerous examples where the outcomes exhibit bias, particularly against historically marginalized groups. Prominent examples include Amazon scrapping a resume screening prototype due to gender bias, the COMPAS recidivism tool used in setting bail in the U.S. showing racial bias, and facial recognition systems showing lower accuracy for Asians and African-Americans. This is widely blamed on historical biases in the training data. This talk discusses other sources of bias, conditions where we should expect machine learning to exhibit biases not found in the training data, and why these situations are more likely to harm the poor and other disadvantaged groups. The talk concludes with an overview of ongoing efforts to address these issues, including standards processes.
Bio: Dr. Clifton is a Professor of Computer Science and, by courtesy, Statistics at Purdue University. He works on data privacy, particularly with respect to analysis of private data. From 2013-2015, Dr. Clifton served as a program director at the National Science Foundation. Prior to joining Purdue in 2001, he was a principal scientist in the Information Technology Division at the MITRE Corporation. Before joining MITRE in 1995, he was an assistant professor of computer science at Northwestern University. He is an IEEE Fellow and ACM Distinguished Member.
Keynote Speaker III
Professor Philippe Fournier-Viger
Shenzhen University, China
Speech Title: Advances and challenges for the automatic discovery of interesting patterns in data
Abstract: Intelligent systems and tools can play an important role in various domains such as for factory automation, e-business, and manufacturing. To build intelligent systems and tools, high-quality data is generally required. Moreover, these systems need to process complex data and can yield large amounts of data such usage logs, images, videos, and data collected from industrial sensors. Managing the data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems and other applications.
The talk will first briefly review early study on designing algorithms for identifying frequent patterns. Then, an overview of recent challenges and advances will be presented to identify other types of interesting patterns in more complex data. Topics that will be discussed include high utility patterns, locally interesting patterns, and periodic patterns. Lastly, the SPMF open-source software will be mentioned and opportunities related to the combination of pattern mining algorithms with traditional artificial intelligence techniques for intelligent systems will be discussed.
Bio: Philippe Fournier-Viger (Ph.D) is a Canadian researcher, distinguished professor at Shenzhen University (China). Five years after completing his Ph.D., he came to China in 2015 and became full professor after receiving a talent title from the National Science Foundation of China. He has published more than 375 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent systems and applications, which have received more than 11,000 citations. He is the founder of the popular SPMF data mining library, offering more than 250 algorithms to find patterns in data, cited in more than 1,000 research papers. He is former associate edito-in-chief of the Applied Intelligence journal and has been keynote speaker for over 15 international conferences and co-edited four books for Springer. He is a co-founder of the UDML, PMDB and MLiSE series of workshops held at the ICDM, PKDD, DASFAA and KDD conferences.
Keynote Speaker Ⅳ
Professor Yu-wang Chen
The University of Manchester, UK
Speech Title: Data, Knowledge and Decision Analytics
Abstract: Decision analytics allow individuals and organizations to transform data and aggregate knowledge to support informed decision making. However, real-world decision making problems are often characterized by multiple sources of data and different types of complex information. In this talk, I will briefly introduce my research on decision analytics in the context of data and uncertain knowledge, and illustrative examples will be used from the fields of both engineering and management.
Bio: Yu-Wang Chen is a Professor in Decision Sciences and Business Analytics at Alliance Manchester Business School (AMBS), The University of Manchester and Turing Fellow at The Alan Turing Institute. Prior to joining AMBS, he worked briefly as a Postdoctoral Research Fellow at the Department of Computer Science, Hong Kong Baptist University. He received the PhD degree in Control and System Engineering from Shanghai Jiao Tong University. His research focuses primarily on Decision Sciences and Data Analytics, including their applications in business analytics, supply chain risk analysis, healthcare decision support, etc. He has published over 100 research articles in leading journals and conferences in the fields of Decision Sciences and Data Analytics.