Prof. Kyuseok Shim

Kyuseok Shim is a Professor at Department of Electrical and Computer Engineering in Seoul National University, Korea. Before that, he was an Assistant Professor at Computer Science Department in KAIST (Korea), a member of technical staff at Bell Laboratories (Murray Hill) and a member of Quest Data Mining project at IBM Almaden Research Center. He is currently an Editor-In-Chief of the VLDB Journal and was previously an Associate Editor for the IEEE TKDE, VLDB as well as PVLDB journals. He also served as a Program Co-chair for PAKDD 2003, WWW 2014, ICDE 2015, APWeb 2016, BigComp 2019 and ICDM 2019 conferences and have been serving on Program Committees of the leading database as well as data mining conferences including SIGMOD, SIGKDD, ICDE, ICDM, EDBT, VLDB, WWW and CIKM. He became an ACM fellow and an IEEE fellow for the contributions to scalable data mining and query processing. He was previously a member of the VLDB Endowment Board of Trustees and is currently a steering committee member of PAKDD as well as DASFAA conferences. He served as the president of the Korean Information Scientist and Engineers (KIISE) in 2022 and became a member of National Academy of Engineering of Korea in 2023. He has been working in the area of data mining, machine learning, privacy preservation, query processing, query optimization, data warehousing, semi-structured data (XML), stream data and histograms.

Cardinality estimation of an approximate substring query is an important problem in database systems. Traditional approaches build a summary from the text data and estimate the cardinality using the summary with some statistical assumptions. Since deep learning models can learn underlying complex data patterns effectively, they have been successfully applied and shown to outperform traditional methods for cardinality estimations of queries in database systems. However, since they are not yet applied to approximate substring queries, we investigate a deep learning approach for cardinality estimation of such queries. Although the accuracy of deep learning models tends to improve as the train data size increases, producing a large train data is computationally expensive for cardinality estimation of approximate substring queries. Thus, we develop efficient train data generation algorithms by avoiding unnecessary computations and sharing common computations. We also propose a deep learning model as well as a novel learning method to quickly obtain an accurate deep learning-based estimator. Extensive experiments confirm the superiority of our data generation algorithms and deep learning model with the novel learning method.

Prof. Baochun Li

Baochun Li received his B.Engr. degree from the Department of Computer Science and Technology, Tsinghua University, China, in 1995 and his M.S. and Ph.D. degrees from the Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, in 1997 and 2000. Since 2000, he has been with the Department of Electrical and Computer Engineering at the University of Toronto, where he is currently a Professor. He holds the Bell Canada Endowed Chair in Computer Engineering since August 2005. His current research interests include cloud computing, security and privacy, distributed machine learning, federated learning, and networking.

Dr. Li has co-authored more than 440 research papers, with a total of over 24000 citations, an H-index of 86 and an i10-index of 314, according to Google Scholar Citations. He was the recipient of the IEEE Communications Society Leonard G. Abraham Award in the Field of Communications Systems in 2000. In 2009, he was a recipient of the Multimedia Communications Best Paper Award from the IEEE Communications Society, and a recipient of the University of Toronto McLean Award. He is a member of ACM and a Fellow of IEEE.

While there were thousands of papers in the literature in the recent years on federated learning, very few are concerned with its production use in practice. As the (perhaps only) practical paradigm that preserves data privacy when training a shared machine learning model, I expect that federated learning will be widely used in production. But are research results in the literature valid and practical in production federated learning systems? In this talk, I will share our recent experiences with claims in the existing literature along the lines of privacy leakage attacks, and show that their assumptions do not necessarily hold in production systems. I will also introduce more efficient ways to solve the "unlearning" problem, which is necessary due to regulatory constraints in production, such as the GDPR. Our experiments were conducted on Plato, a new open-source federated learning framework that I designed from scratch in the past two years to be as close to production systems as possible, while using a minimum amount of computing resources.

Prof. Pavel Pevzner

Pavel Pevzner is Ronald R. Taylor Chair and Distinguished Professor of Computer Science and Engineering at University of California, San Diego. He holds Ph.D. from Moscow Institute of Physics and Technology, Russia. He was named Howard Hughes Medical Institute Professor in 2006. He was elected the Association for Computing Machinery (ACM) Fellow, the International Society for Computational Biology (ISCB) Fellow, the American Association for Advancement in Science (AAAI) Fellow, and the European Academy of Sciences member (Academia Europaea) member. He was awarded the ISCB Senior Scientist Award, and the ACM Kanellakis Theory and Practice Award. Dr. Pevzner authored textbooks "Computational Molecular Biology: An Algorithmic Approach", "Introduction to Bioinformatics Algorithms" (with Neal Jones), “Bioinformatics Algorithms: an Active Learning Approach” (with Phillip Compeau), and “Learning Algorithms through Programming and Puzzle Solving” (with Alexander Kulikov). He co-developed the “Bioinformatics” and “Data Structure and Algorithms” online specializations on Coursera with over a million enrollments in the last decade.

Even though the first draft human genome was generated two decades ago, many biomedically important regions (such as centromeres) represented the terra incognita of the genome sequencing until recently. The long-read DNA sequencing technologies are now transforming genome sequencing toward generating complete genomes and metagenomes. They also raised novel bioinformatics challenges that represent the key bottlenecks for the ongoing large-scale genomics and metagenomics projects. I will describe some bioinformatics challenges in complete genomics/metagenomics, discuss the recently developed approaches for their solution, show how these approaches bring us closer to the goal of complete genome/metagenome sequencing, and illustrate some applications of these approaches in metagenomics-based search for novel antibiotics.

Prof. Kilian Weinberger

Kilian Weinberger is a Professor in the Department of Computer Science at Cornell University. He received his Ph.D. from the University of Pennsylvania in Machine Learning and his undergraduate degree in Mathematics and Computing from the University of Oxford. During his career he has won several best paper awards at ICML (2004), CVPR (2004, 2017), AISTATS (2005) and KDD (2014, runner-up award). In 2011 he was awarded the Outstanding AAAI Senior Program Chair Award and in 2012 he received an NSF CAREER award. He was elected co-Program Chair for ICML 2016 and for AAAI 2018 and currently serves as a board member and president-elect of the ICML society. In 2016 he was the recipient of the Daniel M Lazar '29 Excellence in Teaching Award. In 2021 he became a finalist for the Blavatnik National Awards for Young Scientists. Kilian Weinberger's research focuses on Machine Learning and its applications, in particular, metric learning, Gaussian Processes, computer vision, perception for autonomous vehicles, and deep learning. Before joining Cornell University, he was an Associate Professor at Washington University in St. Louis and before that he worked as a research scientist at Yahoo! Research in Santa Clara.

Recent progress in autonomous driving has been fuelled by improvements in machine learning. Ironically, most autonomous vehicles do not learn while they are in operation. If a car is used in the same location multiple times, it will act identically every single time. We propose to leverage and learn from repetition by allowing a neural network to save some of its activations in a geo-referenced data base that can be retrieved later on. If a vehicle is used in the same location multiple times, it builds up a rich data set of past network activations that aid object detection in the future. This allows it to recognize objects from afar when they are only perceived by a few pixels or LiDAR points. We further demonstrate that it is in fact possible to completely bootstrap an object detection classifier only based on repetition. Our approach has the potential to drastically improve the accuracy and safety of self-driving cars, enable them for sparsely populated areas, and allow them to adapt naturally to their local environment over time.

Prof. Dusit Niyato

Dusit Niyato is currently a President's Chair Professor in Computer Science and Engineering in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He received B.E. from King Mongkuk’s Institute of Technology Ladkrabang (KMITL), Thailand in 1999 and Ph.D. in Electrical and Computer Engineering from the University of Manitoba, Canada in 2008. Dusit's research interests are in the areas of distributed collaborative machine learning, Internet of Things (IoT), edge intelligent metaverse, mobile and distributed computing, and wireless networks. Dusit won the IEEE Communications Society (ComSoc) Best Survey Paper Award, IEEE Asia-Pacific Board (APB) Outstanding Paper Award, the IEEE Computer Society Middle Career Researcher Award for Excellence in Scalable Computing in and Distinguished Technical Achievement Recognition Award of IEEE ComSoc Technical Committee on Green Communications and Computing. Dusit also won a number of best paper awards including IEEE Wireless Communications and Networking Conference (WCNC), IEEE International Conference on Communications (ICC), IEEE ComSoc Communication Systems Integration and Modelling Technical Committee and IEEE ComSoc Signal Processing and Computing for Communications Technical Committee 2021. Currently, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials, an area editor of IEEE Transactions on Vehicular Technology, editor of IEEE Transactions on Wireless Communications, associate editor of IEEE Internet of Things Journal, IEEE Transactions on Mobile Computing, IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), and ACM Computing Surveys. He was a guest editor of IEEE Journal on Selected Areas on Communications. He was a Distinguished Lecturer of the IEEE Communications Society for 2016-2017. He was named the 2017-2022 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.

Artificial Intelligence Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This talk aims to on the deployment of AIGC applications, e.g., ChatGPT, at mobile edge networks, i.e., mobile AIGC networks that provide personalised and customised AIGC services in real-time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. We highlight some future research directions and open issues for the full realisation of mobile AIGC networks.

Prof. Christian S. Jensen

Christian S. Jensen is Professor of Computer Science at Aalborg University, Denmark. His research concerns analytics, including machine learning, data mining, and query processing, and data management, with a focus on temporal and spatio-temporal data. Christian is an ACM and IEEE Fellow, and he is a member of Academia Europaea, the Royal Danish Academy of Sciences and Letters, and the Danish Academy of Technical Sciences. He has received several awards, most recently the 2022 ACM SIGMOD Contributions Award and the 2019 IEEE TCDE Impact Award. He is on the board of Villum Fonden, a major funder of research in Denmark and is vice-chair of the Danish National Research Foundation. He is president of the steering committee of the Swiss National Research Program on Big Data. In Germany, he is on the scientific advisory board (SAB) of the Max Planck Institute for Informatics; and in Norway, he chairs the SAB of the Norwegian Research Center for AI Innovation.

The web is often being accessed by users with an accurate geo-location, and massive geo-tagged content is available on the web, including web pages, points of interest, and microblog posts. Studies suggest that each week, billions of keyword-based queries are issued that have local intent and that target such content. This situation gives prominence to spatial web data management, and it opens to an area full of exciting opportunities and challenges. A prototypical query takes a user location and user-supplied keywords as arguments, and it returns content that is spatially and textually relevant to these arguments. Due perhaps to the rich semantics of geographical space and its importance to our daily lives, many kinds of relevant spatial web query functionality exist.

Rooted in work by the speaker and his colleagues, the presentation reviews selected functionality and concepts related to spatial web object ranking and querying; it presents functionality targeting different kinds of user intent; and it outlines directions for future research.

Prof. Noshir Contractor

Noshir Contractor is the Jane S. & William J. White Professor of Behavioral Sciences in the McCormick School of Engineering & Applied Science, the School of Communication and the Kellogg School of Management and Director of the Science of Networks in Communities (SONIC) Research Group at Northwestern University. He is also the President of the International Communication Association (ICA).

Professor Contractor has been at the forefront of three emerging interdisciplines: network science, computational social science, and web science. He investigates how social and knowledge networks form – and perform – in contexts including business, scientific communities, healthcare, and space travel. His book Theories of Communication Networks (co-authored with Peter Monge) received the 2003 Book of the Year award from the Organizational Communication Division of the National Communication Association and the 2021 International Communication Association’s Fellows Book Award. He is a Fellow of the American Association for the Advancement of Science (AAAS), the Association for Computing Machinery (ACM), the International Communication Association (ICA), and the Network Science Society. He also received the Distinguished Scholar Award from the National Communication Association and the Lifetime Service Award from the Communication, Digital Technology & Organizations Division of the Academy of Management. He received the 2022 Simmel Award from the International Network for Social Network Analysis. In 2018 he received the Distinguished Alumnus Award from the Indian Institute of Technology, Madras, where he received a Bachelor’s in Electrical Engineering. He received his Ph.D. from the Annenberg School of Communication at the University of Southern California.

Organisations need to do more than analyse data on demographic attributes to bring the performance of people analytics in the algorithmically infused workplace up — and in line with the hype. We need to focus not only on who people are but also on who they know. The potential for social network analysis to identify “high potentials,” who has good ideas, who is influential, and what teams will get work done efficiently and effectively is well established based on decades of research. The challenge has been collecting network data via time-consuming surveys, which elicit low response rates, and have high obsolescence. This talk presents empirical examples ranging from corporate enterprises to simulated long-duration space exploration to demonstrate how we can leverage people analytics – and in particular relational analytics - to mine “digital exhaust”— data created by individuals every day in their digital transactions, such as e-mails, chats, “likes,” “follows,” @mentions, and file collaboration— to address challenges they face with issues such as team assembly and team conflict.

Prof. Sihem Amer-Yahia

Sihem Amer-Yahia is a Silver Medal CNRS Research Director and Deputy Director of the Lab of Informatics of Grenoble. She works on exploratory data analysis and fairness in job marketplaces. Before joining CNRS, she was Principal Scientist at QCRI, Senior Scientist at Yahoo! Research and Member of Technical Staff at at&t Labs. Sihem is PC chair for SIGMOD 2023 and vice president of the VLDB Endowment. She currently leads the Diversity, Equity and Inclusion initiative for the database community

Exploratory Data Analysis (EDA) is an iterative and often tedious process. Several strategies have been proposed to ease the burden on users in EDA ranging from stepwise to full-guidance approaches. Stepwise approaches rely on computing utility functions that determine the best action to take at each step. Full-guidance approaches rely on learning end-to-end exploration policies. Today’s big question is how to commodify EDA and make it easily deployable for all but for that we need to know what users are looking for: are they looking for a needle in a haystack, taking a tour of the data, or are they feeling lucky? This talk will investigate those questions and discuss the challenges of storing learned pathways through data or regenerating them when needed.

Prof. Jiangchuan Liu

Jiangchuan Liu is a Full Professor in the School of Computing Science, Simon Fraser University, British Columbia, Canada. He is a Fellow of The Canadian Academy of Engineering, an IEEE Fellow, and an NSERC E.W.R. Steacie Memorial Fellow. In the past he worked as an Assistant Professor at The Chinese University of Hong Kong, a research fellow at Microsoft Research Asia, and an EMC-Endowed Visiting Chair Professor of Tsinghua University.

He received the BEng degree (cum laude) from Tsinghua University, Beijing, China, in 1999, and the PhD degree from The Hong Kong University of Science and Technology in 2003, both in computer science. He is a co-recipient of the inaugural Test of Time Paper Award of IEEE INFOCOM (2015), ACM SIGMM TOMCCAP Nicolas D. Georganas Best Paper Award (2013), ACM Multimedia Best Paper Award (2012), and IEEE MASS Best Paper Award (2021).

His research interests include multimedia systems and networks, cloud and edge computing, social networking, online gaming, and Internet of things/RFID/backscatter. He has served on the editorial boards of IEEE/ACM Transactions on Networking, IEEE Transactions on Network Sciences and Engineering, IEEE Transactions on Big Data, IEEE Transactions on Multimedia, IEEE Communications Surveys and Tutorials, and IEEE Internet of Things Journal. He is a Steering Committee member of IEEE Transactions on Mobile Computing and Steering Committee Chair of IEEE/ACM IWQoS (2015-2017). He was TPC Co-Chair of IEEE INFOCOM'2021.

While the modern power grid is known to be reliable in urban cities, it is not and probably will never be the case for remote areas, e.g., northern Canada or Gobi Desert in China. It is necessary to view energy supply as an integral part towards sustainable edge computing and Internet of Things (IoT), both for maintaining their services and for protecting the sensitive environment.

In this talk, based on our real-world experience in Canada and China, I will discuss sustainable edge and IoT design and deployment from the energy perspective. I will present our recent works on nonintrusive load monitoring, data-driven battery analysis and optimization, and mobile video analytics, particularly on their integration with edge nodes in harsh environments. We also advocate the new paradigm that spatially decouples energy supply and sensing/computing modules and strongly believe that batteryless will be the future for tiny IoT devices. We accordingly present a series of our recent works on batteryless communication and sensing, including high-throughput and multi-hop backscatter, distribution excitation, multi-track acoustic sensing with ambient energy , etc.

Prof. Tuomas Sandholm

Tuomas Sandholm is Angel Jordan University Professor of Computer Science at Carnegie Mellon University and a serial entrepreneur. His research focuses on the convergence of artificial intelligence, economics, and operations research. He is Co-Director of CMU AI, and Founder and Director of the Electronic Marketplaces Laboratory. In parallel with his academic career, he was Founder, Chairman, first CEO, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world's largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. Since 2010, his algorithms have been running the national kidney exchange for the United Network for Organ Sharing, where they autonomously make the kidney exchange transplant plan for 80% of U.S. transplant centers together each week. He also co-invented never-ending altruist-donor-initiated chains and his algorithms created the first such chain. Such chains have become the main modality of kidney exchange worldwide and have led to around 10,000 life-saving transplants. He invented liver lobe and multi-organ exchanges, and the first liver-kidney swap took place in 2019. Sandholm has developed the leading algorithms for several general classes of game. The team that he leads is the multi-time world champion in computer heads-up no-limit Texas hold’em, which is the main benchmark and decades-open challenge problem for testing application-independent algorithms for solving imperfect-information games. Their AI Libratus became the first and only AI to beat top humans at that game. Then their AI Pluribus became the first and only AI to beat top humans at the multi-player game. That is the first superhuman milestone in any game beyond two-player zero-sum games. He is Founder and CEO of Strategic Machine, Inc., which provides solutions for strategic reasoning under imperfect information in a broad set of applications ranging from poker to other recreational games to business strategy, negotiation, strategic pricing, finance, cybersecurity, physical security, auctions, political campaigns, and medical treatment planning. He is also Founder and CEO of Strategy Robot, Inc., which focuses on government applications. He is also Founder and CEO of Optimized Markets, Inc., which is bringing a new optimization-powered paradigm to advertising campaign sales, pricing, and scheduling. He holds a Ph.D. and M.S. in computer science and a Dipl. Eng. (M.S. with B.S. included) with distinction in Industrial Engineering and Management Science. Among his many honors are the AAAI Award for AI for the Benefit of Humanity, Minsky Medal, IJCAI McCarthy Award, AAAI/IAAI Engelmore Award, IJCAI Computers and Thought Award, inaugural ACM Autonomous Agents Research Award, CMU’s Allen Newell Award for Research Excellence, Sloan Fellowship, NSF Career Award, Carnegie Science Center Award for Excellence, and Edelman Laureateship. He is Fellow of the ACM, AAAI, INFORMS, and AAAS. He holds an honorary doctorate from the University of Zurich.

So far, game theory has mainly been used for reasoning by humans. The models have thus been quite stylized and coarse. There is now another, more nascent, use of game theory where the game is computationally solved in its full detail - or else in a large, faithful abstraction thereof. Novel approaches, game representations, and algorithms from the last 18 years have enabled game theory to advance significantly beyond its traditional boundaries. I will discuss that state of the art. The talk is based on my presentation at the 2021 Nobel Symposium: 100 Years of Game Theory, and also includes brand new results.

Prof. Tatsuya Harada

Tatsuya Harada is a Professor in the Research Center for Advanced Science and Technology at the University of Tokyo. His research interests center on visual recognition, machine learning, and intelligent robot. He received his Ph.D. from the University of Tokyo in 2001. He is also a team leader at RIKEN AIP and a Vice Director of Research Center for Medical Bigdata at National Institute of Informatics, Japan.

It's been about a decade since the advent of deep learning. During this time, it has transformed not only the field of computer science, but also numerous scientific and industrial domains. One of the main advantages of deep learning is its ability to learn feature extractors. In this talk, we will review the statistical feature extraction methods developed by our research team before and after the advent of deep learning, and explore their relevance to state-of-the-art neural network architectures. We will also discuss the future direction of feature extraction, with an emphasis on the space - particularly the non-Euclidean space - that serves as the basis for deep neural networks construction.

Prof. Toru Ishida

Toru Ishida is a Professor Emeritus of Kyoto University. He was a President of IEICE from 2021 to 2022, and a member of the Science Council of Japan from 2011 to 2017. He has been working on Autonomous Agents and Multi-Agent Systems since 1988, and served as a General Co-Chair of the first AAMAS in 2002. He has led several research projects, such as Digital City Kyoto and Language Grid. In Digital City Kyoto, he conducted a large-scale evacuation simulation. Various social psychological experiments have also been conducted. In Language Grid, He initiated (probably) the world first multi-lingual project using machine translation with Chinese, Korean, and Malaysian colleagues. For interdisciplinary education, he established a Design School in Kyoto University with his colleagues in Informatics, Architecture, Mechanical Engineering, Management and Psychology.

In this talk, we will review the history of two research streams of the metaverse. One concerns simulations, where various experiments have been conducted in virtual environments called digital cities or digital twins. The other is about interaction, where the psychological impact of communication in virtual space has been analysed. After reviewing the two research streams, we discuss the possibility of applying the metaverse to interdisciplinary education.

Dr. C. Mohan

Dr. C. Mohan is currently a Distinguished Visiting Professor at Tsinghua University in China, a Visiting Researcher at Google, a Member of the inaugural Board of Governors of Digital University Kerala, and an Advisor of the Kerala Blockchain Academy (KBA) and the Tamil Nadu e-Governance Agency (TNeGA) in India. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He was an IBM researcher for 38.5 years in the database, blockchain, AI and related areas, impacting numerous IBM and non-IBM products, the research and academic communities, and standards, especially with his invention of the well-known ARIES family of database locking and recovery algorithms, and the Presumed Abort distributed commit protocol. This IBM (1997-2020), ACM (2002-) and IEEE (2002-) Fellow has also served as the IBM India Chief Scientist (2006-2009). In addition to receiving the ACM SIGMOD Edgar F. Codd Innovations Award (1996), the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards, Mohan was elected to the United States and Indian National Academies of Engineering (2009), and named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. During the last many years, he focused on Blockchain, AI, Big Data and Cloud technologies (https://bit.ly/sigBcP, https://bit.ly/CMoTalks). Since 2017, he has been an evangelist of permissioned blockchains and the myth buster of permissionless blockchains. During 1H2021, Mohan was the Shaw Visiting Professor at the National University of Singapore (NUS) where he taught a seminar course on distributed data and computing. In 2019, he became an Honorary Advisor to TNeGA for its blockchain and other projects. In 2020, he joined the Advisory Board of KBA. Since 2016, Mohan has been a Distinguished Visiting Professor of China’s prestigious Tsinghua University. In 2021, he was inducted as a member of the inaugural Board of Governors of the new Indian university Digital University Kerala (DUK). Mohan has served on the advisory board of IEEE Spectrum, and on numerous conference and journal boards. In 2022, he became a consultant at Google with the title of Visiting Researcher. He has also been a Consultant to the Microsoft Data Team. Mohan is a frequent speaker in North America, Europe and Asia. He has given talks in 43 countries. He is highly active on social media and has a huge network of followers. More information can be found in the Wikipedia page at https://bit.ly/CMwIkP and his homepage at https://bit.ly/CMoDUK

It has been four decades since the commercial emergence of relational database management systems. Since that time, I worked on various database management topics at the birthplace of the relational model and the SQL language, until my retirement 2 years ago as an IBM Fellow at IBM Research in Silicon Valley. As someone who has been, and who continues to be, active as an individual contributor in the database area for over four and a half decades, in this talk, I will give a broad overview of the evolution of the data landscape. I will discuss not only research topics but also the trends in the commercial and standards arenas. I will also present market share numbers from data analysts. I intend for my observations to be of value to not only deeply technical people in the research and product spaces but also to management and administration type people who are responsible for data related topics.