Keynote Speaker Ⅰ
Prof. Schahram Dustdar
TU Wien, Austria
Speech Title: Learning and reasoning for distributed computing continuum ecosystems
Abstract: A captivating set of hypotheses from the field of neuroscience suggests that human and animal brain mechanisms result from few powerful principles. If proved to be accurate, these assumptions could open a deep understanding of the way humans and animals manage to cope with the unpredictability of events and imagination. Modern distributed systems also deal with uncertain scenarios, where environments, infrastructures, and applications are widely diverse. In the scope of IoT-Edge-Fog-Cloud computing, leveraging these neuroscience-inspired principles and mechanisms could aid in building more flexible solutions able to generalize over different environments.
Keynote Speaker Ⅱ
Prof. Chuan-Ming Liu
National Taipei University of Technology (Taipei Tech)
Biography: Dr. Chuan-Ming Liu is a professor in the Department of Computer Science and Information Engineering (CSIE), National Taipei University of Technology (Taipei Tech), TAIWAN, where he was the Department Chair from 2013-2017. He received his Ph.D. in Computer Science from Purdue University in 2002 and joined the CSIE Department in Taipei Tech in the spring of 2003. In 2010 and 2011, he has held visiting appointments with Auburn University, Auburn, AL, USA, and the Beijing Institute of Technology, Beijing, China. He has services in many journals, conferences and societies as well as published more than 100 papers in many prestigious journals and international conferences. Dr. Liu was also the co-recipients of the best paper awards in many conferences, including ICUFN 2015 Excellent Paper Award, ICS 2016 Outstanding Paper Award, MC 2017 Best Poster Award, WOCC 2018 Best Paper Award, MC 2019 Best Poster Award, MC 2021 Best Paper Award, and WOCC 2021 AAEE Best Paper Award. His current research interests include big data management and processing, uncertain data management, data science, spatial data processing, data streams, ad-hoc and sensor networks, location-based services.
Speech Title: Data Management over Uncertain IoT Data Streams in Edge Computing
Abstract: With the evolution of computing, modern computing environments tend to be decentralized and autonomous. One of such examples in internetworking is the wireless sensor networks to the internet of things (IOT) with cloud computing. Furthermore, in order to relieve the workload at the cloud server and have real-time feedbacks for the services, edge servers take a part in the cloud system. With such an emerging computing architecture, the ways for data management should be reviewed or re-designed. In this talk, we will introduce the trends of emerging computing environments and the promising edge cloud systems with IOT applications. The data in such environments, like the sensed data, may not be accurate and are referred to as uncertain data. We thus consider the precise and uncertain data in our work. In addition, the data are produced or generated dynamically and continuously. With tons of various data collected as time evolves, our works thus focus on the Big Data processing on the IOT with Edge Cloud systems. Some interesting query types will be presented, including probabilistic nearest neighbours query, probabilistic skyline query, and probabilistic top-k dominating query. Two major scenarios of the problems will be introduced: one is to continuously monitor the query results over the internet of mobile things in a decentralized fashion and the other is to effectively monitor the global query results over multiple data uncertain data streams. Some proposed effective mechanisms to achieve the objectives will also be covered in this talk.
Keynote Speaker Ⅲ
Prof. Xudong Jiang
Fellow of IEEE
Nanyang Technological University
Biography: Xudong Jiang (Fellow of IEEE) received the B.Eng. and M.Eng. from the University of Electronic Science and Technology of China (UESTC), and the Ph.D. degree from Helmut Schmidt University, Hamburg, Germany. From 1998 to 2004, he was with the Institute for Infocomm Research, A-Star, Singapore, as a Lead Scientist and the Head of the Biometrics Laboratory. He joined Nanyang Technological University (NTU), Singapore, as a Faculty Member in 2004. Currently, he is a Assoc. Professor in School of EEE, NTU. Dr Jiang holds 7 patents and has authored over 200 papers with over 40 papers in the IEEE journals, where 30 papers in IEEE Signal Processing Society Journals and 6 papers in IEEE T-PAMI. Three of his journal papers have been listed as the top 1% highly cited papers in the academic field of Engineering by Essential Science Indicators. He also published 38 papers in IEEE-SP conferences ICASSP/ICIP/ICME and 13 papers in top conferences CVPR/ICCV/ECCV. He served as IEEE-SPS IFS TC Member from 2015 to 2017, Associate Editor for IEEE SPL from 2014 to 2018 and Associate Editor for IEEE T-IP from 2016 to 2020. Dr Jiang is currently an IEEE Fellow and serves as Senior Area Editor for IEEE T-IP and Editor-in-Chief for IET Biometrics. His current research interests include Signal/image processing, machine learning, pattern recognition and computer vision.
Speech Title: The Role of Dimensionality Reduction in Pattern Recognition
Abstract: Finding/extracting low-dimensional structures in high-dimensional data is of increasing importance, where data/signals lie in observational spaces of thousands, millions or billions of dimensions. The curse of dimensionality is in full play here: We have to conduct inference with a limited human knowledge. Machine learning is a solution that becomes hotter to boiling. This is evidenced by numerous techniques published in the past decades, many of which are in prestige conferences and journals. Nevertheless, there are some fundamental concepts and issues still unclear or in paradox. For example, we often need many processing steps in a complex information discovery/recognition system. As the information amount cannot be increased and must be reduced by any processing, why do we need it before the main processing? This simple question is nontrivial in machine learning. People proposed numerous machine learning approaches but seem either unaware of or avoiding this fundamental issue. Although extracting discriminative information is indisputably the ultimate objective for pattern recognition, this talk will challenge it as a proper or effective criterion for the machine learning-based dimension reduction or feature extraction, though it has been employed by almost all researchers.
Keynote Speaker Ⅳ
Prof. Ling Tok Wang
ER Fellow, Department of Computer Science School of Computing, National University of Singapore
Biography: Dr. Tok Wang LING is an emeritus professor in the Department of Computer Science at National University of Singapore. He was Head of IT Division, Deputy Head of the Department of Information Systems and Computer Science, and Vice Dean of the School of Computing of the University. He received his PhD and M. Math., both in computer science, from University of Waterloo (Canada) and B.Sc. in Mathematics from Nanyang University (Singapore). His research interests include Database Modeling, Entity-Relationship Approach, Object-Oriented Data Model, Normalization Theory, Semi-Structured Data Model, XML Twig Pattern Query Processing, XML and Relational Database Keyword Query Processing, Temporal Database keyword Query Processing. He served on the steering committees of 5 international conferences and was the steering committee chair of ER, DASFAA, and BigComp conferences. He was Conference Co-chair of 12 international conferences, including ER 2004, DASFAA 2005, SIGMOD 2007, VLDB 2010, BigComp 2015, and ER 2018. He served as Program Committee Co-chair of 6 international conferences, including DASFAA 1995, ER 1998, ER 2003, and ER 2011. He served on the program committee of more than 160 international database conferences and workshops such as VLDB, CIKM, EDBT, ER, DASFAA, DEXA, etc. He received the ACM Recognition of Service Award in 2007, the DASFAA Outstanding Contributions Award in 2010, and the Peter P. Chen Award in 2011. He is an ER Fellow.
Speech Title: Conceptual Modeling Views on Relational Databases vs Big Data
We first give a brief introduction to big data, then we recall and highlight some limitations and performance issues of RDBMS for database applications. We revisit some important fundamental concepts in relational data model which have big impact on the performance, such as FD and MVD, normal forms, redundancy and updating anomalies, join of relations, ACID for handling concurrent transactions, and parallel and distributed databases, etc. We then briefly review the basic data models of the 4 major categories of NoSQL databases for big data applications. Next, we compare the relational data model and big data model using a set of application requirements and characteristics to help users to decide when to use SQL or NoSQL for big data applications. We describe some existing database techniques which can be used to improve the performances for certain categories of database applications in RDBMS, such as materialized view, data redundancy, horizontal and vertical partitioning of data in physical database schema design, etc. We present some hardly or not mentioned but very important concepts and issues in data/schema integration, such as entity resolution, relationship identification, primary key vs object identifier (OID), local OID vs global OID, and local FD/MVD vs global FD/MVD, etc. These concepts are related to Object-Relationship-Attribute Semantics (ORA-semantics) and they have significant impact on the quality and correctness of the integrated databases.
WBDC Past Speakers
Prof. Jian Pei
Simon Fraser University,
Prof. Massimo Marchiori
University of Padua, Italy
Prof. Victor Chang
Teesside University, UK
Prof. Xiaofang Zhou
The Hong Kong University of
Science and Technology