Central Michigan University, USA
Dr. En-Bing Lin is Chair and Professor of Mathematics at Central Michigan University, USA. He has taught and visited at several institutions including Massachusetts Institute of Technology, University of Wisconsin-Milwaukee, University of California, Riverside, University of Toledo, UCLA, and University of Illinois at Chicago. He received his Ph. D. in Mathematics from Johns Hopkins University. His research interests include Data Analysis, Image Processing, Applied and Computational Mathematics, Wavelet Analysis and Applications, and Mathematical Physics. He has supervised a number of graduate and undergraduate students. Dr. Lin serves on the editorial boards of several mathematics journals and several academic committees of regional and national associations. He has organized several special sessions at regional IEEE conference and American Mathematical Society national and regional meetings.
Speech Title: Big Data Analytics for Approximations in Information Systems
Abstract: We begin with a brief overview of the current trend in Big data. With the increasing use of advanced technology, the amount of data in our world has been exploding. Poor quality of big data results in some inaccurate insights or compliance failures that give rise to partially complete information systems. In order to obtain complete information systems, we use Rough set theory (RST) which was introduced by Pawlak in 1982 as a way to deal with data analysis based on approximation methods in information systems. It is a powerful tool that has many applications in a number of different areas, such as feature selection, decision making, rule mining, data prediction, environment, banking, medicine, bioinformatics, pattern recognition, data mining, machine learning and others. RST is intrinsically a study of equivalence relations on the universe (a set of object). In fact, rough sets can be used to represent ambiguity, vagueness and general uncertainty. Given some relations between objects in the set, we can construct lower and upper approximations of the objects. We intend to use some advanced computing methods to determine lower and upper approximations and find several properties of the characteristics of objects within RST, as well as to obtain generalized RST. We extend RST to VP model and Fuzzy rough set theory (FRST) which is an extension of rough set theory that deal with continuous setting instead of discrete attributes in RST. Traditional algorithm cannot satisfy the needs of big data computing. In this presentation, we will give some advanced computing methods that can solve our problems effectively and extend the methods to VPGRST and FRST. Several examples will also be presented.
Prof. Tianrui Li
Southwest Jiaotong University, China
Tianrui Li received his B.S. degree, M.S. degree and Ph.D. degree from the Southwest Jiaotong University, China in 1992, 1995 and 2002 respectively. He was a Post-Doctoral Researcher at Belgian Nuclear Research Centre (SCK • CEN), Belgium from 2005-2006, a visiting professor at Hasselt University, Belgium in 2008, the University of Technology, Sydney, Australia in 2009 and the University of Regina, Canada in 2014. And, he is presently a Professor and the Director of the Key Lab of Cloud Computing and Intelligent Technique of Sichuan Province, Southwest Jiaotong University, China. Since 2000, he has co-edited 3 books, 12 proceedings, 8 special issues of international journals, received 1 Chinese invention patent and published over 150 research papers (e.g., IEEE Transaction on Knowledge and Data Engineering, IEEE Transactions on Information Forensics & Security, IEEE Transactions on Audio Speech and Language Processing, IEEE Transactions on Industrial Electronics, IEEE Transactions on Cybernetics, IEEE Transactions on Communications, Information Science) in refereed journals and conferences. Five papers was included in ESI ISI database. He serves as area editor of IJCIS (SCI), editor of KBS (SCI), etc. He has served as ISKE2007-2015, CRSSC2015, CWI2014, JRS2012 program chairs, IEEE HPCC 2015, GrC 2009 program vice chairs and RSKT2008, FLINS2010 organizing chairs, etc. and has been a reviewer for several leading academic journals. He is a senior member of IEEE, CCF, CAI, Chair of IEEE CIS Chengdu Chapter and CCF YOCSEF Chengdu Chair (2013-2014)..
Speech Title:Big Data Analysis: A Granular Computing Approach
Abstract: Emerging information technologies and application patterns in modern information society are growing in an amazing speed which causes the advent of the era of Big Data. Exploring efficient and effective data mining and knowledge discovery methods to handle Big Data with rich information has become an important research topic in the area of information science. This talk focuses on our recent work on big data analysis based on the theory of granular computing. It covers the preprocessing of missing values in big data by a multi-view approach, incremental learning based on rough set theory for dynamic data, and data-model parallelism by granular computing theory for big data.
AMIT, AMU MOE FDRE under UNDP and Ex. Academic Faculty Ambassador for Cloud Computing Offering (AI), IBM USA
External Consultant & Adviser (IT), ILO [ An autonomous Agency of United Nations]- Geneva
Confluence of the four; an eminent academician, Author, Researcher and Rehabilitation Technology Expert, Prof. DP Sharma is a strategic innovator and international orator. He is recipient of 46 National and International Awards and wide range of appreciations including India’s one of the highest civilian Award “Sardar Ratna Life Time Achievement International Award-2015” (in memory of first Deputy Prime Minister of Independent India )for Mind of Steel & Social Transformation through Education & Technology. He has published 16 text books and 6 distance learning Series of Computer Science, Information Technology and 127 International research paper/ articles in referred International Journals/conference pertaining to IT and multidisciplinary Informatics. He provides strategic counsel and future based advisory to transform the technology, management and governance practices through ICT and Cloud based Computing Systems and Models. He is serving various International Agencies like ILO (An autonomous Agency of United Nations), UNDP, DST, VLIR-UOS- Belgium, IICDEP and NYAS-USA in internal and external consulting capacities. Prof Sharma’s 27 keynote speeches around the world on innovation, techno imagination, change agents and strategic foresight in IT are commendable. He is member of Editorial/Reviewer boards of 29 International Journals like IEEE, Elsevier and Springer. He has supervised 12 PhD scholars in Cloud Computing and cross disciplinary informatics from USA, Fiji, India, Saudi Arabia and Ethiopia. He has guided over 25 Projects under Academic Initiative of IBM-USA and later awarded as ‘Academic Faculty Ambassador for Cloud Computing offering’ by IBM-USA. He holds fellowships of several International scientific organizations like International Fellow of FSFE- Germany, International Fellow of International Association of Computer Science and IT- Singapore and 22 Senior Memberships/Memberships like IEEE-USA, ISOC, UNV, UACEE-Australia, CSTA-ACM- USA & Canada, SIE-Singapore and ICST-Belgium. His contribution to the technology based rehabilitation under UN Convention for PWDs, ILO & UNDP schemes has been incomparable in the world. He has advised numerous projects and policy drafts for down-trodden communities under the schemes of UN Convention for PWDs and IT Ethical Policies for Least Developed Countries (LDCs). He is an iconic academic planner and promoter of innovations like blend of technology (ICTs) with teaching-learning process in higher education and research. He has traveled 39 countries like South Korea, China, India, North America, USA, Singapore, Malaysia, Egypt, Ethiopia, Dubai, Yemen, Netherlands, France, Germany, Sudan, Turkey etc. for academic assignments, distinguished Plenary/Keynote Speeches in International Conferences and conventions.
Speech Title: Change & Convergence: Dynamic enablers for Re-inventions in Big Data over Cloud
Keywords: Change, Convergence, Big Data, Cloud Computing, Determinism
Abstract: There is nothing permanent in this universe. Technologies have been playing vital in changing everything. Today Big Data and Cloud Computing have proven to be two killer technologies over existing legacy of technologies. Change is irreversible phenomenon in technological domains. This speech will start by throwing multidirectional lights on hidden insights and future prospectus of these technologies by covering diminishing factors which are unknown today but tomorrow will adversely and seriously affect human intelligence. Are these technologies going on the right path for long term suitability? Big Data and Cloud Computing have been trying to converge as complementary technologies but what is hidden behind the other side of the coin? What if? Human generation is not well prepared for these dynamic & digital changes where gearing reverse is not possible. Is dominance of technological determinisms is a good sign? The speech will try to answer the aforementioned questions by convergence of two perspectives i.e. Human & Technology determinisms.
Assoc. Prof. Qing
School of Computing and Information Systems, Athabasca University, Canada
Dr. Qing Tan is an associate professor in School of Computing and Information Systems at Athabasca University, Canada. He was born and raised in Chengdu. He left his beloved hometown in 1977 to study Aviation Automation at the Northwest Polytechnic University. He earned his PhD in Cybernetics Engineering for Robotics from the Norwegian Institute of Technology (NTNU - Norwegian University of Science and Technology) in 1993. As a foreign senior research fellow, he did the research on Telepresence Robot for the human acts simulation program at the Japan Atomic Energy Research Institute in 1994. He did his post-doctorial fellowship at University of Alberta in 1996. He joined Athabasca University in 2007 with extensive IT industrial working experiences in Canada. Dr. Tan is teaching and developing both undergraduate and graduate courses including Mobile Computing, Computer Networking, E-Commerce, Enterprise Modeling, Cloud Computing, and Big Data Analytics. Dr. Tan’s research interests and engagements include Location-Based Technologies, Mobile Computing, Adaptive Mobile Learning, Telepresence Robot, Cloud Computing, Internet of Things, Big Data Analytics, Cyber-Physical Systems, and Computer Network and Cyber Security. Dr. Tan received several Canadian national and provincial research grants. He has published many research papers on International journals and conferences. He also sits on many international journal editor boards and various conference committees.
Speech Title: The Privacy Issues In Big Data Analytics: How To See And What To Do
Abstract: In this Big Data era, digital data has been increased exponentially with great variety. While lots of data carrying Personally Identifiable Information (PII) has been purposely entered online, a large amount of data has been logged from individual's online footprints, which could be turned into PII or sensitive personal information (SPI). Big Data Analytics (BDA) enables researchers or other stakeholders to dig out data and then turn it into useful or interesting information to serve for whatever purpose and interest. As a society, there is no way to reverse information explosion, and as an individual, there is no more a safe haven for personal privacy. Therefore, individuals should be aware of the situation and take necessary measures to protect their own privacy, and most importantly change their perception of privacy. On the other hand,it becomes urgent for the researchers and other stakeholders to understand the privacy impact imposed by BDA, to integrate privacy into BDA architecture design, algorithm and tool development, and implementation to address the PII and SPI issues before data is collected, stored, and used. Furthermore, it is also important for governmental commissions or regulatory body to update guidelines, rules, and regulations accordingly to protect personal privacy in this Big Data era.
Prof. Dr. Anu A. Gokhale
Illinois State University, USA
Dr. Anu A. Gokhale has completed twenty-five years of university teaching and is currently a professor and coordinator of the computer systems technology program at Illinois State University. She is named Fulbright Distinguished Chair in STEM at the University of Pernambuco, Brazil, 2016-17; is a Fulbright Specialist; and was a Fulbright Scholar to India in 2002. She isa Visiting Professor at Shandong University of Science & Technology in Jinan, China during spring 2017. Dr. Gokhale was honored with the 2011 University Outstanding Researcher Award. Originally from India, she has a master’s in physics‒electronics from the College of William & Mary, and a doctorate from Iowa State University. She presents and publishes her peer-reviewed research, and pursues multi-year projects funded by agencies like the US Department of Education, US Department of State, and National Science Foundation. The current NSF funded project is in Computing Education for the 21st Century. Dr. Gokhale authored a second edition of her book Introduction to Telecommunications, which has an international edition in Chinese. She continues to be an invited keynote speaker at various conferences, latest ones include: 2016 International Conference on Communication and Information Systems, Bangkok, Thailand; 2015 International Conference on Information Technology, Amman, Jordan; and 2014 International Conference on Control, Robotics and Cybernetics, Singapore. She consults for businesses and has delivered multiple workshops. As an active volunteer in IEEE, she has served as R4 Educational Activities Chair, Women in Engineering Coordinator, Chair of International Electro/Information Technology 2010 Conference, and MGA representative to Educational Activities Board. She was honored with the IEEE Third Millennium Medal.
Speech Title: Cloud-Stored Big Data Analytics – A Survey of Algorithms
Abstract: The explosion in data resulting from social, mobile, and cloud computing technologies has created new challenges, and they are being converted into opportunities by the scientific community. Knowledge discovery in database is the application of analytical procedures to extract more insight from multiple formats of data that is captured through various means. Powerful tools for finding anomalies, implicit patterns, and correlations in large volumes of both structured and unstructured data are being investigated. The unstructured data and intangible components like intent and sentiment are difficult to analyze but have untapped potential that would enable comprehension of underlying cognitive processes. The talk will discuss several algorithms and techniques being used to discover knowledge hidden in cloud-stored information, and use it to make data-driven decisions.
Prof. William Wei Song
Dalarna University, Sweden
Dr. William Wei Song is full professor of Information Systems and Business Intelligence and Director of Micro-data Analysis at Dalarna University and docent in Stockholm University, Sweden. Previously he was academic staff at Durham University and the head of the Web Intelligence, Services, and Agent Technology Lab in England. He is also guest professor at Zhejiang University. Prof. Song is PC member of many international conferences and workshops (among others, he was workshop organizer and chair for the WISE conferences in 2000, 2013, and 2014) and editorial boards of international journals. He is a guest editor of the international journal on web services research (IJWSR). Prof. Song has been leader of many research projects, funded by ESPRIT and FP6 (EU), EPSRC (UK), VINOVA (Sweden), NSFC (China) and ITF (Hong Kong SAR). He has published over 150 research papers in key international journals and conferences and has been keynote speaker at a number of international conferences and national forums such as ICKET and Annual IT-Forum Sweden. His research interests cover the research fields of conceptual modelling, semantic web, micro-payment in e-commerce, intensive data analysis, web and service science, requirements engineering, e-learning, e-healthcare, aging care, e-governance, etc.
Dr. Song received his BSc in Computer Science from Zhejiang University, Hangzhou, China, 1982, and his PhD in Computer and Information Sciences from Stockholm University and the Royal Institute of Technology in Sweden, 1995. Since then he has been working at Swedish Research Institute of Systems Development in Sweden, Hong Kong University in Hong Kong, and Durham University in England (UK). Following are his recent publications:
Lou Q., Zhang S., and Song W. Combination of Evaluation Methods for Assessing the Quality of Service for Express Delivery Industry, WISE-QUAT 2015, Wang et al (eds.) LNCS 9419, pp. 414-425
Yan S., Zheng X., Wang Y., Song W., Zhang W. A graph-based comprehensive reputation model: exploiting the social context of opinions to enhance trust in social commerce, Information Sciences, 2015, Vol. 318, 51-72.
Song W. A Conceptual Framework of Information Analysis and Modelling for E-health, International Conference on Smart Healthcare (ICSH), 2014, Zheng, et al (eds.) LNCS 8549, pp. 142-147
Li J., Zheng X., Chen S., Song W., Chen D. An efficient and reliable approach for quality-of-service-aware service composition, Information Sciences, 2014, Vol. 269, 238-254.
Li J., Zhen X., Chen D., Song W. Trust based service selection in service oriented environment, International Journal of Web Services Research, 2012, Vol. 9, No. 3, 23-42.
Geldart J. and Song W. Category-based Equational Reasoning: An Approach to Ontology Reasoning and Integration, in Journal of Logic and Computation (JLC), Vol. 19 (5), pp. 791-806
Song W., Du X., and Munro M. A Concept Graph Approach to Semantic Similarity Computation Method for e-Service Discovery, in the International Journal on Knowledge Engineering and Data Mining, Vol. 1 (1), pp.50-68, Inderscience. 2010 Chen D. and Song W. An Analytic Model of Atomic Service for Services Descriptions, accepted to present at and included in the proceedings of the International Conference on Service Science (ICSS2010), Hangzhou, China, May 15-17, 2010.
Speech Title: Big Data and Semantics
Abstract: We are in the era of "Big Data" - a huge amount of data which is still growing at an amazing speed - and many data analysis methods have been proposed to deal with "Big Data". However, it still seems that powerful machines with powerful tools (intelligent methods) could not compete with human beings. By looking at the sky in the early morning before one goes out they know what they should wear to stay comfortable and stylish, whereas for machines, they must know the temperature, the wind speed, look for clouds (not literally), the sun and so on before an "accurate" prediction of "index of clothes" is provided. Semantics - the meaning of objects - that human beings percept. In this speech I attempt to outline a relationship between big data and their meanings, as well as their presentations, which expectedly points out a novel method for "Big Data" analysis. .