Keynote Talk 1
Deep Data Integration
Wang-Chiew Tan, Research Scientist, Facebook AI
Abstract
We are witnessing the widespread adoption of deep learning techniques as avant-garde solutions to different computational problems in recent years. In data integration, the use of deep learning techniques has helped establish several state-of-the-art results in long standing problems, including information extraction, entity matching, data cleaning, and table understanding. In this talk, I will reflect on the strengths of deep learning and how that has helped move forward the needle in data integration. I will also discuss a few challenges associated with solutions based on deep learning techniques and describe some opportunities for the future work.
The Speaker
Wang-Chiew is a research scientist manager at Meta AI. Before she was the Head of Research at Megagon Labs, where she led the research efforts on building advanced technologies to enhance search by experience. This included research on data integration, information extraction, text mining and summarization. Prior to joining Megagon Labs, she was a Professor of Computer Science at University of California, Santa Cruz. She also spent two years at IBM Research-Almaden.
Keynote Talk 2
Towards instance-optimized data systems
Tim Kraska, Associate Professor, MIT
Abstract
Recently, there has been a lot of excitement around ML-enhanced (or learned) algorithms and data structures. For example, there has been work on applying machine learning to improve query optimization, indexing, storage layouts, scheduling, log-structured merge trees, sorting, compression, sketches, among many other data management tasks. Arguably, the ideas behind these techniques are similar: machine learning is used to model the data and/or workload in order to derive a more efficient algorithm or data structure. Ultimately, what these techniques will allow us to build are “instance-optimized” systems; systems that self-adjust to a given workload and data distribution to provide unprecedented performance and avoid the need for tuning by an administrator. In this talk, I will first provide an overview of the opportunities and limitations of current ML-enhanced algorithms and data structures, present initial results of SageDB, a first instance-optimized system we are building as part of DSAIL@CSAIL at MIT, and finally outline remaining challenges and future directions.
The Speaker
Tim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT’s Computer Science and Artificial Intelligence Laboratory, co-director of the Data System and AI Lab at MIT (DSAIL@CSAIL), and co-founder of Einblick Analytics. Currently, his research focuses on building systems for machine learning, and using machine learning for systems. Before joining MIT, Tim was an Assistant Professor at Brown, spent time at Google Brain, and was a PostDoc in the AMPLab at UC Berkeley after he got his PhD from ETH Zurich. Tim is a 2017 Alfred P. Sloan Research Fellow in computer science and received several awards including the VLDB Early Career Research Contribution Award, the VMware Systems Research Award, the university-wide Early Career Research Achievement Award at Brown University, an NSF CAREER Award, as well as several best paper and demo awards at VLDB, SIGMOD, and ICDE.
Keynote Talk 3
Accelerating Data Analytics in the Era of Ubiquitous Computing: Opportunities and Challenges
Maya Gokhale, Distinguished Member of Technical Staff, Lawrence Livermore National Laboratory, USA
Abstract
With innovations in storage and memory capacity combined with the profusion of acceleration architectures, opportunities abound to gain insight from exponentially increasing data sources. Compute functions can be distributed among sensors, in intermediate network aggregation points, in-transit through network routers and host interface, and in/near the data repositories. Efficiently and securely exploiting these emerging opportunities will spur new research, including re-thinking data structures and algorithms, designing domain-specific languages and compiler optimizations, OS and process-level scheduling and resource management, and overriding all, ensuring security and privacy. This talk will discuss the spectrum of opportunities, challenges, and solutions in this domain.
The Speaker
Maya Gokhale is Distinguished Member of Technical Staff at the Lawrence Livermore National Laboratory, USA. Her career spans research conducted in academia, industry, and National Laboratories. Maya received a Ph.D. in Computer Science from University of Pennsylvania. Her current research interests include data intensive heterogeneous architectures and reconfigurable computing. Maya is co-recipient of an R&D 100 award for a C-to-FPGA compiler, co-recipient of four patents related to memory architectures for embedded processors, reconfigurable computing architectures, and cybersecurity, and co-author of more than one hundred forty technical publications. Maya is on the editorial board of the Proceedings of the IEEE and an associate editor of IEEE Micro. She is a co-recipient of the National Intelligence Community Award, is a member of Phi Beta Kappa, and is an IEEE Fellow.
Founders and Pioneers Keynote Talk
Modern Cloud DBMSs Vindicate Age-Old Work on Shared Disks DBMSs!
C. Mohan, Distinguished Visiting Professor, Tsinghua University, China
Abstract
Over 3 decades ago, when the database research community was enamored of shared nothing database management systems (DBMSs), some of us were focused on DBMSs which were based on the shared disks (SD) architecture. While my own work involved IBM’s DB2 on the mainframe, earlier SD product work had been done by DEC, IBM (with IMS), Oracle and a couple of Japanese vendors. The research community didn’t appreciate that much our SD work even though IBM and Oracle have been quite successful with their SD relational DBMS products. With the emergence of the public cloud, many classical on-premises DBMSs have been ported to the cloud arena. New DBMSs have also been developed from scratch to work in the cloud environment. One of the dominant characteristics of the cloud DBMSs is that they are embracing the SD architecture because of the architectural separation of compute nodes and storage nodes (also called disaggregated storage) in the cloud environment to gain several advantages. I feel that these recent developments vindicate our age-old SD work! In this talk, I will first introduce traditional (non-cloud) parallel and distributed database systems. I will cover concepts like SQL and NoSQL systems, data replication, distributed and parallel query processing, and data recovery after different types of failures. Then, I will discuss how the emergence of the (public) cloud has introduced new requirements on parallel and distributed database systems, and how such requirements have necessitated fundamental changes to the architectures of such systems which includes embracing at least some of the SD ideas. I will illustrate the related developments by discussing the details of several cloud DBMSs.
The Speaker
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 (, ). 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 atand his homepage at.