TUTORIALS
Tutorial I: |
Title: Cloud Computing
and Big Data Analytics for Teaching and Research
http://faculty.uoit.ca/mahmoud/iit2014tutorial.html
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Presenter: |

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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
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Abstract |
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.
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Biography |
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.
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Tutorial II: |
Title:Bigdata Systems:
NOSQL vs NEWSQL |
Presenters: |

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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/
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Abstract |
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).
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Biography |
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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