This is a full-day lecture-style tutorial delivered fully online.
Nov. 19, 2020
2:15 - 3:15pm Part A
4:15 - 5:15pm Part B
Nov. 20, 2020
2:30 - 3:30pm Part C
3:30 - 4:30pm Part D
Tutorial Online Link
Video Link for the recorded tutorial will be made available when it is ready.
Dubbed as the new oil of our age, data has been univer- sally recognized as the most important element in today’s economy. Yet the current global data economy is seriously flawed in a number of important aspects including privacy concerns, data valuation, data accounting, data pricing and data auditing. The solution to these long-neglected prob- lems is to rigorously establish “data” as a new asset class and invest research effort into the whole bundle of problems lying at the intersection of data science and other domains which we collectively put under the umbrella of “governance”. In this tutorial, we aim to introduce the notion of data as asset and systematically examine various components and fron- tiers in the scope of data asset governance, which includes data ownership, data pricing, data trading and data audit- ing. We would also illustrate the data economy ecosystem in terms of how data flow among individual users, private sectors and public sectors, zooming into two case studies (1) Personal data as emerging asset class; and (2) B-to-B data sharing and exchange.
- Data Asset: What and Why
- 1.1 Background
- 1.2 Definition
- 1.3 Motivation
- Data Asset Governance:
- 2.1 Value
- Data Pricing
- Data Value Extraction
- 2.2 Right
- Type of Right
- Right Confirmation
- Right Enforcement
- 2.3 Control
- Incentivization & Penalization
- 2.4 Technologies
- Distributed Ledger Technology & Consensus Protocols
- Federated Learning and Trusted Computing Technology
- Data Economy Ecosystem:
- Case Study: Personal Data as Emerging Asset Class
- Case Study: B-to-B Data Sharing and Exchange
- Challenges and Future Directions
Prof. Feida Zhu
Associate Professor, Singapore Management University
Founder, Symphony Protocol
Prof. Feida Zhu is currently a tenured associate professor at Singapore Management University. His research interests include large-scale data mining and machine learning, block chain, text mining, graph/network mining and social network analysis, with emphasis on their application to business, financial and consumer innovation.
Feida Zhu was the founding director of both the Pinnacle Lab for Analytics with China Ping An Insurance Group and DBS-SMU Life Analytics Lab, focusing on social data mining and analysis for finance industry. Prof. ZHU has been the Founder and Chief Scientist of Symphony Protocol, which is a next-generation blockchain-based protocol to empower a data-driven economy by democratized and personalized Intelligence with privacy by design.
Prof. ZHU has published extensively at peer-reviewed top international venues, including ICDE, VLDB, SIGMOD, KDD, WWW, JMLR, TODS, TKDE, etc. His work on large-scale frequent pattern mining has won the Best Student Paper Awards at 2007 IEEE International Conference on Data Engineering (ICDE’07) and 2007 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’07). He also received the Best Paper Award at the 21th International Conference on Database Systems for Advanced Applications (DASFAA’16) and the Best Demo Paper Award at The 17th International Conference on Web-Age Information Management (WAIM’16). He won the Early Career Award of PAKDD’19 and is the General Co-Chair of ICDM’18 and KDD’21. Prof. ZHU obtained his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009, supervised by Prof. Jiawei Han.
Prof. Jian Pei
Professor, Simon Fraser University
Jian Pei is currently a Professor in the School of Computing Science and an associate member of the Department of Statistics and Actuarial Science at Simon Fraser University, Canada. His general areas include data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications. He is recognized as a Fellow of the Royal Society of Canada (RSC), Academy of Science, a Fellow of ACM and a Fellow of IEEE.
Jian Pei is a productive and influential author in data mining, database systems, and information retrieval. Since 2000, he has published one textbook, two monographs and over 200 research papers in refereed journals and conferences, which have been cited over 94,000 times in literature, and over 36,000 times in the last 5 years. His H-index is 87. His research has generated remarkable impact substantially beyond academia. His algorithms have been adopted by industry in production and popular open source software suites. He is responsible for several commercial systems of unprecedentedly large scale.
Jian Pei received many prestigious awards, including the 2017 ACM SIGKDD Innovation Award, the 2015 ACM SIGKDD Service Award, the 2014 IEEE ICDM Research Contributions Award, the British Columbia Innovation Council 2005 Young Innovator Award, an NSERC 2008 Discovery Accelerator Supplements Award (100 awards cross the whole country), an IBM Faculty Award (2006), a KDD Best Application Paper Award (2008), an IEEE ICDE Influential Paper Award (2018), a PAKDD Best Paper Award (2014), a PAKDD Most Influential Paper Award (2009), and an IEEE Outstanding Paper Award (2007).