Dr. Sherif Sakr is currently a Professor of Computer Science at King Saud bin Abdulaziz University for Health Sciences. He is also affiliated with The School of Computer Science and Engineering (CSE) at University of New South Wales (UNSW Australia) and Data61/CSIRO (formerly NICTA). He received his PhD degree in Computer and Information Science from Konstanz University, Germany in 2007. He received his BSc and MSc degrees in Computer Science from the Information Systems department at the Faculty of Computers and Information in Cairo University, Egypt, in 2000 and 2003 respectively. In 2008 and 2009, Sherif held an Adjunct Lecturer position at the Department of Computing of Macquarie University, Australia. Dr. Sakr held a visiting Researcher/Professor appointments in international reputable research and academic institutes including Microsoft Research, Redmond, USA (2011), Nokia Bell Labs, Ireland – Formerly Alcatel-Lucent Bell Labs (2012), Humboldt-Universität zu Berlin, Germany (2015), University of Zurich, Switzerland (2016), Technical University of Dresden, Germany (2016). In 2013, Sherif has been awarded the Stanford Innovation and Entrepreneurship Certificate. Dr. Sakr’s research interest is data and information management in general, particularly in big data processing systems, big data analytics, data science and big data management in cloud computing platforms. He is an associate editor of the cluster computing journal and Transactions on Large-Scale Data and Knowledge-Centered Systems (TLDKS). He is also an editorial board member of many reputable international journals. Dr. Sakr is an IEEE Senior Member.
For a decade, the MapReduce framework, and its open source realization, Hadoop, has emerged as a highly successful framework that has created a lot of momentum such that it has become the defacto standard of big data processing platforms. However, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and big data processing scenarios such as large scale processing of structured data, graph data and streaming data. Thus, we have witnessed an unprecedented interest to tackle these challenges with new solutions which constituted a new wave of mostly domain-specific, optimized big data processing engines. In this talk, we refer to this new wave of systems as “Big Data 2.0 processing systems”. We provide a taxonomy and analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.
Large Scale Graph Processing Systems
Recently, people, devices, processes, and other entities have been more connected than at any other point in history. In general, a graph is a natural, neat, and flexible structure to model the complex relationships, interactions, and interdependencies between objects. Graphs have been widely used to represent datasets in a wide range of application domains such as social science, astronomy, computational biology, telecommunications, computer networks, and many others. The ever-increasing size of graph-structured data for these applications creates a critical need for scalable systems that can process large amounts of it efficiently. In practice, graph analytics is an important and effective Big Data discovery tool. In this talk, we provide a taxonomy and analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.
Big Data Science as a Service
Recently, big data science has emerged as a modern and important data analysis discipline. It is considered as an amalgamation of classical disciplines such as statistics, artificial intelligence and computer science with its sub-disciplines including database systems, machine learning and distributed systems. It combines existing approaches with the aim of turning abundantly available data into value for. individuals, organizations, and society. Cloud computing represents a practical and cost-effective solution for supporting Big Data storage, processing and for sophisticated analytics applications. We analyze in details the building blocks of the software stack for supporting and democratizing big data science as a commodity service for data scientists. We provide various insights about the latest ongoing developments and open challenges in this domain.
Data Analytics for Healthcare Services
The last decade has seen huge advances in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to use technology to analyze and understand it. The intersection of these trends is what we call Big Data Science and it is currently helping in every domain to become more productive and efficient. In the era of Big Data, healthcare industry is being challenged to develop better techniques, skills and tools to deal competently with the flood of patient data and its inherent insights. Modern data science technologies can play an effective role to tackle this challenge and change the future for improving our lives. This talk introduces the role that the new trend of Big Data science can play in the health care domain and discusses the main aspects of this trend including its benefits, applications and various opportunities in the healthcare domain. Methods and technology progress of Big Data Science are also overviewed in this talk. Case studies are presented. The challenges of Big Data Science in medical applications and health care are discussed as well.