10th International Conference on Innovations in Information Technology
November 9-11, 2014. Al Ain, UAE
Technically Co-Sponsored by:



Tutorial I:
Title: Cloud Computing and Big Data Analytics for Teaching and Research

 Qusay H. Mahmoud, Ph.D.

Professor and Chair

Department of Electrical Computer and Software Engineering

University of Ontario Institute of Technology

Oshawa, ON, Canada

Email: Qusay.mahmoud@uoit.ca

Web: http://faculty.uoit.ca/mahmoud


Cloud computing is a model for enabling convenient on-demand network access to a shared pool of configurable computing resources (servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [NIST]. This model is made possible by advances in technologies for virtualization and data centers. Cloud computing provides three service model architectures: (1) software as a service (SaaS) enabling consumers to use applications and services (e.g. dropbox) running on cloud infrastructures; (2) platform as a service (PaaS) that allow consumers to deploy applications and services on platforms such as Heroku, Google App Engine, and Microsoft Azure that are running on the cloud infrastructure; and (3) infrastructure as a service (IaaS), such as Amazon Web Services, Rackspace, and VMWare, that allow consumers to provision processing and storage in the cloud. IaaS supports storage and computational needs for big data – a term used to describe data sets that are too large and complex to be manipulated by standard methods and tools. This includes user data generated from social networks, data from scientific experiments, government data, or data generated from sensors. This tutorial will help participants understand the opportunities and challenges in cloud computing and big data analytics for teaching and research. They will learn about cloud computing principles and service architectures and some of the most widely used cloud providers and why; see examples of cloud computing applications and services; get a flavor of the effort involved in developing cloud computing apps; learn about the different technologies that can be used for big data analytics, such as Apache Hadoop and the Berkeley Data Analytics Stack. A tutorial on this emerging topic wouldn’t be complete with a discussion of the security and privacy issues, which will also be discussed.


Qusay H. Mahmoud is a Professor of Software Engineering and Chair of the Department of Electrical, Computer and Software Engineering at the University of Ontario Institute of Technology. He holds a Ph.D. in Computer Science from Middlesex University (UK), and an M.Sc. in Computer Science and a B.Sc. in Data Analysis, both from the University of New Brunswick in Canada. Qusay is the author of two books: Distributed Programming with Java (Manning Publications, 1999) and Learning Wireless Java (O’Reilly, 2002). He is a licensed Professional Engineer (P.Eng.) in Ontario, and a Senior Member of the IEEE. Qusay has presented numerous workshops and tutorials on mobile application development. Qusay has led the development of the CMER Academic Kit (http://www.cmer.ca/kit.html) that contains instructor and student resources for developing state-of-the-art mobile applications for a variety of mobile devices. Qusay’s research interests include software engineering in the cloud and mobile systems.

Tutorial II:
Title:Bigdata Systems: NOSQL vs NEWSQL

Jawwad Shamsi, Ph.D.  

Head of the Department of Computer Science, FAST-NU

Email: jawwad.shamsi@nu.edu.pk

Web: https://sites.google.com/a/nu.edu.pk/jawwadshamsi/



Big Data systems encompass terabytes to petabytes of data. Such systems require massive storage and intensive computational power in order to execute complex queries and generate timely results. The growth of the Internet has induced massive requirements and potential related to applications and usage of Big Data systems. Further, the rate at which this data is being generated induces extensive challenges of data storage, linkage, and processing. The four V’s of Big Data, i.e., Velocity, Variety, Volume, and Value provide new dimensions for exploration and understanding. The purpose of this tutorial is to impart different architectural and framework-related requirements and advancements of Big Data systems. Over the years, evolution of Big Data systems has been governed by market needs. Challenges related to compliance with ACID properties and meeting scalability requirements and providing support for real-time querying and transactional operations have seen progression from NOSQL frameworks to NEWSQL systems. The tutorial aims to cover a thorough explanation of these requirements and expectations in order to differentiate between NEWSQL and NOSQL systems. The tutorial will also cover hands on demonstration of NOSQL and NEWSQL Systems. In that, Amazon AWS will be utilized to teach MapReduce (NOSQL) and VoltDB (NEWSQL).


Jawwad A. Shamsi earned a PhD. in Computer Science from Wayne State University in 2009. He is currently an Associate Professor and Head of the Computer Science Department at FAST National University of Computer and Emerging Sciences. His research interest lies in distributed systems, cloud computing, high performance computing, and network security.

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