The 2nd World AI-Big Data Convergence Forum
Abstract
In this Second Annual World AI-BigData Convergence (ABC) Forum, five or six world-renowned speakers from both academia and industry will discuss the issues and problems surrounding the vision of Artificial Intelligence (AI) and Big Data confluence and propose ground-breaking solutions with a full awareness of potential benefits and risks. The objective of this World Forum is to explore the current status and R&D opportunities and issues in the convergence (or confluence) of AI and BigData from a data centric perspective. The objective of the ABC initiative, as defined by Prof. Won Kim in his keynote for iiWAS-2021, is to help propel the current smart data processing to the next level. The ABC vision is based on advancing both AI and Big Data through fuller uses of data and through AI and big data leveraging each other. The fuller uses of data include the use of higher quality data and multimodal data for both AI and Big Data.
Please join us in what we expect will be a very informative, productive, and exciting Forum.
Forum Chair
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Prof. Won Kim Distinguished Professor and AI Vice President of Gachon University, near Seoul, Korea |
Won Kim is currently a distinguished professor with Software Department at Gachon University, near Seoul, Korea. He is also AI Vice President of Gachon University and is Managing Director of the National Program of Excellence in Software Education at Gachon University, sponsored by the Korean Ministry of Science & Technology and Information Technology.
Before joining the faculty of Gachon University, he was a professor with SKKU (SungKyunKwan University), and a senior vice president of Samsung Electronics, both in Korea.
He was the founder and CEO of UniSQL (where he led the development of world’s first object-relational database system) and also Cyber Database Solutions in the US. Before founding UniSQL, he worked as a researcher at IBM Almaden Research Center; and as a research director at MCC(Microelectronics and Computer Technology Corporation), where he led a team that developed the ORION object-oriented database system (one of the first OODBs).
He received a Ph.D. in computer science from the University of Illinois at Urbana-Champaign.
In 2017, he received an Order of Service Merit Medal from the Government of Korea for his services to IT industry for both the US and Korea. In 2018, he received an Alumni Outstanding Educator Award from the Computer Science Department of the University of Illinois at Urbana-Champaign.
In the US, he served as Chair of ACM(Association of Computing Machinery) SIGMOD (special interest group on management of data) and founding Chair of ACM SIGKDD (special interest group on knowledge discovery and data mining). He also served as Editor-in-Chief of ACM Transactions on Database Systems, and founding Editor-in-Chief of ACM Transactions on Internet Technology.
Speakers
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David Taniar |
AI-Big Data in Medical Informatics
Abstract
In this talk, I will explore a few projects in digital health which combine AI and Big Data. The focus is on data to empower medical informatics research and practice. I will discuss (1) EMR(Electronic Medical Records): data analysis of EMR; (2) Patient Registry data linkage and data warehousing of hospital patient records; (3) Medical Imaging: explainable AI and deep learning in medical imaging; and (4) Medical IoT: kidney disease monitoring, chemotherapy monitoring, and other monitoring systems. AI Big Data convergence is an opportunity for collaborative research in medical informatics.
Biography
David Taniar has worked in the area of data engineering with applications in digital health, GIS, and big data. His recent book published by Springer on Data Warehousing and Analytics has attracted more than 25,000 downloads since the book was released in Feb 2022. He has previously published a book on High Performance Parallel Database Processing and Grid Databases by Wiley (2008).
He is a founding editor-in-chief of a number of international journals, including Intl. J. of Data Warehousing and Mining, Mobile Information Systems, Intl. J. of Web Information Systems, and Intl. J. of Web and Grid Services
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Wenny Rahayu |
Big Data IoT in Distributed Smart Manufacturing
Abstract
One of the main drivers behind Big Data in the recent years has been the proliferation of applications and devices that generate data with high velocity in multiple formats. This new data generation, called data streams, requires new ways to manage, process, and analyze. AI and Big Data that deal with IoT data streams have opened up a new era of digital transformation in industry and manufacturing. In this talk I will share some project experiences in the development of a big data ecosystem involving IoT data, the development of data lake for smart factory with sensor data collection/ingestion, and AI based data pre-processing and predictive maintenance.
Biography
Wenny Rahayu has worked in the area of data engineering, database integration and optimization, knowledge discovery, and big data management. She has been a chief-investigator of three ARC (Australian Research Council) Linkage grants, Industry collaboration grants (Airservices and IPL Australia), the Australian Army (Army Research), the AAS (Australia Academy of Science), and collaborators in international grants (Open Geospatial Consortium, Japan JSPS, and Australia Indonesia AIGRP). She has published around 300 papers with more than 7000 citations.
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Ok-Ran Jeong |
An AI framework for intelligent data analysis
Abstract
One of the ways to store human knowledge is the knowledge graphs. Knowledge graphs help people and computers to better tap into the connections among data. However, they have two limitations. First, they are limited in size and scope for most of the human languages. Second, they cannot deal with neologisms that form a part of human common sense. In this talk, I will introduce a means of automating the expansion of the knowledge graph. Based on this, I will discuss an AI framework that enables multi-aspect analysis and prediction of big data.
Biography
Ok-Ran Jeong’s current research interests include big data mining, machine learning, deep learning, and its applications to knowledge graphs, conversational AI, and emotional AI.
She has participated in the Apache CouchDB/AsterixDB Project and has been leading the Polaris Project (big data analysis and prediction system) since 2015. She has authored over 140 papers on AI and Big data in scholarly journals and conferences and has been awarded 5 patents.
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Jhonghyun An |
Convergence of AI & Bigdata, New Map building Trend for Autonomous Driving: crowd-sourced mapping
Abstract
Autonomous driving requires a variety of technologies. It is necessary to recognize the driving situations, understand the traffic signals, and consider the safety of the driver. To this end, various companies are conducting research in their own ways. I will discuss driving environment recognition technology for autonomous driving and a new trend in the map building method, namely, crowd-sourced map building.
Biography
Before joining Gachon University, Jhonghyun An was a senior researcher at the Agency for Defense Development, working on autonomous technology for unmanned vehicles. He carried out a number of projects with car manufacturing companies such as Hyundai Motor and MNSOFT in relation to object detection and tracking technology using 3D laser scanners. He also participated in the 13th Hyundai Autonomous Driving Competition from 2016 to 2018 as a team leader and conducted research on complete autonomous vehicles. His current research interests include computational intelligence, statistical machine learning, and deep learning and its application to intelligent robotics, autonomous vehicles, and robot vision.
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Jiawei Han |
An Annotation-Free, Pretrained-Language Model Integrated Approach to Text Mining
Abstract
The real-world big data are largely dynamic, interconnected, and unstructured texts. It is important to transform such massive unstructured text into structured knowledge. Equipped with pretrained language models and data mining/machine learning methods, it is promising to transform unstructured text into structured knowledge without extensive human annotation. In this talk, I will overview a set of annotation-free text mining methods my research team has developed that transform massive text into structured knowledge.
Biography
Jiawei Han received ACM SIGKDD Innovation Award (2004), IEEE Computer Society Technical Achievement Award (2005), IEEE Computer Society W. Wallace McDowell Award (2009), and Japan's Funai Achievement Award (2018). He is Fellow of ACM and Fellow of IEEE and served as the Director of Information Network Academic Research Center (INARC) (2009-2016) supported by the Network Science-Collaborative Technology Alliance (NS-CTA) program of U.S. Army Research Lab and co-Director of KnowEnG, a Center of Excellence in Big Data Computing (2014-2019), funded by NIH Big Data to Knowledge (BD2K) Initiative. Currently, he is serving on the executive committees of two NSF funded research centers: MMLI (Molecular Make Research Institute)—one of NSF funded national AI centers since 2020 and I-Guide—The National Science Foundation (NSF) Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) since 2021.
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Sharad Mehrotra |
TippersDB: A Middleware System for Developing Smart Space Applications
Abstract
TippersDB is a middleware system designed to build sensor-based smart space analytical applications. TippersDB supports a powerful data model that decouples semantic data about the application domain from sensor data using which the semantic data is derived. By supporting mechanisms to map/translate data, concepts, and queries between the two levels, TippersDB relieves the application developers from having to know or reason about either the type or location of sensors or write sensor-specific code. I will present TippersDB's data model, query-driven translation of sensor data, a summary of the system implementation, and a demonstration of the TippersDB system.
Biography
Sharad Mehrotra is a Fellow of the IEEE, a Distinguished Member of the ACM, and a trustee of the VLDB Endowment. His primary research interests include scalable data analytics, data cleaning, big data, distributed systems, secure databases, privacy, and Internet of Things. He is a recipient of over 12 best paper awards including 2011 SIGMOD Best Paper Award, 2012 SIGMOD Test of Time award, DASFAA ten year best paper awards for 2013 and 2014, ACM ICMR best paper award for 2013, IEEE NCA Best paper award for 2019, IEEE SmartComp 2021, Percom best paper award, 2022, and Dean’s Award for Research in 2016, and a CAREER Award in 1998 from the US National Science Foundation (NSF).