Big data analytics proceedings of CSI 2015

Advances in Intelligent Systems and Computing 654

V.B. Aggarwal
Vasudha Bhatnagar
Durgesh Kumar Mishra Editors

Big Data
Analytics
Proceedings of CSI 2015


Advances in Intelligent Systems and Computing
Volume 654

Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail:


About this Series
The series “Advances in Intelligent Systems and Computing” contains publications on theory,
applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually
all disciplines such as engineering, natural sciences, computer and information science, ICT,
economics, business, e-commerce, environment, healthcare, life science are covered. The list
of topics spans all the areas of modern intelligent systems and computing.
The publications within “Advances in Intelligent Systems and Computing” are primarily
textbooks and proceedings of important conferences, symposia and congresses. They cover
significant recent developments in the field, both of a foundational and applicable character.
An important characteristic feature of the series is the short publication time and world-wide
distribution. This permits a rapid and broad dissemination of research results.

Advisory Board
Chairman
Nikhil R. Pal, Indian Statistical Institute, Kolkata, India
e-mail:
Members
Rafael Bello Perez, Universidad Central “Marta Abreu” de Las Villas, Santa Clara, Cuba
e-mail:
Emilio S. Corchado, University of Salamanca, Salamanca, Spain
e-mail:
Hani Hagras, University of Essex, Colchester, UK
e-mail:
László T. Kóczy, Széchenyi István University, Győr, Hungary
e-mail:
Vladik Kreinovich, University of Texas at El Paso, El Paso, USA
e-mail:
Chin-Teng Lin, National Chiao Tung University, Hsinchu, Taiwan
e-mail:
Jie Lu, University of Technology, Sydney, Australia
e-mail:
Patricia Melin, Tijuana Institute of Technology, Tijuana, Mexico
e-mail:
Nadia Nedjah, State University of Rio de Janeiro, Rio de Janeiro, Brazil
e-mail:
Ngoc Thanh Nguyen, Wroclaw University of Technology, Wroclaw, Poland
e-mail:
Jun Wang, The Chinese University of Hong Kong, Shatin, Hong Kong
e-mail:

More information about this series at />

V.B. Aggarwal Vasudha Bhatnagar
Durgesh Kumar Mishra
•

Editors

Big Data Analytics
Proceedings of CSI 2015

123


Editors
V.B. Aggarwal
Jagan Institute of Management Studies
New Delhi, Delhi
India

Durgesh Kumar Mishra
Microsoft Innovation Centre
Sri Aurobindo Institute of Technology
Indore, Madhya Pradesh
India

Vasudha Bhatnagar
Department of Computer Science
University of Delhi
New Delhi, Delhi
India

ISSN 2194-5357
ISSN 2194-5365 (electronic)
Advances in Intelligent Systems and Computing
ISBN 978-981-10-6619-1
ISBN 978-981-10-6620-7 (eBook)
/>Library of Congress Control Number: 2017952513
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Preface

The last decade has witnessed remarkable changes in IT industry, virtually in all
domains. The 50th Annual Convention, CSI-2015, on the theme “Digital Life” was

organized as a part of CSI-2015, by CSI at Delhi, the national capital of the country,
during December 02–05, 2015. Its concept was formed with an objective to keep
ICT community abreast of emerging paradigms in the areas of computing technologies and more importantly looking at its impact on the society.
Information and Communication Technology (ICT) comprises of three main
components: infrastructure, services, and product. These components include the
Internet, infrastructure-based/infrastructure-less wireless networks, mobile terminals, and other communication mediums. ICT is gaining popularity due to rapid
growth in communication capabilities for real-time-based applications. New user
requirements and services entail mechanisms for enabling systems to intelligently
process speech- and language-based input from human users. CSI-2015 attracted
over 1500 papers from researchers and practitioners from academia, industry and
government agencies, from all over of the world, thereby making the job of the
Programme Committee extremely difficult. After a series of tough review exercises
by a team of over 700 experts, 565 papers were accepted for presentation in
CSI-2015 during the 3 days of the convention under ten parallel tracks. The
Programme Committee, in consultation with Springer, the world’s largest publisher
of scientific documents, decided to publish the proceedings of the presented papers,
after the convention, in ten topical volumes, under ASIC series of the Springer, as
detailed hereunder:
1.
2.
3.
4.

Volume
Volume
Volume
Volume

#
#

#
#

1:
2:
3:
4:

ICT Based Innovations
Next Generation Networks
Nature Inspired Computing
Speech and Language Processing for Human-Machine
Communications
5. Volume # 5: Sensors and Image Processing
6. Volume # 6: Big Data Analytics

v


vi

7.
8.
9.
10.

Preface

Volume
Volume

Volume
Volume

#
#
#
#

7: Systems and Architecture
8: Cyber Security
9: Software Engineering
10: Silicon Photonics and High Performance Computing

We are pleased to present before you the proceedings of the Volume # 6 on “Big
Data Analytics”. The title “Big Data Analytics” discusses the new models applied
for Big Data Analytics. It traces the different business interests in the field of Big
Data Analytics from the perspective of decision-makers. The title also evaluates the
uses of data analytics in understanding the need of customer base in various
organizations.
Big data is a new buzzword due to the generation of data from a diversity of
sources. The volume, variety and velocity of data coming into an organization from
both structured and unstructured data sources continue to reach unprecedented
levels. This phenomenal growth implies that one must not only understand the big
data in order to decipher the information that truly counts, but one must also
understand the possibilities and opportunities of data analytics.
Big data analytics is the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make
better decisions. With big data analytics, data scientists and others can analyse huge
volumes of data that conventional analytics and business intelligence solutions
cannot touch. The title “Big Data Analytics” analyses the different aspects of big
data research and how the same is being applied across organizations to handle their

data for decision-making and different types of analytics for different business
strategies.
This volume is designed to bring together researchers and practitioners from
academia and industry to focus on extending the understanding and establishing
new collaborations in these areas. It is the outcome of the hard work of the editorial
team, who have relentlessly worked with the authors and steered up the same to
compile this volume. It will be a useful source of reference for the future researchers
in this domain. Under the CSI-2015 umbrella, we received over 500 papers for this
volume, out of which 74 papers are being published, after a rigorous review processes, carried out in multiple cycles.
On behalf of organizing team, it is a matter of great pleasure that CSI-2015 has
received an overwhelming response from various professionals from across the
country. The organizers of CSI-2015 are thankful to the members of Advisory
Committee, Programme Committee and Organizing Committee for their all-round
guidance, encouragement and continuous support. We express our sincere gratitude
to the learned Keynote Speakers for support and help extended to make this event a
grand success. Our sincere thanks are also due to our Review Committee Members
and the Editorial Board for their untiring efforts in reviewing the manuscripts,
giving suggestions and valuable inputs for shaping this volume. We hope that all
the participants/delegates will be benefitted academically and wish them all the best
for their future endeavours.


Preface

vii

We also take the opportunity to thank the entire team from Springer, who have
worked tirelessly and made the publication of the volume a reality. Last but not
least, we thank the team from Bharati Vidyapeeth’s Institute of Computer
Applications and Management (BVICAM), New Delhi, for their untiring support,

without which the compilation of this huge volume would not have been possible.
New Delhi, India
New Delhi, India
Indore, India
March 2017

V.B. Aggarwal
Vasudha Bhatnagar
Durgesh Kumar Mishra


The Organization of CSI-2015

Chief Patron
Padmashree Dr. R. Chidambaram, Principal Scientific Advisor, Government of
India

Patrons
Prof. S.V. Raghavan, Department of Computer Science, IIT Madras, Chennai
Prof. Ashutosh Sharma, Secretary, Department of Science and Technology,
Ministry of Science and Technology, Government of India
Chair, Programme Committee
Prof. K.K. Aggarwal, Founder Vice Chancellor, GGSIP University, New Delhi
Secretary, Programme Committee
Prof. M.N. Hoda, Director, Bharati Vidyapeeth’s Institute of Computer
Applications and Management (BVICAM), New Delhi

Advisory Committee
• Padma Bhushan Dr. F.C. Kohli, Co-Founder, TCS
• Mr. Ravindra Nath, CMD, National Small Industries Corporation, New Delhi

• Dr. Omkar Rai, Director General, Software Technological Parks of India (STPI),
New Delhi
• Adv. Pavan Duggal, Noted Cyber Law Advocate, Supreme Courts of India
• Prof. Bipin Mehta, President, CSI
• Prof. Anirban Basu, Vice President—cum- President Elect, CSI
• Shri Sanjay Mohapatra, Secretary, CSI
• Prof. Yogesh Singh, Vice Chancellor, Delhi Technological University, Delhi
• Prof. S.K. Gupta, Department of Computer Science and Engineering, IIT, Delhi

ix


x

The Organization of CSI-2015

• Prof. P.B. Sharma, Founder Vice Chancellor, Delhi Technological University,
Delhi
• Mr. Prakash Kumar, IAS, Chief Executive Officer, Goods and Services Tax
Network (GSTN)
• Mr. R.S. Mani, Group Head, National Knowledge Networks (NKN), NIC,
Government of India, New Delhi

Editorial Board
•
•
•
•
•
•

•
•
•
•

A.K. Nayak, CSI
A.K. Saini, GGSIPU, New Delhi
R.K. Vyas, University of Delhi, Delhi
Shiv Kumar, CSI
Vishal Jain, BVICAM, New Delhi
S.S. Agrawal, KIIT, Gurgaon
Amita Dev, BPIBS, New Delhi
D.K. Lobiyal, JNU, New Delhi
Ritika Wason, BVICAM, New Delhi
Anupam Baliyan, BVICAM, New Delhi


Contents

Need for Developing Intelligent Interfaces for Big Data Analytics
in the Microfinance Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Purav Parikh and Pragya Singh
Unified Resource Descriptor over KAAS Framework . . . . . . . . . . . . . . .
Subhajit Bhattacharya
An Adaptable and Secure Intelligent Smart Card Framework
for Internet of Things and Cloud Computing . . . . . . . . . . . . . . . . . . . . . .
T. Daisy Premila Bai, A. Vimal Jerald and S. Albert Rabara
A Framework for Ontology Learning from Taxonomic Data . . . . . . . . .
Chandan Kumar Deb, Sudeep Marwaha, Alka Arora and Madhurima Das
Leveraging MapReduce with Column-Oriented Stores: Study

of Solutions and Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Narinder K. Seera and S. Taruna
Hadoop: Solution to Unstructured Data Handling . . . . . . . . . . . . . . . . . .
Aman Madaan, Vishal Sharma, Prince Pahwa, Prasenjit Das
and Chetan Sharma

1
7

19
29

39
47

Task-Based Load Balancing Algorithm by Efficient Utilization of VMs
in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ramandeep Kaur and Navtej Singh Ghumman

55

A Load Balancing Algorithm Based on Processing Capacities
of VMs in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Ramandeep Kaur and Navtej Singh Ghumman

63

Package-Based Approach for Load Balancing
in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Amanpreet Chawla and Navtej Singh Ghumman

71

xi


xii

Contents

Workload Prediction of E-business Websites on Cloud Using Different
Methods of ANN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Supreet Kaur Sahi and V.S. Dhaka

79

Data Security in Cloud-Based Analytics . . . . . . . . . . . . . . . . . . . . . . . . . .
Charru Hasti and Ashema Hasti

89

Ontology-Based Ranking in Search Engine . . . . . . . . . . . . . . . . . . . . . . . .
Rahul Bansal, Jyoti and Komal Kumar Bhatia

97

Hidden Data Extraction Using URL Templates Processing . . . . . . . . . . . 111
Babita Ahuja, Anuradha and Dimple Juneja
Automatic Generation of Ontology for Extracting Hidden
Web Pages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Manvi, Komal Kumar Bhatia and Ashutosh Dixit
Importance of SLA in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . 141
Angira Ghosh Chowdhury and Ajanta Das
A Survey on Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Mohammad Ubaidullah Bokhari, Qahtan Makki
and Yahya Kord Tamandani
Adapting and Reducing Cost in Cloud Paradigm (ARCCP) . . . . . . . . . . 165
Khushboo Tripathi and Dharmender Singh Kushwaha
Power Aware-Based Workflow Model of Grid Computing
Using Ant-Based Heuristic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
T. Sunil Kumar Reddy, Dasari Naga Raju, P. Ravi Kumar and S.R. Raj
Kumar
Image Categorization Using Improved Data Mining Technique . . . . . . . 185
Pinki Solanki and Girdhar Gopal
An Effective Hybrid Encryption Algorithm for Ensuring Cloud Data
Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
Vikas Goyal and Chander Kant
Big Data Analytics: Recent and Emerging Application in Services
Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Rajesh Math
An Analysis of Resource-Aware Adaptive Scheduling for HPC
Clusters with Hadoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
S. Rashmi and Anirban Basu
Analytical and Perspective Approach of Big Data in
Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Rekha Pal, Tanvi Anand and Sanjay Kumar Dubey


Contents

xiii

Implementation of CouchDBViews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Subita Kumari and Pankaj Gupta
Evolution of FOAF and SIOC in Semantic Web: A Survey . . . . . . . . . . 253
Gagandeep Singh Narula, Usha Yadav, Neelam Duhan and Vishal Jain
Classification of E-commerce Products Using RepTree and K-means
Hybrid Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
Neha Midha and Vikram Singh
A Study of Factors Affecting MapReduce Scheduling . . . . . . . . . . . . . . . 275
Manisha Gaur, Bhawna Minocha and Sunil Kumar Muttoo
Outlier Detection in Agriculture Domain: Application
and Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
Sonal Sharma and Rajni Jain
A Framework for Twitter Data Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . 297
Imran Khan, S.K. Naqvi, Mansaf Alam and S.N.A. Rizvi
Web Structure Mining Algorithms: A Survey . . . . . . . . . . . . . . . . . . . . . 305
Neha Tyagi and Santosh Kumar Gupta
Big Data Analytics via IoT with Cloud Service . . . . . . . . . . . . . . . . . . . . 319
Saritha Dittakavi, Goutham Bhamidipati and V. Siva Krishna Neelam
A Proposed Contextual Model for Big Data Analysis Using Advanced
Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
Manjula Ramannavar and Nandini S. Sidnal
Ranked Search Over Encrypted Cloud Data in Azure Using Secure
K-NN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341
Himaja Cheruku and P. Subhashini
DCI3 Model for Privacy Preserving in Big Data. . . . . . . . . . . . . . . . . . . . 351
Hemlata and Preeti Gulia
Study of Sentiment Analysis Using Hadoop . . . . . . . . . . . . . . . . . . . . . . . 363
Dipty Sharma

OPTIMA (OPinionated Tweet Implied Mining and Analysis) . . . . . . . . . 377
Ram Chatterjee and Monika Goyal
Mobile Agent Based MapReduce Framework for Big Data
Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
Umesh Kumar and Sapna Gambhir
Review of Parallel Apriori Algorithm on MapReduce Framework for
Performance Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
Ruchi Agarwal, Sunny Singh and Satvik Vats


xiv

Contents

A Novel Approach to Realize Internet of Intelligent Things . . . . . . . . . . 413
Vishal Mehta
An Innovative Approach of Web Page Ranking Using Hadoop- and
Map Reduce-Based Cloud Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 421
Dheeraj Malhotra, Monica Malhotra and O.P. Rishi
SAASQUAL: A Quality Model for Evaluating SaaS on the Cloud
Computing Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429
Dhanamma Jagli, Seema Purohit and N. Subhash Chandra
Scalable Aspect-Based Summarization in the Hadoop
Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439
Kalyanasundaram Krishnakumari and Elango Sivasankar
Parallel Mining of Frequent Itemsets from Memory-Mapped Files. . . . . 451
T. Anuradha
Handling Smurfing Through Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 459
Akshay Chadha and Preeti Kaur
A Novel Approach for Semantic Prefetching Using Semantic

Information and Semantic Association . . . . . . . . . . . . . . . . . . . . . . . . . . . 471
Sonia Setia, Jyoti and Neelam Duhan
Optimized Cost Model with Optimal Disk Usage for Cloud . . . . . . . . . . 481
Mayank Aggrawal, Nishant Kumar and Raj Kumar
Understanding Live Migration Techniques Intended for Resource
Interference Minimization in Virtualized Cloud Environment. . . . . . . . . 487
Tarannum Bloch, R. Sridaran and CSR Prashanth
Cloud Security Issues and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499
Dhaivat Dave, Nayana Meruliya, Tirth D. Gajjar, Grishma T. Ghoda,
Disha H. Parekh and R. Sridaran
A Novel Approach to Protect Cloud Environments Against DDOS
Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515
Nagaraju Kilari and R. Sridaran
An Approach for Workflow Scheduling in Cloud Using ACO . . . . . . . . 525
V Vinothina and R Sridaran
Data Type Identification and Extension Validator Framework Model
for Public Cloud Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533
D. Boopathy and M. Sundaresan
Robust Fuzzy Neuro system for Big Data Analytics . . . . . . . . . . . . . . . . . 543
Ritu Taneja and Deepti Gaur


Contents

xv

Deployment of Cloud Using Open-Source Virtualization: Study of VM
Migration Methods and Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553
Garima Rastogi, Satya Narayan, Gopal Krishan and Rama Sushil
Implementation of Category-Wise Focused Web Crawler . . . . . . . . . . . . 565

Jyoti Pruthi and Monika
MAYA: An Approach for Energy and Cost Optimization for Mobile
Cloud Computing Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575
Jitender Kumar and Amita Malik
Load Balancing in Cloud—A Systematic Review . . . . . . . . . . . . . . . . . . . 583
Veenita Kunwar, Neha Agarwal, Ajay Rana and J.P. Pandey
Cloud-Based Big Data Analytics—A Survey of Current Research and
Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595
Samiya Khan, Kashish Ara Shakil and Mansaf Alam
Fully Homomorphic Encryption Scheme with Probabilistic
Encryption Based on Euler’s Theorem and Application in Cloud
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605
Vinod Kumar, Rajendra Kumar, Santosh Kumar Pandey and Mansaf Alam
Big Data: Issues, Challenges, and Techniques in Business
Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613
Mudasir Ahmad Wani and Suraiya Jabin
Cloud Computing in Bioinformatics and Big Data Analytics: Current
Status and Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629
Kashish Ara Shakil and Mansaf Alam
Generalized Query Processing Mechanism in Cloud Database
Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641
Shweta Malhotra, Mohammad Najmud Doja, Bashir Alam
and Mansaf Alam
Deliberative Study of Security Issues in Cloud Computing . . . . . . . . . . . 649
Chandani Kathad and Tosal Bhalodia
An Overview of Optimized Computing Approach: Green Cloud
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659
Archana Gondalia, Rahul N. Vaza and Amit B. Parmar
A Literature Review of QoS with Load Balancing in Cloud Computing
Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667

Geeta and Shiva Prakash
WAMLB: Weighted Active Monitoring Load Balancing in Cloud
Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677
Aditya Narayan Singh and Shiva Prakash


xvi

Contents

Applications of Attribute-Based Encryption in Cloud Computing
Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 687
Vishnu Shankar and Karan Singh
Query Optimization: Issues and Challenges in Mining of Distributed
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693
Pramod Kumar Yadav and Sam Rizvi
Comprehensive Study of Cloud Computing and Related Security
Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 699
Manju Khari, Manoj kumar and Vaishali
Healthcare Data Analysis Using R and MongoDB . . . . . . . . . . . . . . . . . . 709
Sonia Saini and Shruti Kohli
Data Mining Tools and Techniques for Mining Software Repositories:
A Systematic Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 717
Tamanna Siddiqui and Ausaf Ahmad
SWOT Analysis of Cloud Computing Environment . . . . . . . . . . . . . . . . . 727
Sonal Dubey, Kritika Verma, M.A. Rizvi and Khaleel Ahmad
A Review on Quality of Service in Cloud Computing . . . . . . . . . . . . . . . 739
Geeta and Shiva Prakash
Association Rule Mining for Finding Admission Tendency
of Engineering Student with Pattern Growth Approach . . . . . . . . . . . . . 749

Rashmi V. Mane and V.R. Ghorpade
Integrated Effect of Nearest Neighbors and Distance Measures
in k-NN Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759
Rashmi Agrawal


About the Editors

Dr. V.B. Aggarwal from 1981 to 88 was the Founder Head, Department of
Computer Science, University of Delhi, India, where he introduced the 3-year
postgraduate (PG) programme, Master of Computer Applications (MCA), from
1982 to 1985. In 1973, he was awarded his Ph.D. by the University of Illinois,
Urbana, USA. He continued his research work in the areas of supercomputers and
array processors. In the USA, he taught for seven years as a faculty member at three
universities. As a life member of the Computer Society of India (CSI), he has held
various offices at the Delhi Chapter, including chapter vice-chairman and chairman,
since 1979. In February 2014, he received the prestigious “Chapter Patron Award
2013” for Delhi Chapter by the CSI Awards Committee. Dr. Aggarwal has authored
more than 18 Computer Publications, which are very popular among school students.
Prof. Vasudha Bhatnagar is a Professor at the Department of Computer Science,
University of Delhi, India. She is actively involved in research in the field of
knowledge discovery and data mining (KDD). Her broad area of interest is intelligent data analysis. She is particularly interested in developing process models for
knowledge discovery in databases and data mining algorithms. Her further interests
include problems pertaining to modelling of changes in discovered knowledge in
evolving (streaming) data sets, handling user subjectivity in KDD, projected
clustering, outlier detection, classification and cluster ensembles. She is currently
studying graphs as tool for modelling biology problems and texts.
Dr. Durgesh Kumar Mishra is a Professor (CSE) and Director of the Microsoft
Innovation Centre at Shri Aurobindo Institute of Technology, Indore, India. He has
24 years of teaching and research experience and has published over 100 research

papers. He is a Senior Member of IEEE and Chairman, Computer Society of India

xvii


xviii

About the Editors

(CSI) Division IV. He has held positions including Chairman, IEEE MP-Subsection
and Chairman, IEEE Computer Society, Bombay Chapter. He has delivered invited
talks at IEEE International conferences and serves on the Editorial Board of many
national and international refereed journals. He is also a Member of the Bureau of
Indian Standards (BIS), Government of India.


Need for Developing Intelligent Interfaces
for Big Data Analytics in the Microfinance
Industry
Purav Parikh and Pragya Singh

Abstract The main objective of the paper is to provide a multidimensional perspective of the microfinance industry where one finds that several different components such as “Sustainable Rural employment”, “Data Analysis for the Micro
Finance Industry”, and Theory of Maslow’s Need Hierarchy interrelate and work
hand in hand. There is a strong correlation between Maslow’s need hierarchy theory
of motivation and assessing the changes in demand for financial services in the
microfinance industry. How ICT and data analytics could help in efficiently tracking
the change in demand and thus help the microfinance institutions in better demand
forecasting as well as acquisition and management of resources, which are shared
commonly, between various stakeholders, is the focus of this research paper. The
paper is structured in sections starting with an introduction of the microfinance

industry. It is then followed by the literature review, which explains a few of the
concepts in theory to form the base. Other sections include discussion and policy
implications followed by conclusion and future research which focuses more on the
IT interventions and the need for advance level and integrated systems design for
efficient delivery of financial services, better policy planning, and optimized use of
real-time information for analytical decision-making, at the MFI level for the
microfinance industry to achieve its goal of financial inclusion.

Á

Á

Keywords Microfinance industry
Big data
Data analytics
Motivation MIPC Human–computer interactions ICT

Á

Á

Á

Á

Real time

Á

P. Parikh (&) Á P. Singh

Department of Management Studies, Indian Institute
of Information Technology, Allahabad, India
e-mail:
P. Singh
e-mail:
© Springer Nature Singapore Pte Ltd. 2018
V.B. Aggarwal et al. (eds.), Big Data Analytics, Advances in Intelligent
Systems and Computing 654, />
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P. Parikh and P. Singh

1 Introduction
Microfinance industry as we know it today is changing the lives of people who
depend on it for various financial services not only in India but globally as well.
Whether it is a small size or a marginal loan amount, or a savings account, crop
loan, or for fulfilling social events of life such as birth or death ceremonies, marriages and likewise. Schumpeterian has defined microfinance service provider as an
entrepreneur, in a sense that the form of business he is involved is social but
innovative in nature. The fact is that by venturing into such a business he is not only
running the business, but also solving a social problem, and creating new relationships using innovative business models which involve ground level actions for
empowering people in different ways [1].
This research paper focuses on the aspect the use of data analytics in the MFIs
(MFIs hereafter) for analyzing and tracking the user needs and necessities. The
paper is structured in forms of sections, such as literature review, which relates
more towards the need hierarchy theory of motivation as defined by Maslow
(1943). The contextual correlation of this theory is significant in serving the
microfinance sector customers as their needs and aspirations keep on changing from

time to time. The section covers in detail about the connections of this theory and its
applicability in the microfinance industry, in particular at the MFIs level. Followed
by it is the method of study, which is analytical and based on the information
obtained from the secondary data sources such as scholarly articles, periodical,
working papers, report publications, as well as recent studies conducted by the
researchers in India and abroad. The rest of the sections such as discussion and
policy implications, followed by conclusion and future research, talks more about
the ICT interventions for efficient delivery of financial services for the microfinance
industry and in particular, the MFIs.

2 Literature Review
Maslow (1943) said that, “A musician must make music, an artist must paint, a poet
must write, if he is to be ultimately happy. What a man can be, he must be. This
need we may call self actualization”. This definition as proposed by Maslow
indicates that there is a strong relationship with the entrepreneur and the business he
operates. At the same time, this also indicates the fact that the self-actualizing
entrepreneur is also looked upon in this world for producing most innovative ideas,
products and services, for the benefit of mankind [2]. Maslow (1943) further proposed a theory in order to give more contextual meaning to his definition of a
self-actualizing entrepreneur. He called it a theory of the need hierarchy of motivation. In this theory, he has defined individual needs in terms of hierarchy.
According to this world famous theory, he has defined an individual’s need in terms
of lower order and higher order needs. An individual will gain satisfaction by


Need for Developing Intelligent Interfaces …

3

fulfilling lower order needs first and then he will gradually move toward fulfilling
higher order needs. This process continues up till he reaches the highest order of
need which Maslow (1943) refers to as “Self Actualization”. At this point, he

attends highest satisfaction and a sense of fulfillment as well as accomplishment [2].
Bernheim [1], in her research paper, indicates that microfinance is a mechanism, for
providing financial services, to the poor as well as financially excluded people.
Further, the services provided are very small amount, which generates high level of
transaction as well as operations costs. Therefore, in order to serve this segment, it
becomes imperative that innovative way of doing the business be developed [1]
Parikh [3–5] has emphasized on the Maslow’s Need theory in his published
research papers. In this context, he has pointed the fact, such that a purchasing
power of a consumer changes with the change in income and standard of living over
a period of time. This has a direct impact on the demand for financial services
which he requires for consumption and growth. According to his opinion, this
change phenomenon as defined by the Motivation theory requires IT interventions,
in the form of more analytical, robust and IT based system, which he calls as,
“Microfinance Information Processing Centers” (MIPCs) [5], as one solution for
dealing with the change aspect of the microfinance industry.

3 Discussion and Policy Implications
As discussed in the literature review section of this paper, it becomes apparent that
the data analytics and the demand forecasting plays a very important role, in efficient delivery of financial services, in the microfinance industry. In this context, it
becomes important to study the change in consumers demand and requirements in
real time as their purchasing power increases over a period of time. There is a need
for developing a client responsive technological solution for the microfinance
industry and the MFIs in particular, which could help them to take informed
investment decisions based on the real-time data and thus provide better financial
products and services to the customer of the microfinance industry.
As explained in Fig. 1, we have constant interaction of various components
which impacts the growth and development of the microfinance industry. On one
hand, you have big chunk of data which is available from the consumers. This data
has to be put in use in real time, analyzed in real time and actions such as policies
and programs need to be implemented based on such a study, that too in real time.

Second and third aspects which we could see in Fig. 1 are related to Maslow’s
Need Hierarchy Theory of Motivation and a need for sustainable rural employment
and entrepreneurship for financial inclusion. Enough has been explained in previous
sections as to how this theory is important and affects every individual’s livelihood.
Also, the system such as MIPCs which could provide a robust solution for the MFIs
to leverage on the growth potentials of the ICT enabled system for the benefit of its


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P. Parikh and P. Singh

Fig. 1 Multi-dimensional perspective for the micro finance industry

customer has been well covered. These two aspects are the cornerstones while
developing an ICT enabled system for human and computer interactions with the
customers of microfinance industry.

4 Conclusion and Future Research
An attempt was made through this research paper, to present a theory of Maslow’s
need hierarchy (1943) and show its relevance in the microfinance industry, particularly at MFI level. The present literature review indicates the gap, which is that,
it is difficult for the MFIs to study and keep track of the change in customer
demands in relation to the microfinance products and services in real time, using the
traditional framework. In this context, it provides with a perspective model such as,
“Multi-dimensional perspective for the Microfinance Industry” (see Fig. 1).
Acknowledgements I would like to acknowledge the funding received from Ministry of Human
Resource Development, Government of India in terms of Junior Research Fellowship
(JRF) towards my PhD research work at IIIT Allahabad.

Need for Developing Intelligent Interfaces …

5

References
1. Bernheim, E.: Microfinance and micro entrepreneurship: case studies in social entrepreneurship and social innovation. CEB Working Paper. Solvay Brussels School of Economics and
Management Center, Universite Libre de Bruxelles (2013)
2. Mui, A.: Entrepreneurship: the act of enhancing one’s reality (ERSA). Erasmus School of
Economics, Erasmus University, Rotterdam (2010)
3. Parikh, P.: Building of an ecosystem of applications for efficient delivery of financial services:
a case for MIPC. In: IEEE Xplore. International Conference on IT in Business, Industry and
Government (CSIBIG) 2014, Sri Aurbido Institute of Technology, March, 2014, pp. 218–220,
India (2014)
4. Parikh, P.: Cloud computing based open source information technology infrastructure for
financial inclusion. In: 12th Thinkers and Writers Forum. 28th Skoch Summit on
Mainstreaming the Marginalized, New Delhi, India, 28 March 2012
5. Parikh, P.: Mobile based intelligent computing model for MFIs through MIPC. In: Computer
Society of India, ICIS-2014, International Conference on Information Science July, 2014,
Kochi, India (2014)
6. Augsburg, B., Schmidt, J.P., Krishnaswamy, K.: Free and open source software for
microfinance: increasing efficiency and extending benefits to the poor. In: Business Science
References (Ch. 2). New York (2011). />7. Assadi, D., Hudson, M.: Marketing analysis of emerging peer-to-peer microlending websites.
Bus. Sci. Ref. 30(4) (2005)
8. Das, P.: A case study of Mifos implementation at Asomi. In: Business Science References
(Ch. 5). New York (2011)
9. Khan, S.: Automating MFIs: how far should we go? In: Business Science References (Ch. 4).
New York (2011)
10. Jawadi, F., Jawadi, N., Ziane, Y.: Can information and communication technologies improve
the performance of microfinance programs? Further Evidence from developing and emerging
financial markets. In: Business Science References (Ch. 10). New York (2011)

11. Nyapati, K.: Stakeholder analysis of IT applications for microfinance. Business Science
References (Ch. 1). New York (2011)
12. Musa, A.S.M., Khan, M.S.R.: Implementing point of sale technology in microfinance: an
evaluation of come to save (CTS) cooperatives, Bangladesh. Business Science References
(Ch. 6). New York (2011)
13. Makhijani, N.: Non banking finance companies—time to introspect! ANALYTIQUE 9–10(2)
(2014)
14. Quadri, S.M.N., Singh, V.K., Iyenger, K.P.: IT and MIS in microfinance institution:
effectiveness and sustainability issues. In: Business Science References (Ch. 3). New York
(2011)
15. Sairam, M.S.: Information asymmetry and trust: a framework for studying microfinance in
India. Vikalpa 30(4) (2005)


Unified Resource Descriptor over KAAS
Framework
Refining Cloud Dynamics
Subhajit Bhattacharya

Abstract With the advent of information digitization, virtual social networking,
and other means of information sharing protocols, today billions of data are
available on the World Wide Web from heterogeneous sources. All these data
further contribute to the emergence of Big Data gamut. When these data are processed further, we get a glimpse of information which gives some level of understanding on the subject or the matter (person, place, enterprise, etc.). Knowledge is
cohesively logically processed related information with the intellect to give us
multidimensional information spectrum for decision-making in real time. In today’s
global environment, data plays crucial role to understand the social, cultural,
behavioral, and demographic attributes of a subject. Knowledge-as-a-Service
(KAAS) is a pioneering cloud framework inheriting the “Internet of Things”
principles that extract data from various sources in a seamless manner and can
further decouple–couple logically processed information based on the “matching

chromosome” algorithm. Unified Resource Descriptor (URD) is an innovative
information modeling technique that operates over KAAS framework to further
publish knowledge on the subject on need basis. Based on this concept, every
resource or subject is assigned a unique identifier that can perform multilayered
search in the KAAS Database to extract relevant knowledge frames. Considering
India’s context, second most populated country in the world, URD can play an
indispensable role to tighten information dynamics holistically and accumulate a
broader spectrum of knowledge of the resource to address adverse situations
(natural calamity, medication, insurance, etc.), business process solution (Banking,
BPOs, KPOs, etc.), and research practices.
Keywords Big data

Á KAAS Á Cloud computing Á Knowledgebase Á BI

S. Bhattacharya (&)
Accenture, New Delhi, India
e-mail:
© Springer Nature Singapore Pte Ltd. 2018
V.B. Aggarwal et al. (eds.), Big Data Analytics, Advances in Intelligent
Systems and Computing 654, />
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S. Bhattacharya

1 Introduction
Today, Information Technology has spread its wings wide and social sites have
become the boon for social connectivity, every day the World Wide Web is getting

cluttered with billions of data from heterogeneous sources. These structured,
semi-structured, and unstructured data hubs form the big data gamut. Today, the
biggest challenge is the utilization and proper processing of these data to derive
adequate information.
Knowledge-As-A-Service is one of the pioneering initiatives to redefine cloud
dynamics which enables multi-tier filtering and processing of data over “matching
chromosome” algorithm to form information cuboids that are further filtered
through analytical engine to get intelligently sliced, diced, and re-clustered to build
information pool for a particular resource/subject. Matching chromosome is an
AI-based algorithm to compare and then couple, decouple, and recouple the relevant data about the resource and thus formalize knowledge framework that further
gets processed through KAAS engine to form knowledge warehouses. The ultimate
idea is to bring “Information Neutrality” across the globe.
Here, the primary objective is to optimize and convert huge abandon data in the
form of knowledge that can provide significant level of information for decision
making and further knowledge transition.
Unified Resource Descriptor (URD) is an innovative information modeling
technique that operates over KAAS framework to further publish knowledge on the
subject/resource comparing behavioral, demographic, social, political, economic,
and other aspects. URD ID operates as a primary key assigned to every
resource/subject for which significant volume of knowledge is presented to the end
user. It can be further associated as “Social Resource Planning (SRP)”.
Considering India’s context, URD can play a central role to tighten information
dynamics holistically and accumulate a broader spectrum of knowledge of the
resources to address adverse situations (war, natural calamity, medication, insurance, etc.), business process solutions (BFSI/FMCG/BPOs/KPOs, etc.), and
education/research institutions resulting to cost efficiencies, productivity, and
innovation. Most importantly, it can prove one of the significant and indispensable
technologies for rural India for education and other vital facilities.
The URD ID is assigned to a subject/resource; the information about that
resource will be available to the end user for knowledge and decision purpose.
This URD ID works as cohesive meta-knowledge. Under KAAS framework,

URD ID is explicitly associated with the resource for unified information
representation.
In the KAAS framework, resources are scanned as an image or by data attributes
or by videos/audios to get an in-depth insight. Therefore, when a medical firm scans
an image of a patient so it can get the patient’s past medical reports saving time and
cost, an insurance institution scans through person details to get his past insurance
details, bank can assess the credibility of the resource or company to save itself
from bad debts, defense personnel can scan suspect to see his past history, a villager