The 6th IEEE International Conference on Big Data and Cloud Computing (BDCloud 2016)

October 8th - 10th, Atlanta, GA

Keynote Speakers

October 8, 2016 9:00-10:00am Keynote 1

Perspectives on Smart and Connected Communities and Cyber-Physical Systems

Dr. Gurdip Singh Dr. Gurdip Singh

Development of Smart and Connected Communities (SCC) will require novel approaches to design reliable and robust infrastructure systems. In addition, to provide resilient services, the interactions and interdependence of infrastructure systems in different domains (e.g., energy, transportation, and public health) must be addressed. This is also resulting in accumulation of large amounts of data, which can be analyzed, interpreted, and appropriately leveraged. When multiple systems are interacting with each other, and closed-loop control is implemented, real-time analysis of the large amount of cross-device data becomes a critical requirement. A number of programs at NSF such as the Cyber-Physical Systems program, the Critical Resilient Interdependent Infrastructure Systems and Processes program, and the Partnership for Innovation: Broadening Innovation Capacity program are supporting the development of technologies to support SCC and Big-Data analytics in real-time. In this presentation, we provide an overview of these programs, and focus on their multidisciplinary nature. We will discuss synergies between these programs, and provide perspectives on techniques for reliable and robust software infrastructure systems.

Biography: Dr. Gurdip Singh is the Associate Dean for Research and Doctoral Programs at Syracuse University. He is also an Expert in the Division of Computer and Network Systems in the CISE Directorate at National Science Foundation and was a Program Director in the same division from 2014 to 2016. His program management duties include the following programs: Cyber-Physical Systems, Computer Systems Research, Critical Resilient Interdependent Infrastructure Systems and Processes, Partnership for Innovation, and Research Coordination Networks. From 2009 and 2014, he was the Head of Computer Science Department at Kansas State University. His research interests include real-time embedded systems, sensor networks, network protocols and distributed computing. His research has been funded by NSF, ARO, DARPA and Lockheed Martin. He has been involved in developing software tools to design large-scale, distributed safety critical systems.

October 9, 2016 9:00-10:00am Keynote 2

Searchable Symmetric Encryption: Potential Attacks, Practical Constructions and Extensions

Dr. Jinjun Chen Dr. Jinjun Chen

Data outsourcing has become one of the most successful applications of cloud computing, as it significantly reduces data owners' costs on data storage and management. To prevent privacy disclosure, sensitive data has to be encrypted before outsourcing. Traditional encryption tools such as AES, however, destroy the data searchability because keyword searches cannot be performed over encrypted data. Though the above issue has been addressed by an advanced cryptographic primitive, called searchable symmetric encryption (SSE), we observe that existing SSE schemes still suffer security, efficiency or functionality flaws. In this research, we further study SSE on three aspects. Firstly, we address the search pattern leakage issue. We demonstrate that potential attacks are exist as long as an adversary with some background knowledge learns users' search pattern. We then develop a general countermeasure to transform an existing SSE scheme to a new scheme where the search pattern is hidden. Secondly, motivated by the practical phenomenon in data outsourcing scenarios that user data is distributed over multiple data sources, we propose efficient SSE constructions which allow each data source to build a local index individually and enable the storage provider to merge all local indexes into a global one. Thirdly, we extend SSE into graph encryption with support for specific graph queries. E.g., we investigate how to perform shortest distance queries on an encrypted graph.

Biography: Dr. Jinjun Chen is a Professor from Faculty of Engineering and IT, University of Technology Sydney (UTS), Australia. He is the Director of Lab for Data Systems and Visual Analytics in the Global Big Data Technologies Centre at UTS. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include scalability, big data, data science, data intensive systems, cloud computing, workflow management, privacy and security, and related various research topics. His research results have been published in more than 130 papers in international journals and conferences, including ACM Transactions on Software Engineering and Methodology (TOSEM), IEEE Transactions on Software Engineering (TSE), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Transactions on Cloud Computing, IEEE Transactions on Computers (TC), IEEE Transactions on Service Computing, and IEEE TKDE.
He received UTS Vice-Chancellor's Awards for Research Excellence Highly Commended (2014), UTS Vice-Chancellor's Awards for Research Excellence Finalist (2013), Swinburne Vice-Chancellor's Research Award (ECR) (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011), IEEE Computer Society Service Award (2007), Swinburne Faculty of ICT Research Thesis Excellence Award (2007). He is an Associate Editor for ACM Computing Surveys, IEEE Transactions on Big Data, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, as well as other journals such as Journal of Computer and System Sciences, JNCA. He is the Chair of IEEE Computer Society's Technical Committee on Scalable Computing (TCSC), Vice Chair of Steering Committee of Australasian Symposium on Parallel and Distributed Computing, Founder and Coordinator of IEEE TCSC Technical Area on Big Data and MapReduce, Founder and Steering Committee Co-Chair of IEEE International Conference on Big Data and Cloud Computing, IEEE International Conference on Big Data Science and Engineering, and IEEE International Conference on Data Science and Systems.

October 10, 2016 9:00-10:00am Keynote 3

The Era of Big Spatial Data

Dr. Mohamed F. Mokbel Dr. Mohamed F. Mokbel

In recent years, there has been an explosion in the amounts of spatial and spatio-temporal data produced from several devices including smart phones, space telescopes, medical devices. Unfortunately, managing and analyzing such big spatial data is hampered by the lack of specialized systems, techniques, and algorithms. While big data is well supported with a variety of distributed systems and cloud infrastructure, none of these systems or infrastructure provide any special support for spatial or spatio-temporal data. This talk presents our efforts in indexing, querying, and visualizing big spatial and spatio-temporal data. We will describe our efforts within SpatialHadoop; our full-fledged MapReduce framework with native support for spatial data, including support for basic spatial operations, computational geometry, and spatial visualization.

Biography: Mohamed F. Mokbel (Ph.D., Purdue University, MS, B.Sc., Alexandria University) is Associate Professor in the Department of Computer Science and Engineering, University of Minnesota. His research interests include the interaction of GIS and location-based services with database systems and cloud computing. His research work has been recognized by the VLDB 10-Years Best Paper Award, five Best Paper Awards, and by the NSF CAREER award. Mohamed is/was the program co-chair for ACM SIGMOD 2018, ACM SIGSPATIAL GIS from 2008 to 2010, and IEEE MDM Conference 2011 and 2014, and the General Chair for SSTD 2011. He is an Associate Editor for ACM TODS, ACM TSAS, VLDB journal, and GeoInformatica. Mohamed is an elected Chair of ACM SIGSPATIAL 2014-2017. For more information, please visit: