Panels

Plenary Panel on the Convergence of Big Data, IoT/CPS, and SCC (Smart & Connected Communities)

The keywords of Big Data, Internet of Things (IoT), Cyber-Physical Systems (CPS), and Smart & Connected Communities (a.k.a. Smart Cities and Smart Planet) have been around for quite a few years. They started from different disciplines:

  • Big data originated from scientific sensors (e.g., satellites) and enterprise applications (e.g., Walmart cash registers).
  • IoT/CPS started from wireless sensor networks and real-time systems (e.g., the CPSweek series of conferences)
  • Smart Planet, Smart Cities, and SCC started from Autonomic Computing (with these keywords popularized initially by IBM)

These research fields and application areas have been expanding tremendously in recent years, with significant and growing overlaps among them. We believe these overlaps represent huge opportunities for innovative research and potential societal impact. At the same time, they also create unprecedented research challenges that surpass the traditional confines of each original discipline.

As simple examples, big data analytics and real-time IoT sensor-based decision making (individually) have been the front runner smart applications showcased by Smart City projects. However, their combination has seen limited integration of techniques from both fields. An example of overlapping areas is streaming analytics of real-time IoT sensors, which typically use big data tools at fine time granularity (e.g., Apache Spark and Storm) with lingering limitations in real-time guarantees and machine learning capabilities needed for smart applications. A concrete example of challenge applications is the automated real-time tracking of moving entities across networks of video cameras. Although such tracking is done routinely by humans, its automation requires significant advances in the integration of vision/machine learning tools (object recognition from video images) with streaming analytics (parallel processing of video images from related cameras).

The panel will discuss the overlapping areas among big data, IoT/CPS, and SCC/Smart Cities, with emphasis on the research opportunities and technical challenges.

Panel Moderator: Calton Pu

Calton Pu
Calton’s research interests are in the areas of service computing, distributed and cloud computing, integration and veracity of big data. His current projects include cloud computing (Elba) and big data (GRAIT-DM) research. Using experimental data from realistic benchmarks, the Elba project studies the interesting phenomena such as very short bottlenecks that have large impact on n-tier system response time. The GRAIT-DM project collects real world data from social sensors (e.g., Twitter and YouTube) and physical sensors (e.g., USGS GSN and NASA TRMM) to detect physical events and manage real-time information on them. The sponsors for Calton Pu’s research include both government funding agencies such as NSF, and companies from industry such as HP, Fujitsu, IBM, and Intel. He is a co-director of Center for Experimental Research in Computer Systems (CERCS), and affiliate faculty of Institute for Information Security and Privacy (IISP) at Georgia Tech. He is also the director of RCN on Big Data for Smart Cities, with collaborations around the world. Positions available: Georgia Tech is recruiting good graduate students.

Panelists

Karl Aberer
Karl Aberer received his PhD in mathematics in 1991 from the ETH Zürich. From 1991 to 1992 he was postdoctoral fellow at the International Computer Science Institute (ICSI) at the University of California, Berkeley. In 1992, he joined the Integrated Publication and Information Systems institute (IPSI) of GMD in Germany, where he was leading the research division Open Adaptive Information Management Systems. In 2000 he joined EPFL as full professor. Since 2005 he is the director of the Swiss National Research Center for Mobile Information and Communication Systems (NCCR-MICS, www.mics.ch). He is member of the editorial boards of VLDB Journal, ACM Transaction on Autonomous and Adaptive Systems and World Wide Web Journal. He has been consulting for the Swiss government in research and science policy as a member of the Swiss Research and Technology Council (SWTR) from 2003 – 2011.
Srinivas Aluru
Srinivas Aluru is a professor in the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology. He co-leads the Georgia Tech Strategic Initiative in Data Engineering and Science. He conducts research in high performance computing, bioinformatics and systems biology, combinatorial scientific computing, and applied algorithms. He pioneered the development of parallel methods in computational biology, and contributed to the assembly and analysis of complex plant genomes. His group is currently focused on developing bioinformatics methods for high-throughput DNA sequencing, particularly error correction and genome assembly. In systems biology, his group is working on network inference methods using mutual information and Bayesian approaches, and network analysis techniques to further the knowledge of partially characterized pathways. His contributions in scientific computing lie in parallel Fast Multipole Method, domain decomposition methods, spatial data structures, and applications in computational electromagnetics and materials informatics. Aluru is a Fellow of the American Association for the Advancement of Science (AAAS) and the Institute for Electrical and Electronic Engineers (IEEE). He is a recipient of the NSF Career award (1997), IBM faculty award (2002), and Swarnajayanti fellowship from the Government of India (2007). He serves on the editorial boards of the IEEE Transactions on Parallel and Distributed Systems, the Journal of Parallel and Distributed Computing, and the International Journal of Data Mining and Bioinformatics.
Ken Calvert
Ken Calvert is Division Director for Computer and Network Systems in the Computer and Information Science and Engineering (CISE) Directorate at the National Science Foundation. He is on rotation from the University of Kentucky, where he is Gartner Group Professor in Network Engineering in the Department of Computer Science. His research deals with the design and implementation of advanced network protocols and services, with particular interest in routing and incentives in future network architectures. He received his Ph.D. in computer science from the University of Texas at Austin. He holds a M.S. in computer science from Stanford University and a B.S. in computer science and engineering from the Massachusetts Institute of Technology. Prior to his appointment at the University of Kentucky, he was a Member of the Technical Staff at Bell Laboratories in Holmdel, NJ, and served on the faculty in the College of Computing at the Georgia Institute of Technology. He is an IEEE Fellow and a member of the ACM.
Manish Parashar
Manish Parashar is Distinguished Professor of Computer Science at Rutgers, The State University of New Jersey University. He is also the founding Director of the Rutgers Discovery Informatics Institute (RDI2) and The Applied Software Systems Laboratory (TASSL), Full Member (Clinical Investigations and Precision Therapeutics Program) of the Rutgers Cancer Institute of New Jersey, and is Associate Director at the Rutgers Center for Information Assurance (RUCIA). He also has a Joint Faculty Appointment with Oak Ridge National Laboratory (ORNL), and is Visiting Professor in the Faculty of Business, Computing & Law, University of Derby, UK. He co-founded and was Co-Director of the Cloud and Autonomic Computing Center (CAC) NSF IUCRC at Rutgers (CAC@Rutgers) between 2008 and 2013.At Rutgers, he led (with Prof. H. Berman) the strategic planning efforts in Research Computing and served as the Interim Associate Vice President of Research Computing between 2015 – 2016 to oversee the establishment of the Rutgers Office of Advanced Research Computing (OARC). He is also currently the Lead PI for Cyberinfrastructure for the NSF Ocean Observatories Initiative.