Keynote Speakers
Invited Speakers

Keynote Speakers

Divyakant Agrawal

University of California at Santa Barbara,USA

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Title: Demystifying Blockchains: Decentralized and Fault- tolerant Storage for the Future of Big Data?
Abstract: Bitcoin is a successful and interesting example of a global scale peer-to-peer cryptocurrency that integrates many techniques and protocols from cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the cryptocurrency system. The novel aspect of Blockchain is that historical data about currency transactions is maintained in the absence of any central authority. This property of Blockchain has given rise to the possibility that the future applications will transition from centralized databases to a fully decentralized storage based on blockchains. In this talk, we start by developing an understanding of the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent efforts to design scalable and efficient blockchains.
Biography: Divyakant Agrawal is a Professor of Computer Science at the University of California at Santa Barbara. His research interests are in the areas of databases, distributed systems, cloud computing, and big data infrastructures and analysis. He is the Fellow of the ACM, the IEEE, and the AAAS. He serves as the Editor-in-Chief of Journal of Distributed and Parallel Databases and serves on the Editorial boards of ACM Transactions of Spatial Algorithms and Systems and ACM Books. He has published 400+ articles on databases and distributed systems and has supervised 35+ PhD students during his tenure at the University of California at Santa Barbara.

Yoshiharu Ishikawa

Nagoya University, Japan

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Title: Fault Tolerant Data Stream Processing in Cooperation with OLTP Engine

Abstract: In recent years, with the increase of big data and the spread of IoT technology and the continual evolution of hardware technology, the demand for data stream processing is further increased. Meanwhile, in the field of database systems, a new demand for HTAP (hybrid transactional and analytical processing) that integrates the functions of on-line transaction processing (OLTP) and on-line analytical processing (OLAP) is emerging. Based on this background, our group started a new project to develop data stream processing technologies in the HTAP environment in cooperation with other research groups in Japan. Our main focus is to develop new data stream processing methodologies such as fault tolerance in cooperation with the OLAP engine. In this paper, we describe the background, the objectives and the issues of the research.

Biography: Yoshiharu Ishikawa received the B.Eng., M.Eng., and Dr.Eng. degrees in computer science, all from University of Tsukuba in 1989, 1991, and 1994, respectively. He joined Nara Institute of Science and Technology (NAIST) as an assistant professor in April 1994. In April 1999, he moved to University of Tsukuba and worked as an assistant professor and an associate professor. From April 2006, he is a full professor in Nagoya University. Currently he belongs to Graduate School of Informatics, Nagoya University. His research interests include databases, especially in spatial and spatio-temporal databases, data stream management, mobile databases, and scientific databases. He was a visiting researcher of University of Maryland and Carnegie Mellon University from 1998 to 1999. He is a member of ACM, IEEE CS, IEICE, IPSJ, and DBSJ. He is now working as a general chair for VLDB 2020 in Tokyo.

Mukesh Mohania

IBM Australia

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Title: Blockchain-powered Big Data Analytics Platform

Abstract: As crypto-currencies and other business blockchain applications are becoming mainstream, the amount of transactional data as well as business contracts and documents captured within various ledgers are getting bigger and bigger. Further, blockchains provide enterprises and consumers with greater confidence in the integrity of the data that have been captured. This gives rise to the new level of analytics that marries the advantages of both blockchain and big data technologies to provide trusted analysis on validated and quality big data. Particularly, blockchain-based big data is a perfect source for subsequent analytics because the big data maintained on the blockchain is both secure (i.e., tamper-proof and cannot be forged) and valuable (i.e., validated and abundant). In addition, data integration and advanced analysis across on-chain and off-chain data present enterprises with even more complete business insights. In this paper, we first discuss a blockchain-based business application for micro-insurance and AI marketplaces, which render blockchain-generated big data scenarios. Then, we describe the design of a blockchain-powered big data analytics platform as well as our initial steps being taken along the development of this platform.

Biography: Mukesh Mohania was an IBM Distinguished Engineer in IBM Research - India, and is currently working in IBM Research - Australia, in the areas of Blockchain, and Cognitive Data and Analytics. He is also an Adjunct Professor at University of South Australia, Australian National University, and University of Melbourne. He has worked extensively in the areas of Information Management and Autonomic Computing. His work in these areas has led to the development of new products and also influenced several existing IBM products. He has received several awards within IBM, such as "Best of IBM", "Excellence in People Management", "Outstanding Innovation Award", "Technical Accomplishment Award", "Leadership By Doin", and many more. He has published more than 120 Research papers in International Conferences and Journals and also filed more than 80 patents in these or related areas, and more than 50 have already been granted. He is an IBM Master Inventor and a member of IBM Academy of Technology. He has held several visible positions in professional activities, like VLDB 2016 Conference Organizing Chair, ACM India Vice-President. He is an ACM Distinguished Scientist and currently chairing ACM Distinguished Service Award Committee in 2017-2018.

Invited Speakers

Prof. Yanchun Zhang

Director, Centre for Applied Informatics, Victoria University, Australia.

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Title: Analysis of Narcolepsy Based on Single-Channel EEG Signals
Abstract: A normal person spends about a third of his life in sleep. Healthy sleep is vital to people's normal lives. Sleep analysis can be used to diagnose certain physiological and neurological diseases such as apnea, insomnia and narcolepsy. This paper will introduce the sleep stage and the corresponding electroencephalogram (EEG) characteristics at each stage. We used the deep convolutional neural network (CNN) to classify original EEG data with narcolepsy. We use perturbations based on frequency to generate adversarial examples to analyze the characteristics of narcolepsy in different sleep stages. We find that perturbations at specific frequencies affect the classification results of deep learning.
Biography: Yanchun Zhang is a Professor and Director of Centre for Applied Informatics at Victoria University since 2004. Dr Zhang obtained a PhD degree in Computer Science from The University of Queensland in 1991. His research interests include databases, data mining, web services and e-health. He has published over 300 research papers in international journals and conference proceedings including ACM Transactions on Computer and Human Interaction (TOCHI), IEEE Transactions on Knowledge and Data Engineering (TKDE), VLDBJ, SIGMOD and ICDE conferences, and a dozen of books and journal special issues in the related areas. Dr. Zhang is a founding editor and editor-in-chief of World Wide Web Journal (Springer) and Health Information Science and Systems Journal (Springer), and also the founding editor of Web Information Systems Engineering Book Series and Health Information Science Book Series. He is Chairman of International Web information Systems Engineering Society (WISE). He was a member of Australian Research Council's College of Experts (2008-2010), and also serves as expert panel member at various international funding agencies such as National Natural Science Fund of China (NSFC), “National 1000 Talents Program” of China, the Royal Society of New Zealand Marsden Fund and National Natural Science Fund of China (NSFC). He is one of the National "Thousand Talents Program" Experts in China since 2010 (currently with Fudan University).

Srinath Srinivasa

IIIT Bangalore, India

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Title: Design of the Cogno Web Observatory for Characterizing Online Social Cognition
Abstract: It is important to occasionally remember that the World Wide Web (WWW) is the largest information network the world has ever seen. Just about every sphere of human activity has been altered in some way, due to the web. Our understanding of the web has been evolving over the past few decades ever since it was born. In its early days, the web was seen just as an unstructured hypertext document collection. However, over time, we have come to model the web as a global, participatory, socio-cognitive "space". One of the consequences of modeling the web as a space rather than as a tool, is the emergence of the concept of Web observatories. These are application programs that are meant to observe and curate data about online phenomena. In this talk, we present the underlying model behind our Web observatory called Cogno, that is meant to observe online social cognition. Social cognition refers to the way social discourses lead to the formation of collective worldviews. Social media is modeled as a "marketplace of opinions" where different opinions come together to form "narratives" that not only drive the discourse, but may also bring some form of returns to the opinion holders. The problem of characterizing social cognition is defined as breaking down a social discourse into its constituent narratives, and for each narrative, its key opinions, and the key people driving the narrative.
Biography: Srinath Srinivasa heads the Web Science lab and is the Dean (R&D) at IIIT Bangalore, India. Srinath holds a Ph.D (magna cum laude) from the Berlin Brandenburg Graduate School for Distributed Information Systems (GkVI) Germany, an M.S. (by Research) from IIT-Madras and B.E. in Computer Science and Engineering from The National Institute of Engineering (NIE) Mysore. He works in the area of Web Science — that models of the impact of the web on humanity. Technology for educational outreach and social empowerment has been a primary motivation driving his research. He has participated in several initiatives for technology enhanced education including the VTU Edusat program, The National Programme for Technology Enhanced Learning (NPTEL) and an educational outreach program in collaboration with Upgrad. He is a member of various technical and organizational committees for international conferences like International Conference on Weblogs and Social Media (ICWSM), ACM Hypertext, COMAD/CoDS, ODBASE, etc. He is also a life member of the Computer Society of India (CSI). As part of academic community outreach, Srinath has served on the Board of Studies of Goa University and as a member of the Academic Council of the National Institute of Engineering, Mysore. He has served as a technical reviewer for various journals like the VLDB journal, IEEE Transactions on Knowledge and Data Engineering, and IEEE Transactions on Cloud Computing. He is also the recipient of various national and international grants for his research activities.

Subhash Bhalla

The University of Aizu, Japan

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Title:  Polystore Data Management Systems for Managing Scientific Data-sets in Big Data Archives
Abstract:  Large scale scientific data sets are often analyzed for the purpose of supporting workflow and querying. User need to query over different data sources. These systems manage intermediate results. Most prototypes are complex and have an ad hoc design. These require extensive modifications in case of growth of data and change of scale, in terms of data or number of users. New data sources may arise to further complicate the ad hoc design. The polystore data management approach provides 'data independence' for changes in data profile, including addition of cloud data resources. The users are often provided a quasi- relational query language. In many cases, the polystore systems support distinct tasks that are user defined workflow activity, in addition to providing a common view of data resources.
Biography:  Dr. Subhash Bhalla teaches at University of Aizu since 1993. His area of study is Distributed Information Systems. He completed studies up to PhD from IIT Delhi. He started teaching at School of Computer and Systems Sciences at JNU in 1986. After that, he briefly worked at Sloan School of Management at MIT during 1987-88. His current interests include- standards in Electronic Health Records Databases, data modeling in Big data archives in Time-domain Astronomy and Polystore database systems.

Sharma Chakravarthy

The University of Texas at Arlington

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Title: Humble Data Management to Big Data Analytics/Science: A Retrospective Stroll
Abstract: We are on the cusp of analyzing a variety of data being collected in every walk of life in diverse ways and holistically as well as developing a science (Big Data Science) to benefit humanity at large in the best possible way. This warrants developing and/or using new approaches -- technological, scientific, and systems -- in addition to building upon and integrating with the ones that have been developed so far. With this ambitious goal, there is also the risk of these advancements being misused or abused as we have seen so many times with respect to new technologies. In this presentation, we take the audience on a retrospective stroll on the approaches that have come about for managing and analyzing data over the last 40+ years. Since the advent of Database Management Systems (or DBMSs) and especially the Relational DBMSs (or RDBMSs), data management and analysis have seen several significant strides. Today, data has become an important tool (or even a weapon) in society and its role and importance is unprecedented. The goal of this paper is to provide the audience an understanding of data management and analysis approaches with respect to where we have come from, motivations for developing them, and what this journey has been about in a short span of 40+ years. We sincerely hope this presentation provides a historical as well as a pedagogical perspective for those who are new to the field and provides a useful perspective that they can relate to and appreciate for those who have been working and contributing to the field.
Biography: Prof. Chakravarthy is an ACM Distinguished Scientist. He is also an IEEE Senior Member. He organized (General Co-Chair) the 13 th international Conference on Distributed Event-Based Systems (DEBS) in 2013 at UT Arlington. He spent the summers of 2013, 2014, and 2017 at the Rome Air Force Research Laboratory (AFRL-Rome) working on, respectively, continuous query processing over fault-tolerant networks and applying stream processing framework to video stream analysis. He is a co-author of the book "Stream Data Processing: A Quality of Service Perspective" published by springer in 2009. Sharma Chakravarthy is Professor of Computer Science and Engineering Department at The University of Texas at Arlington, Texas since 2000. He established the Information Technology Laboratory (IT Lab) at UT Arlington in Jan 2000. Sharma Chakravarthy has also established the NSF funded, Distributed and Parallel Computing Cluster at UT Arlington. He is the recipient of the university-level "Creative Outstanding Researcher" award for 2003 and the department level senior outstanding researcher award in 2002. He is well known for his work on stream data processing, semantic query optimization, multiple query optimization, active databases (HiPAC project at CCA and Sentinel project at the University of Florida, Gainesville), and more recently scalability issues in graph mining, social network analysis, and graph analysis of multilayered networks. His group at UTA is currently adapting map/reduce and other paradigms for scaling graph mining algorithms to very large graphs and for answering graph queries. He has applied machine learning techniques to rank answers, identify general- and topic-based experts in a Question-Answer (or Q-A) social network. His work on InfoSift - a classification system for text, email, and web - has used graph mining techniques. His current research includes fusion using multi-layered networks, stream data processing for video analysis, scaling graph mining algorithms for analyzing social networks, active and real-time databases, distributed and heterogeneous databases, query optimization, and multi-media databases. He has published over 200 papers/book chapters in refereed international journals and conference proceedings. He has given tutorial on a number of database topics, such as graph mining, active, real- time, distributed, object-oriented, and heterogeneous databases in North America, Europe, and Asia. He is listed in Who's Who Among South Asian Americans and Who's Who Among America's Teachers. Prior to joining UTA, he was with the University of Florida, Gainesville for 10 years. Prior to that, he worked as a Computer Scientist at the Computer Corporation of America (CCA) for 3 years and as a Member, Technical Staff at Xerox Advanced Information Technology, Cambridge, MA for a year. Sharma Chakrvarthy received the B.E. degree in Electrical Engineering from the Indian Institute of Science (IISc), Bangalore and M.Tech from IIT Bombay, India. He worked at TIFR (Tata Institute of Fundamental Research), Bombay, India for a few years. He received M.S. and Ph.D degrees from the University of Maryland in College park in 1981 and 1985, respectively.

Praveen Paruchuri

IIIT Hyderabad

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Title: Fusion of Game Theory and Big Data for AI Applications
Abstract: With the increasing reach of the Internet, more and more people and their devices are coming online which has resulted in the fact that, a significant amount of our time and a significant number of tasks are getting performed online. As the world moves faster towards more automation and as concepts such as IoT catch up, a lot more (data generation) devices are getting added online without needing the involvement of human agents. The result of all this is that there will be lots (and lots) of information generated in a variety of contexts, in a variety of formats at a variety of rates. Big data analytics therefore becomes (and is already) a vital topic to gain insights or understand the trends encoded in the large datasets. For example, the worldwide Big Data market revenues for software and services are projected to increase from $42$ $Billion$ USD in 2018 to $103$ $Billion$ in 2027. However, in the real-world it may not be enough to just perform analysis, but many times there may be a need to operationalize the insights to obtain strategic advantages. Game theory being a mathematical tool to analyze strategic interactions between rational decision-makers, in this paper, we study the usage of Game Theory to obtain strategic advantages in different settings involving usage of large amounts of data. The goal is to provide an overview of the use of game theory in different applications that rely extensively on big data. In particular, we present case studies of four different Artificial Intelligence (AI) applications namely Information Markets, Security systems, Trading agents and Internet Advertising and present details for how game theory helps to tackle them. Each of these applications has been studied in detail in the game theory literature, and different algorithms and techniques have been developed to address the different challenges posed by them.

Biography: Dr. Praveen Paruchuri is an Associate Professor at IIIT Hyderabad in the Machine Learning Lab. He obtained his Ph.D. from the University of Southern California in 2007 and his post doctorate from the Carnegie Mellon University. His research interests span Applied Artificial Intelligence and Machine Learning, Multi-agent Systems and Game theory. Dr. Paruchuri’s Ph.D. thesis is well-known for initiating the development of a deployed game theoretic resource allocation application called the ARMOR system that currently performs efficient scheduling of security resources at the Los Angeles International airport. This work laid the foundation for enhancement of ARMOR for deployment at many locations of international importance such as the New York, LA, Boston ports and others. Dr. Paruchuri’s research in the area of security scheduling has received wide publicity and his work has been described in various international news media such as Newsweek, LA Times, Times of India, Lenta.Ru etc. He is the first author of a book, has 40+ technical publications including two finalists for best paper awards and has two patents based on his work. He was officially nominated by USC in 2008 for the TR-35 award and was profiled as an innovator by the USC Stevens Institute for Innovation.

Ravi Kothari

Ashoka University, India

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Title: Deep Neural Network Based Image Captioning.
Abstract: Generating a concise natural language description of an image enables a number of applications including fast keyword based search of large image collections. Primarily inspired by deep learning, recent times have witnessed a substantially increased focus on machine based image caption generation. In this paper, we provide a brief review of deep learning based image caption generation, the datasets and metrics used to evaluate the captioning algorithms. We conclude the paper with some discussion on promising directions for future research.

Biography: Ravi Kothari started his professional career as an Assistant Professor in the Department of Electrical and Computer Engineering of the University of Cincinnati, OH, USA where he later became a tenured Associte Professor and Director of the Artificial Neural Systems Laboratory. After about 10 years in the academia, he joined IBM Research and held several positions over the years including that of Chief Scientist of IBM Research, India and the Global Chief Architect of the IBM-Airtel outsourcing account where he introduced the first-ever stream based processing of telecom data (Airtel is one of the world's largest full service telecom providers and the Chief Architect is responsible for the IT strategy, design and realization across more than 20 countries). He also became IBM's first Distinguished Engineer from India. After about 15 years in IBM, he joined Accenture for a short stint as a Fellow, Technical Labs prior to returning to academia as Professor of Computer Science at Ashoka University.
Dr. Kothari's expertise lies in Machine Learning, Pattern Recognition, AI, Data Mining, Big Data and other data-driven technologies. His present research focuses on multiple aspects of Artifical Intelligence including the exploration of "creative" machines.
Dr. Kothari has served as an Associate Editor of the IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, Pattern Analysis and Applications (Springer) as well as on the program committees of various conferences. He was an IEEE Distinguished Visitor (2003-2005 and 2006-2009) and was a member of the IBM Academy of Technology and the IBM Industry Academy. He was a recipient of the 2008 Gerstner Award (IBM's highest team award), the Best of IBM award (IBM's highest individual award) and is a fellow of the Indian National Academy of Engineering.

The Sixth International Conference On
Big Data Analytics
December 18 - 21, 2018

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