Keynote Speaker：
Dr. Xiaodong Li
Huaihua University, China
Xiaodong Li, PhD, teacher of Huaihua University, member of International Sports Psychology Association, lifetime member of International Chinese Sports and Health Association. He has published more than 50 papers in "Frontiers Aging Neuroscience", "Basic & Clinical Pharmacology & Toxicology", "Journal of Beijing Sport University", "Journal of Tianjin Sport University" and other journals, and participated in many international academic conferences and presented keynote reports. He presided over and participated in more than 10 projects at all levels. He has obtained two invention patents, written two textbooks, and participated in editing many textbooks. His students' innovation and entrepreneurship project under his instruction and guidance was approved by the national level.
Dr.David Taniar
Monash University, Australia
David Taniar is an Associate Professor in the Faculty of Information Technology, Monash University, Australia. He received his MSc and PhD in Computer Science, from Swinburne University of Technology and Victoria University, respectively. His main research area is in Data Engineering, covering Parallel Databases and Big Data, Spatial and Mobile Query Processing, and Data Warehousing and Data Lake. He has a book published by John Wiley & Son (High Performance Parallel Database Processing and Grid Databases, 2008). His upcoming book will be published by Springer (Data Warehousing and Analytics, 2021). He is the Founding Editor-in-Chief of two SCIE journals: Intl J of Data Warehousing and Mining (Q4), and Intl J of Web and Grid Services (Q1). He has successfully supervised more than 20 PhD students until completion. He has had a wealth of experiences in industry projects in various fields, such as healthcare, medicine, utility, energy, manufacturing, etc, where he applied his data engineering expertise in those areas. He frequently delivers keynote speeches at international conferences.
Speech Title: Big Data is all about data that we don't have
Abstract: Big Data is now becoming a buzzword in the information technology industry and research. Is Big Data only about large volumes of data?, and if it is yes, why is it suddenly becoming a trend? Hasn't the growth of data volume been gigantic in the last decade? From a research point of view, it is not surprising to see researchers from all walks of computer science are trying to align their research to Big Data for the sake of being trendy. The question remains whether it tackles the real Big Data problems. This talk describes the misconceptions of Big Data, presents motivating cases, and discusses the unavoidable challenges faced by industry and research.
Keywords: Big Data, Volume, Variety, Velocity
Dr. Witold Pedrycz
Department of Electrical & Computer Engineering, University of Alberta
Witold Pedrycz (IEEE Life Fellow, 2021) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. He is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He received a prestigious Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society. He is a recipient of the IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.
Speech Title: Information Granules and Interpretability in Data Analytics
Abstract: We advocate that there are two factors that immensely contribute to the realization of interpretability of data analytics constructs, namely, a suitable level of abstraction in describing the problem and a logic fabric of the resultant construct. It is demonstrated that their conceptualization and the following realization can be conveniently carried out with the use of information granules (for example, fuzzy sets, sets, rough sets, and alike).
Concepts are building blocks forming the interpretable environment capturing the essence of data and key relationships existing there. The emergence of concepts is supported by a systematic and focused analysis of data. At the same time, their initialization is specified by stakeholders or/and the owners and users of data. We present a comprehensive discussion of information granules-oriented design of concepts and their description by engaging an innovative mechanism of conditional (concept)-driven clustering. The challenging issue of dimensionality reduction is realized by developing relational factorization which can be sought as an interpretable architecture of nonnegative matrix factorization. A detailed example of enhancement of interpretability of functional rule-based models.
Keywords: Data analytics, interpretability, information granules, Granular Computing
Dr. Ingmar Weber
Department of Social Computing, Qatar Computing Research Institute, HBKU
Ingmar Weber is the Research Director for Social Computing at the Qatar Computing Research Institute (QCRI). His interdisciplinary research looks at what online user-generated data can tell us about the offline world and society at large. Working closely with sociologists and demographers he has pioneered the use of online advertising data for complementing official statistics on international migration, digital gender gaps, and poverty. His work is regularly featured in UN reports, and analyses performed by his team have been used to improve operations by UN agencies and NGOs ranging from Colombia to the Philippines. Prior to joining QCRI, Dr Weber was a researcher at Yahoo Research Barcelona. As an undergraduate he studied mathematics at the University of Cambridge before pursuing a PhD at the Max-Planck Institute for Computer Science. He is an ACM, IEEE and AAAI Senior Member and serves as an ACM Distinguished Speaker.
Speech Title: Digital Gender Gaps Seen Through Social Media
Abstract: Gender equality in access to the internet and mobile phones has become increasingly recognized as a development goal. Monitoring progress towards this goal, however, is challenging due to the limited availability of gender-disaggregated data, particularly in low-income countries. In this talk, I’ll give an overview of our work on using non-traditional data sources to study and quantify digital gender gaps.
A key methodological novelty consists of tapping into social media advertising platforms to obtain so-called audience estimates. For example, Facebook provides potential advertisers with information on how many of their users (i) are female, (ii) are aged 13-21, (iii) are living in Chicago, and (iv) have access to an iOS device. Answer: 87k, as of April 23, 2021. Similar estimates can be obtained on Twitter, Weibo, Snapchat and other services.
We use such audience estimates, traditionally provided for budget planning, to build models predicting the ratio of female-to-male internet users around the globe. Our model predictions are regularly shared through the SDGs Today portal (https://sdgstoday.org/dataset/digital-gender-gap). Beyond internet access gender gaps, we use similar data to provide insights into skill gender gaps, and even wealth gender gaps. This demonstrates how anonymous and aggregate data that was originally collected from billions of users to serve targeted advertising can be re-purposed to illuminate different aspects of gender inequality.
The presentation is based joint work with the University of Oxford and supported by Data2X (https://data2x.org/big-data-for-gender-research-grantees-dig-into-digital-data-insights/).
Keywords: Gender Gaps, Social Media, Advertising Data, SDG #5
Dr.Hing Kai Chan
University of Nottingham Ningbo,China
Professor Hing Kai Chan joined the Nottingham University Business School China in September 2014, and is a Professor of Operations Management. He received the MSc degree (with distinction) and the PhD degree from the Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong. He also earned the BEng degree in Electrical and Electronics Engineering and BSc degree in Economics and Management from the University of Hong Kong and the London School of Economics and Political Science respectively.
Professor Chan has published over 100 peer-reviewed academic articles. His publications appear in Production and Operations Management, European Journal of Operational Research, various IEEE Transactions, Decision Support Systems, International Journal of Production Economics, International Journal of Production Research, among others.
Professor Chan is a Fellow of the Institution of Engineering and Technology (FIET), and the Higher Education Academy (FHEA). He is also a Senior Member of the Institute of Electrical and Electronics Engineers, a Member of the Chartered Institute of Marketing and the Chartered Institute of Logistics and Transport. Professor Chan is a Chartered Engineer and a Chartered Marketer. In 2019, he was appointed as the Expert Committee Member of the Ningbo Municipal Commerce Bureau “Supply Chain Innovation and Applications Committee”.
Speech Title: A reflection on social media analytics
Abstract:Social media data is important form of big data. They are user-generated and hence analyzing such data contains certain degree of subjectivity. Based on a number of scholarly works on social media data analysis or social media analytics, the presentation aims to summarize the key issues surrounding social media analytics from a management perspective. For example, how new product development can be facilitated from social media data? How companies can improve customer satisfaction from social media analytics? More importantly, how we can improve customers or stakeholders loyalty by engaging them in using social media analytics, such as in online e-commerce marketplace.
Keywords:Social media analytics, new production development, customer satisfaction, loyalty.