=Paper=
{{Paper
|id=Vol-1830/Paper79
|storemode=property
|title=Big Data: A Computing Model for Knowledge Extraction on Insurgency Management
|pdfUrl=https://ceur-ws.org/Vol-1830/Paper79.pdf
|volume=Vol-1830
|authors=Adeniji Oluwashola David
}}
==Big Data: A Computing Model for Knowledge Extraction on Insurgency Management==
https://ceur-ws.org/Vol-1830/Paper79.pdf
International Conference on Information and Communication Technology and Its Applications
(ICTA 2016)
Federal University of Technology, Minna, Nigeria
November 28 – 30, 2016
Big Data: A Computing Model for Knowledge Extraction on Insurgency
Management
Adeniji Oluwashola David
Department of Computer Science, University of Ibadan, Ibadan, Nigeria
od.adeniji@mail.ui.edu.ng
Abstract—The study investigates applications of big-data in Traditional data analytics is the traditional method of
Africa using Boko Haram and Al shabaab insurgency and managing structured data which includes a relational
their effect in the society. A framework on Big Data was database and schema to manage the storage and retrieval of
developed to extract knowledge based on the activities of the the dataset. This paper is organized as follows: Section 2
insurgency. The crucial information that concerns the activities explains the Component of Big Data and insurgency. In
of insurgent majorly cannot be easily access due to inaccurate section 3 Computing models and knowledge extraction for
data set. The research employs traditional computing models the model was described while in section 4 discussion of
that were used to limit volume, velocity and variety as the result was provided. The conclusion of paper was made in
major component of Big Data. The analysis of the frame work
Section 5.
are based on different methods of attacks employed by these
terrorists, development of sophisticated apparatus or
technology, the relative increase in the spate of attack, and II. COMPONENT OF BIG DATA
success rate in terms of causalities recorded. Big data requires The Complexity of handling data-set in Big Data using
exceptional technologies to efficiently process large quantities on-hand management tools was describe by [1]. The study
of data within tolerable elapsed time. The knowledge based it assessment on traditional way of processing data.
extraction in this research is to analyse the data sets by However Big data can be analyzed with common software
presenting a methodology from the application of big data to tools which can predict, mine data, analyze text and provide
solve and offer analytical solutions using an integrated Boko statistical analysis as discussed by [2]. The data set in big
Haram and Al shabaab data set.
data are faced with challenges such as capturing, storing,
Keywords: computing models; insurgency; knowledge
searching, sharing and visualization. The three major indices
extraction; component of big data for characterizing Big Data are Volume, Velocity and
Variety. Although these three indices made Big Data to be
different from the traditional way of analyzing data. Variety
I. INTRODUCTION
manages the complexity of multiply data types which vary
The Concept of Big Data has led to the variance in the from structure and unstructured data. Formatted, modeled
deployment of different technologies. The deployment and organized data are refer to structured data while emails,
variance in technology will provide data-set for extraction. audio, video and images are unstructured data. Velocity is
Big Data can be defined as collection of data-set from the major characteristic of big data. Velocity provides the
various sources which are analyzed when certain invent took speed of generation of data or how fast the data is generated
placed. The occurrence of these invent are associated with and processed in order to meet specific task. Volume refers
data at rest and data in motion. The interaction and activities to the quantity of data that is generated. There are some other
of people, process and data will provide real time data set for characteristic of big data such as variability, veracity, value
knowledge extraction and discovery. The investigation of and complexity.
“Boko Haram” and “Al-shabaab” in this study is timely due
to the challenges faced by Nigeria and Somalia. Insurgencies
A. Insurgency
are resources that need to be managed. The effect of
insurgencies management cannot be emphases and their Basically the act of rebellion directly or indirectly is
implication on investments such as Schools, Churches referred to insurgency [3]. The ideal definition of
Mosque and Health care centers are prime indexes for insurgencies can be ambiguous depending on the usage [4].
sustainable development of any Nation. The core interest in The review by some researcher considers insurgency has
this research is to develop an information system basically association that is illegitimate or not lawful by the law of the
on Boko Haram (Nigeria) and Al-shabaab (Somalia) land. The act that is illicit is called Terrorism but it is more
insurgence in Africa that will contain timely data set than mere criminality [5]. Terrorism is the basic part of the
collected from several sources which will give opportunities first part of the three phases of revolutionary warfare. Boko
for new data discovery, value creation and important Haram is an indigenous Salafist group which turned to a
decision making on how to curb insurgencies in Africa. Salafist Jihadist group in 2009 while al-Shabaab stems from
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
a Salafi- Wahhabi strand of Sunni Islam, Muhyadin [6]. The could be deduce that a specific location in the region was
information that was gathered in the militant salafi group massively attacked that lead high destruction of life and
1990 shows that the forerunner of al-Shabab, was Al-Ittihad properties. From the table l also there could be more of
Al-Islami (AIAI, or “Unity of Islam”).The fall of the Siad Bombing and explosion, likewise the data that was extracted
Barre military regime (1969-1991) during the outbreak of on the pie chart shows 59.3% terror attack in 2014. In 2009
civil war. The internet was used by Boko Haram and al- the terror attacks was 4.7%, 2010 was 0.5%, 2012 was
Shabaab to catalyze and promote their ideology in order to 11.8%, 2013 was 19.9% while in 2015 there was a drop of
celebrate their martyrs. These two group communicate with 0.1% in the attack. The justification from the data gathered
their audience through the internet using blog and email. shows that, number of attacks increases with years showing
They maintain an email at; nigjihadist@yahoo.com to chronicles of cases of terror attacks.
communicate with intending members.
They propagate Boko Haram and their interactions with
the Western World is forbidden [7]. The information V. CONCLUSION
gathered shows that their interaction are against the Muslim
The crucial information that concerns the activities of
establishment and the government of Nigeria [8]. Their
Boko Haram and Al shabaab are vulnerable. Big Data
agenda was to accuse the government of political corruption
technology will assist the government to extract information
and weak judicial structure.
and provide solution on how to eradicate the insurgent.
When developing the application of big data for analysis.
Different methods of attacks were employed and deployed
III. MATERIALS AND METHODS which clearly revealed the success rate in terms of causalities
The computing model is made up of Use case model, recorded. However the analysis shows that the fatalities in
Data flow Diagram and E-R diagram. The UML diagrams attack periodically increase and suddenly dropped. The
was used to specify the functionality and non-functionality of applications of big-data analysis have high effect in the
the system. The process in the modeling is inherently society because of the numerous attacks the society is facing.
iterative. Figure 1 shows level 0, data flow diagram.
The system requirement in the computing model
developed consists of program and database which can adapt VI. ACKNOWLEDGMENT
to modification. The operational approach of the model
deployed consists of parallel change over, direct change over The author wish to thank the Department of Computer
and pilot/phased change over. The system was tested to Science, University of Ibadan for support in this research
ensure validation and invalidation data in order to ascertain work.
the correctness, effectiveness and efficiency of the system,
[9]. This is to evaluate that the complete, integrated system
complies with its specified requirement. REFERENCES
[1] R. GUTH, “Deriving Intelligence from Big Data in Hadoop: A Big
Data Analytics Primer [Z],” Karmasphere.com, 2013
IV. RESULT AND DISCUSSION [2] R. Magoulas and B. Lorica, “Big data: Technologies and techniques
Knowledge extraction will allow users to view, sort or for large scale data,” Jimmy Gutterman, Nov 2009.
search by year, country or location of the information [3] B. Fall, “The Theory and Practice of Insurgency and
Counterinsurgency”, Naval War College Review, 17, 2010.
needed. The forms that were design provide information that
is sent to the database. It also prevents duplication of records [4] J. Bughin, M. Chui, and J. Manyika “Clouds, big data, and smart
assets,” McKinsey Quarterly, 1, August 2010.
in the database by the administrator. The extraction could be
[5] J. Brock, “Nigerian Islamist sect claims bomb attack,” 17( 06), 2011
data of terror attacks plotted against fatalities by year of
[6] M. A. Roble. “Al-Shabaab Razes Somali Forests To Finance Jihad,”
attack. Figure 2 presents the line chart of the statistical data Terrorism Monitor, vol 8, issue 42, Nov 2010.
of terror attacks, Figure 3 presents pie chart of the statistical [7] A. Murtada, “Boko Haram: Its Beginnings, Principles and Activities
data of terror attacks, and Figure 4 presents bar chart in Nigeria,”May, 2014
statistical data of terror attacks. [8] J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh,
The extraction of knowledge from the figures can be and A. H. Byers, “Big data: The next frontier for innovation,
summarized in table I. competition, and productivity,” May 2011.
The analysis presented in the table above provide the [9] E. G. Anderson, “A Proof-of-Concept Model for Evaluating
nature and type of attack .The rate of fatalities on the line Insurgency Management Policies Using the System Dynamics
chart increases from 2009 to 2014 and suddenly dropped at Methodology.” Strategic Insights, vol VI, issue 5, August 2007.
2015. Consider 9.5k on the line chart in 2014 terror attack, it
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
Figure 1. Shows Level 0, Data Flow Diagram
Figure 2. Line chart of the statistical data of terror attacks
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International Conference on Information and Communication Technology and Its Applications (ICTA 2016)
Figure 3. Pie chart of the statistical data of terror attacks
Figure 4. Bar chart statistical data of terror attacks
TABLE I. ANALYSIS SUMMARY OF THE FRAMEWORK BASED ON METHOD OF ATTACK, TECHNOLOGY USED, INCREASE IN ATTACK RATE AND SUCCESS
RATE 2009 TO 2015
Years of
2009 2010 2011 2012 2013 2014 2015
Terror
Line Chart 1k 0.8k 0.9k 2k 2.6k 9.5k 0.1k
Pie Chart 4.7% 0.5% 3.8% 11.8% 19.7% 59.3% 0. 1%
Bar Chart 800 300 700 1,800 3,000 8,880 100
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