An Introduction to Mobile Data Analytics.

1. MOBILE DATA
ANALYTICS
PRESENTED BY RICHARD
YAHUMA AMUOK
SCT312-D1-1676/2016

2. Introduction
 According to He, Yu, Zhao, Yin, Yao and
Qiu (2016) Mobile cellular networks have
become both generators and carriers of
massive data.
 When geo-locating mobile devices,
recording phone calls, and capturing
mobile applications activities, an
enormous amount of data is generated
and carried in mobile cellular networks.

3. Big Data Analysis
 Young (IEEE 2015), explains that big data
analysis is aimed at making sense of data
by applying efficient and scalable
algorithm on big data for analysis,
learning, modelling, visualization and
understanding.

4. Search for Knowledge
 The goal of analytics in business is to make
available relevant customer data such as
purchase preferences, purchase history, responses
to promotions, complaints as well as any possible
salient and persuasive data point.
 This Includes design of algorithms and systems to
integrate the data and uncover hidden values
from the data, algorithms for automatic or mixed
initiative knowledge delivery and learning, data
transformation and modelling, predictions and
explanation of the data.

5. Mobile Big Data
 Alsheikh, Niyato, Lin, Tan and Han (2016)
state that; mobile big data (MBD)is a
concept that describes a massive amount
of mobile data which cannot be
processed using a single machine.
 MBD contains useful information for
solving many problems such as fraud
detection, Marketing and targeted
advertising, context aware computing
and healthcare.

6. Motivation for Big Data
Analytics for Mobile data
 Data is now growing rapidly and new tools
are needed to handle the data within a
tolerable amount of time.
 Clients behavioural data is captured through
several devices and sensors, various social
interactions and communication.
 Decision making is moving from relatively ad-
hoc and subjective to evidence-based.

7. Motivation for Big Data
Analytics for Mobile data
 Inferring knowledge from complex
heterogeneous client sources and
leveraging the client/data correlations in
longitudinal records.
 Understanding unstructured notes in the
right context
 Efficiently handling large volumes of data
and extracting useful information.

8. Big Data Infrastructure And
Tools
In order to collect, ingest and analyse
increasingly complex and quickly multiplying
data sets requires thorough analytical platforms.
 Hadoop - is a free, Java-based programming
framework that supports the processing of
large data sets in a distributed computing
environment.
 Hadoop distributed File systems HDFS - a fault-
tolerant file system designed to run on
commodity hardware.

9. Infrastructure and tools
 Spark - is a scalable open source Hadoop
execution engine designed for fast and
flexible analysis of large multiple format
data sets—with an emphasis on the word
“fast”.
 Spark runs programs 100 times faster in
memory and 10 times faster for complex
applications running on disk compared to
other tools.

10. Infrastructure and tools
 Storm is a stream processing framework that
also does micro-batching . It is focused on
stream processing or what some call complex
event processing.
 Hadoop MapReduce - a software framework
for easily writing applications which process
vast amounts of data (multi-terabyte data-
sets) and allows for massive scalability across
hundreds or thousands of servers in a Hadoop
cluster.

11. Infrastructure and tools
 Deep learning (also known as deep
structured learning, hierarchical learning
or deep machine learning) is a branch of
machine learning based on a set of
algorithms that attempt to model high-
level abstractions in data by using a deep
graph with multiple processing layers,
composed of multiple linear and non-
linear transformations.

12. MBD Infrastructure and Tools
 A combination of the above tools and
platforms with HTML5 mobile applications
accessible via tablets and smartphones
by workers is also helping a lot in MBD.

13. Challenges of Big data
Analytics on Mobile Devices
 Limited scalability of users and devices.
 Limited availability of software
applications.
 Resources scarceness in embedded
gadgets.
 Frequent disconnection.
 Finite energy of mobile devices.

14. Mobile Cloud Computing
 According to Youssef (2014), the reason
for increasing usage of mobile computing
is its ability to provide a tool for the user
when and where it is needed regardless
of user movement thereby supporting
location independence.
 Cloud computing provides an attractive
platform to cut down costs of systems
ownership and maintenance burdens.

15. Cloud Deployment Models
 Private Cloud
 Public Cloud
 Hybrid Cloud
 Community Cloud
 Distributed Cloud
 Intercloud
 Multicloud

16. Cloud Service Models
 Infrastructure as a service – IaaS
 Platform as a service – PaaS
 Software as a service – SaaS
 Mobile backend as a Service – MBaaS
 Server less computing

17. How Cloud Solves Problems
 Offloads data and computation to a
remote resource provider which is the
internet. Devices don’t have to host the
execution of users application and storing
of users data.
 By exploiting resource pooling of the
cloud, mobile intensive applications can
be executed on low resource and limited
energy mobile devices.

18. How Cloud Solves Problems
 Broad access network of the cloud
overcomes the limited availability and
frequent disconnection problems since
cloud resources are available anywhere
at any time.
 Cloud infrastructure is very scalable.
Providers can add new nodes and servers
to the cloud with minor modifications to
cloud infrastructure.

19. Security Issues
Security is top priority in cloud based systems and
data is protected through various means including:
 Comprehensive physical security
 Data encryption
 User authentication to verify user identity
 Authorization to grant specific user privileges
 Non repudiation
 Integrity and Confidentiality
 Availability by protecting from denial of service
attacks.

20. conclusion
 In order to cope with the increased demand on
scalable and adaptive mobile systems , time
efficient mobile big data analytics is required. The
cloud makes it easy to share and utilize resources
by users who have authorization to do so. Big data
analytics helps in data analysis to provide the
correct feedback on a subject.
 A framework that supports cloud based services
and big data analytics should have the correct set
of security and control mechanisms that will offer
integrity, confidentiality and privacy.

21. References
 Alsheikh, M. A., Niyaro, D., Lin, S., Tan, H., and Han, Z.
(2016)Mobile Data Analytics Using Deep Learning
Apache Spark. arXiv:1602.0703v1.
 Young, Q. 2015)Introduction to the IEEE Transactions on
Big Data. IEEE Transactions on Big Data,1(1).
 Ying, H., Yu, F.R., Zhao, N., Yin, H., Yao, H.,and Qiu, R. C.
(2016)Big Data Analytics in Mobile Cellular Networks.
10.1109/ACCFSS.2016.2540920
 Youssef, A. E. (2014) A Framework For Secure Healthcare
SystemsBased on Big Data Analytics in Mobile Cloud
Computing Environments. International Journal of
Ambient Systems and Applications,2(2).