April 2020 top read artilce in Computer Science and Information Technology

April 2020: Top Read
Articles in Computer Science
and Information Technology
International Journal of Computer Science and
Information Technology (IJCSIT)
ISSN: 0975-3826(online); 0975-4660 (Print)
http://airccse.org/journal/ijcsit.html

BIG DATA IN CLOUD COMPUTING REVIEW AND
OPPORTUNITIES
Manoj Muniswamaiah, Tilak Agerwala and Charles Tappert
Seidenberg School of CSIS, Pace University, White Plains, New York
ABSTRACT
Big Data is used in decision making process to gain useful insights hidden in the data for
business and engineering. At the same time it presents challenges in processing, cloud
computing has helped in advancement of big data by providing computational, networking
and storage capacity. This paper presents the review, opportunities and challenges of
transforming big data using cloud computing resources.
KEYWORDS
Big data; cloud computing; analytics; database; data warehouse
For More Details: http://aircconline.com/ijcsit/V11N4/11419ijcsit04.pdf
Volume Link: http://airccse.org/journal/ijcsit2019_curr.html

REFERENCES
[1] Konstantinou, I., Angelou, E., Boumpouka, C., Tsoumakos, D., & Koziris, N. (2011, October).
On the elasticity of nosql databases over cloud management platforms. In Proceedings of the
20th ACM international conference on Information and knowledge management (pp. 2385-
2388). ACM.
[2] Labrinidis, Alexandros, and Hosagrahar V. Jagadish. "Challenges and opportunities with big
data." Proceedings of the VLDB Endowment 5.12 (2012): 2032-2033.
[3] Abadi, D. J. (2009). Data management in the cloud: Limitations and opportunities. IEEE Data
Eng. Bull, 32(1), 3-12.
[4] Luhn, H. P. (1958). A business intelligence system. IBM Journal of Research and Development,
2(4), 314-319 International Journal of Computer Science & Information Technology (IJCSIT)
Vol 11, No 4, August 2019 57
[5] Sivarajah, Uthayasankar, et al. "Critical analysis of Big Data challenges and analytical
methods." Journal of Business Research 70 (2017): 263-286.
[6] https://www.bmc.com/blogs/saas-vs-paas-vs-iaas-whats-the-difference-and-how-to-choose/
[7] Kavis, Michael J. Architecting the cloud: design decisions for cloud computing service models
(SaaS, PaaS, and IaaS). John Wiley & Sons, 2014.
[8] https://www.ripublication.com/ijaer17/ijaerv12n17_89.pdf
[9] Sakr, S. & Gaber, M.M., 2014. Large Scale and big data: Processing and Management
Auerbach, ed.
[10] Ji, Changqing, et al. "Big data processing in cloud computing environments." 2012 12th
international symposium on pervasive systems, algorithms and networks. IEEE, 2012.
[11] Han, J., Haihong, E., Le, G., & Du, J. (2011, October). Survey on nosql database. In Pervasive
Computing and Applications (ICPCA), 2011 6th International Conference on (pp. 363-366).
IEEE.
[12] Zhang, L. et al., 2013. Moving big data to the cloud. INFOCOM, 2013 Proceedings IEEE,
pp.405–409
[13] Fernández, Alberto, et al. "Big Data with Cloud Computing: an insight on the computing
environment, MapReduce, and programming frameworks." Wiley Interdisciplinary Reviews:
Data Mining and Knowledge Discovery 4.5 (2014): 380-409.
[14] http://acme.able.cs.cmu.edu/pubs/uploads/pdf/IoTBD_2016_10.pdf
[15] Xiaofeng, Meng, and Chi Xiang. "Big data management: concepts, techniques and challenges
[J]." Journal of computer research and development 1.98 (2013): 146-169.
[16] Muniswamaiah, Manoj & Agerwala, Tilak & Tappert, Charles. (2019). Challenges of Big Data
Applications in Cloud Computing. 221-232. 10.5121/csit.2019.90918.

SMART MOTORCYCLE HELMET: REAL-TIME CRASH
DETECTION WITH EMERGENCY NOTIFICATION,
TRACKER AND ANTI-THEFT SYSTEM USING
INTERNET-OF-THINGS CLOUD BASED TECHNOLOGY
Marlon Intal Tayag1 and Maria Emmalyn Asuncion De Vigal Capuno2
1College of Information and Communications Technology Holy Angel University,
Angeles, Philippines
2Faculty of Information Technology Future University, Khartoum, Sudan
ABSTRACT
Buying a car entails a cost, not counting the day to day high price tag of gasoline. People are
looking for viable means of transportation that is cost-effective and can move its way through
traffic faster. In the Philippines, motorcycle was the answer to most people transportation
needs. With the increasing number of a motorcycle rider in the Philippines safety is the
utmost concern. Today technology plays a huge role on how this safety can be assured. We
now see advances in connected devices. Devices can sense its surrounding through sensor
attach to it. With this in mind, this study focuses on the development of a wearable device
named Smart Motorcycle Helmet or simply Smart Helmet, whose main objective is to help
motorcycle rider in times of emergency. Utilizing sensors such as alcohol level detector,
crash/impact sensor, Internet connection thru 3G, accelerometer, Short Message Service
(SMS) and cloud computing infrastructure connected to a Raspberry Pi Zero-W and
integrating a separate Arduino board for the anti-theft tracking module is used to develop the
propose Internet-of Things (IoT) device.
Using quantitative method and descriptive type research, the researchers validated the results
from the inputs of the participant who tested the smart helmet during the alpha and beta
testing process. Taking into account the ethical consideration of the volunteers, who will test
the Smart Helmet. To ensure the reliability of the beta and alpha testing, ISO 25010 quality
model was used for the assessment focusing on the device accuracy, efficiency and
functionality. Based on the inputs and results gathered, the proposed Smart Helmet IoT device
can be used as a tool in helping a motorcycle rider when an accident happens to inform the
first-responder of the accident location and informing the family of the motorcycle rider.
.
KEYWORDS
Smart Helmet, Internet of Things, Sensors, Real-Time Crash Detection, Emergency
Notification, Tracker, Anti-Theft System Cloud Based Technology

REFERENCES
[1] Mascarinas, E. M. (2016). Study in better safety measures for motorcycles urged -
SUNSTAR. Retrieved December 11, 2018, from
https://www.sunstar.com.ph/article/111646
[2] L. Ramos. (2018). Road Accidents In The Philippines: Key Figures - eCompareMo -
eCompareMo.Retrieved December 11, 2018, from
https://www.ecomparemo.com/info/road-accidents-in-the-philippines-key-figures/
[3] Nandu, R., & Singh, K. (2014). Smart Helmet for Two-Wheelers. Advances in Automobile
Engineering, 03(01), 1–2. https://doi.org/10.4172/2167-7670.1000110
[4] Sung, K.-M., Noble, J., Kim, S.-C., Jeon, H.-J., Kim, J.-Y., Do, H.-H., … Baek, K.-J.
(2016). The Preventive Effect of Head Injury by Helmet Type in Motorcycle Crashes: A
Rural Korean Single-Center Observational Study. BioMed Research International, 2016, 1–
7. https://doi.org/10.1155/2016/1849134
[5] J. Dodson. (n.d.). Motorcycle Crashes and Brain Injuries | Jim Dodson Law. Retrieved
December 11, 2018, from https://www.jimdodsonlaw.com/library/motorcycle-crashes-and-
brain-injuries.cfm
[6] W. Tan. (2018). WHO PH: Over 90% of Motorcycle Deaths Didn’t Wear Helmets -
Carmudi Philippines. Retrieved December 18, 2018, from
https://www.carmudi.com.ph/journal/philippines-90-motorcycle-deaths-didnt-wear-helmets/
[7] Lahausse, J. A., Fildes, B. N., Page, Y., & Fitzharris, M. P. (2008). The potential for
automatic crash notification systems to reduce road fatalities. Annals of Advances in
Automotive Medicine. Association for the Advancement of Automotive Medicine. Annual
Scientific Conference, 52, 85–92. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/19026225
[8] M. Glasscoe. (n.d.). What is GPS? Retrieved December 11, 2018, from
http://scecinfo.usc.edu/education/k12/learn/gps1.htm
[9] Brodsky, H. (n.d.). Emergency Medical Service Rescue Time in Fatal Road Accidents.
Retrieved from http://onlinepubs.trb.org/Onlinepubs/trr/1990/1270/1270-011.pdf
[10] J. Porter. (2018). The History of IoT (Internet of Things) and How It’s Changed Today.
Retrieved December 18, 2018, from https://www.techprevue.com/history-iot-changed-
today/
[11] Vishal, D., Afaque, H. S., Bhardawaj, H., & Ramesh, T. K. (2018). IoT-driven road safety
system. International Conference on Electrical, Electronics, Communication Computer
Technologies and Optimization Techniques, ICEECCOT 2017, 2018–Janua, 862–866.
https://doi.org/10.1109/ICEECCOT.2017.8284624
[12] Dickenson. (2016). How IoT and machine learning can make our roads safer | TechCrunch.
Retrieved December 18, 2018, from https://techcrunch.com/2016/07/13/how-iot-and-
machine-learning-can-make-our-roads-safer/
[13] Faizan Manzoor, S. A. B. (2017). Faaz smart helmet, 6(6), 332–335.

[14] Hobby, K. C., Gowing, B., & Matt, D. P. (2016). Smart helmet, 5(3), 660–663.
[15] Khaja, M., Aatif, A., & Manoj, A. (2017). Smart Helmet Based On IoT Technology, 5(Vii),
409–413.
[16] Motorcyclist age group and gender data - TAC - Transport Accident Commission. (n.d.).
Retrieved April 20, 2019, from http://www.tac.vic.gov.au/road-
safety/statistics/summaries/motorcycle-crash-data/motorcyclist-age-group-and-gender-data
[17] Amir, G. (n.d.). Prototyping Model in Software Development and Testing. Retrieved
January 22, 2019, from https://www.testingexcellence.com/prototyping-model-software-
development/
[18] The Importance of Alpha & Beta Testing Services | Software Testing Tips and Best
Practices. (n.d.). Retrieved April 21, 2019, from https://blog.testmatick.com/2016/04/19/the-
importance-of-alpha-beta-testing-services/
AUTHORS
Dr. Marlon I. Tayag is a full-time Associate Professor at Holy Angel
University and teaches Cyber Security subjects on Ethical Hacking and
Forensic. He earned the degree of Doctor in Information Technology from
St. Linus University in 2015 and is currently taking up Doctor of
Philosophy in Computer Science at Technological Institute of the
Philippines – Manila. Dr. Tayag is Cisco Certified Network Associate,
210-250 CCNA Understanding Cisco Cybersecurity Fundamentals and
Fluke CCTTA – Certified Cabling Test Technician Associate. Microsoft
Certified Professional and Microsoft Certified Educator.
Dr. Ma. Emmalyn A. V. Capuno is a currently the Dean of the Faculty of
Information Technology of Future University Sudan with the academic rank
of Associate Professor; a position she has been holding since 2009. She
earned the degree of Doctor of Philosophy in Information Technology
Management from Colegio de San Juan Letran – Calamba, Philippines in
2005. Her teaching and research expertise includes Operating Systems,
Knowledge Management, Business Intelligence and many more.

DATA MINING MODEL PERFORMANCE OF SALES PREDICTIVE
ALGORITHMS BASED ON RAPIDMINER WORKFLOWS
Alessandro Massaro, Vincenzo Maritati, Angelo Galiano
Dyrecta Lab, IT research Laboratory,via Vescovo Simplicio,
45, 70014 Conversano (BA), Italy
ABSTRACT
By applying RapidMiner workflows has been processed a dataset originated from
different data files, and containing information about the sales over three years of a large
chain of retail stores. Subsequently, has been constructed a Deep Learning model
performing a predictive algorithm suitable for sales forecasting. This model is based on
artificial neural network –ANN- algorithm able to learn the model starting from sales
historical data and by pre-processing the data. The best built model uses a multilayer
eural network together with an “optimized operator” able to find automatically the best
parameter setting of the implemented algorithm. In order to prove the best performing
predictive model, other machine learning algorithms have been tested. The performance
comparison has been performed between Support Vector Machine –SVM-, k- Nearest
Neighbor k-NN-,Gradient Boosted Trees, Decision Trees, and Deep Learning algorithms.
The comparison of the degree of correlation between real and predicted values, the
verage absolute error and the relative average error proved that ANN exhibited the best
performance. The Gradient Boosted Trees approach represents an alternative approach
having the second best performance. The case of study has been developed within the
framework of an industry project oriented on the integration of high performance data
mining models able to predict sales using– ERP- and customer relationship management
–CRM- tools.
KEYWORDS
RapidMiner, Neural Network, Deep Learning, Gradient Boosted Trees, Data Mining
Performance, Sales Prediction.
For More Details : http://aircconline.com/ijcsit/V10N3/10318ijcsit03.pdf

REFERENCES
[1] Penpece D., & Elma O. E. (2014) “Predicting Sales Revenue by Using Artificial
Neural Network in Grocery Retailing Industry: A Case Study in Turkey”,
International Journal of Trade Economics and Finance, Vol. 5, No. 5, pp435-440.
[2] Thiesing F. M., & Vornberger, O. (1997) “Sales Forecasting Using Neural
Networks”, IEEE Proceedings ICNN’97, Houston, Texas, 9-12 June 1997, pp2125-
2128.
[3] Zhang, G. P. (2003) “Time series forecasting using a hybrid ARIMA and neural
network model”, Neurocomputing, Vol. 50, pp159–175.
[4] Sharma, A., & Panigrahi, P. K. (2011) “Neural Network based Approach for
Predicting Customer Churn in Cellular Network Services”, International Journal of
Computer Applications, Vol. 27, No.11, pp0975–8887.
[5] Kamakura, W., Mela, C. F., Ansari A., & al. (2005) ” Choice Models and Customer
Relationship Management,” Marketing Letters, Vol. 16, No.3/4, pp279–291.
[6] Smith, K. A., & Gupta, J. N. D. (2000) “Neural Networks in Business: Techniques
and Applications for the Operations Researcher,” Computers & Operations
Research, Vol. 27, No. 11–12, pp1023- 1044.
[7] Chattopadhyay, M., Dan, P. K., Majumdar, S., & Chakraborty, P. S. (2012)
“Application of Artificial Neural Network in Market Segmentation: A Review on
Recent Trends,” Management Science Letters, Vol. 2, pp425-438.
[8] Berry, J. A. M., & Linoff, G. S. (2004) “Data Mining Techniques For Marketing,
Sales, and Customer Relationship Management”, Wiley, Second Edition.
[9] Buttle, F. (2009) “Customer Relationship Management Concepts and
Technologies”, Elsevier, Second Edition.
[10] Thomassey, S. (2014) “Sales Forecasting in Apparel and Fashion Industry: A
Review”, Springer, chapter 2.
[11] Massaro, A. Barbuzzi, D., Vitti, V., Galiano, A., Aruci, M., Pirlo, G. (2016)
“Predictive Sales Analysis According to the Effect of Weather”, Proceeding of the
2nd International Conference on Recent Trends and Applications in Computer
Science and Information Technology, Tirana, Albania, November 18 - 19, pp53-55.
[12] Parsons, A.G. (2001), “The Association between Daily Weather and Daily
Shopping Patterns”, Australasian Marketing Journal, Vol. 9, No. 2, pp78–84.

[13] Steele, A.T., (1951) “Weather’s Effect on the Sales of a Department Store”, Journal
of Marketing Vol. 15, No. 4, pp436–443.
[14] Murray, K. B., Di Muro, F., Finn, A., & Leszczyc, P. P. (2010) “The Effect of
Weather on Consumer Spending”, Journal of Retailing and Consumer Services,
Vol. 17, No.6, pp512-520.
[15] Massaro, A., Galiano, A., Barbuzzi, D., Pellicani, L., Birardi, G., Romagno, D. D.,
& Frulli, L., (2017) “Joint Activities of Market Basket Analysis and Product Facing
for Business Intelligence oriented on Global Distribution Market: examples of data
mining applications,” International Journal of Computer Science and Information
Technologies, Vol. 8, No.2 , pp178- 183.
[16] Aguinis, H., Forcum, L. E., & Joo, H. (2013) “Using Market Basket Analysis in
Management Research,” Journal of Management, Vol. 39, No. 7, pp1799-1824.
[17] Štulec, I, Petljak, K., & Kukor, A. (2016) “The Role of Store Layout and Visual
Merchandising in Food Retailing”, European Journal of Economics and Business
Studies, Vol. 4, No. 1, pp139- 152.
[18] Otha, M. & Higuci, Y. (2013) “Study on Design of Supermarket Store Layouts: the
Principle of “Sales Magnet””, World Academy of Science, Engieering and
Technology, Vol. 7, No. 1, pp209-212.
[19] Shallu, & Gupta, S. (2013) “Impact of Promotional Activities on Consumer Buying
Behavior: A Study of Cosmetic Industry”, International Journal of Commerce,
Business and Management (IJCBM), Vol. 2, No.6, pp379-385.
[20] Al Essa, A. & Bach, C. (2014)“ Data Mining and Knowledge Management for
Marketing”, International Journal of Innovation and Scientific Research, Vol. 2, No.
2, pp321-328.
[21] Kotu, V., & Deshpande B. (2015) “Predictive Analytics and Data Mining- Concepts
and Practice with RapidMiner” Elsevier.
[22] Wimmer, H., Powell, L. M. (2015) “A Comparison of Open Source Tools for Data
Science”, Proceedings of the Conference on Information Systems Applied
Research. Wilmington, North Carolina USA.
[23] Al-Khoder, A., Harmouch, H., “Evaluating Four Of The most Popular Open Source
and Free Data Mining Tools”, International Journal of Academic Scientific
Research, Vol. 3, No. 1, pp13-23.

[24] Gulli, A., & Pal, S. (2017) “Deep Learning with Keras- Implement neural networks
with Keras on Theano and TensorFlow,” Birmingham -Mumbai Packt book, ISBN
978-1-78712-842-2.
[25] Kovalev, V., Kalinovsky, A., & Kovalev, S. (2016) “Deep Learning with Theano,
Torch, Caffe, TensorFlow, and deeplearning4j: which one is the best in speed and
accuracy?” Proceeding of XIII Int. Conf. on Pattern Recognition and Information
Processing, 3-5 October, Minsk, Belarus State University, pp99-103.
AUTHOR
Alessandro Massaro: Research & Development Chief of Dyrecta Lab s.r.l.

CONVOLUTIONAL NEURAL NETWORK BASED FEATURE
EXTRACTION FOR IRIS RECOGNITION
Maram.G Alaslani1 and Lamiaa A. Elrefaei1,2
1Computer Science Department, Faculty of Computing and Information
Technology, King Abdulaziz University, Jeddah, Saudi Arabia
2 Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha
University, Cairo, Egypt
ABSTRACT
Iris is a powerful tool for reliable human identification. It has the potential to identify
individuals with a high degree of assurance. Extracting good features is the most
significant step in the iris recognition system. In the past, different features have been
used to implement iris recognition system. Most of them are depend on hand-crafted
features designed by biometrics specialists. Due to the success of deep learning in
computer vision problems, the features learned by the Convolutional Neural Network
(CNN) have gained much attention to be applied for iris recognition system. In this paper,
we evaluate the extracted learned features from a pre-trained Convolutional Neural
Network (Alex-Net Model) followed by a multi-class Support Vector Machine (SVM)
algorithm to perform classification. The performance of the proposed system is
investigated when extracting features from the segmented iris image and from the
normalized iris image. The proposed iris recognition system is tested on four public
datasets IITD, iris databases CASIAIris-V1, CASIA-Iris-thousand and, CASIA-Iris- V3
Interval. The system achieved excellent results with the very high accuracy rate.
KEYWORDS
Biometrics, Iris, Recognition, Deep learning, Convolutional Neural Network (CNN),
Feature extraction (FE).
For More Details : http://aircconline.com/ijcsit/V10N2/10218ijcsit06.pdf

REFERENCES
[1] M. Haghighat, S. Zonouz, and M. Abdel-Mottaleb, "CloudID: Trustworthy cloud-based and
crossenterprise biometric identification," Expert Systems with Applications, vol. 42, pp.
7905-7916, 2015.
[2] D. Kesavaraja, D. Sasireka, and D. Jeyabharathi, "Cloud software as a service with iris
authentication," Journal of Global Research in Computer Science, vol. 1, pp. 16-22, 2010.
[3] N. Shah and P. Shrinath, "Iris Recognition System–A Review," International Journal of
Computer and Information Technology, vol. 3, 2014.
[4] A. B. Dehkordi and S. A. Abu-Bakar, "A review of iris recognition system," Jurnal
Teknologi, vol. 77, 2015.
[5] S. Minaee, A. Abdolrashidiy, and Y. Wang, "An experimental study of deep convolutional
features for iris recognition," in Signal Processing in Medicine and Biology Symposium
(SPMB), 2016 IEEE, 2016, pp. 1-6. International Journal of Computer Science &
Information Technology (IJCSIT) Vol 10, No 2, April 2018 77
[6] S. Minaee, A. Abdolrashidi, and Y. Wang, "Iris recognition using scattering transform and
textural features," in Signal Processing and Signal Processing Education Workshop
(SP/SPE), 2015 IEEE, 2015, pp. 37-42.
[7] S. Minaee, A. Abdolrashidi, and Y. Wang, "Face Recognition Using Scattering
Convolutional Network," arXiv preprint arXiv:1608.00059, 2016.
[8] IIT Delhi Database. Available:
http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm. Accessed 14 April
2017.
[9] ( 2 April2017). CASIA Iris Image Database Version 1.0. Available:
http://www.idealtest.org/findDownloadDbByMode.do?mode=Iris. Accessed 12 April 2017.
[10] CASIA Iris Image Database Version 4.0 (CAS IA-Iris-Thousand). Available:
http://biometrics.idealtest.org/dbDetailForUser.do?id=4. Accessed 17 April 2017.
[11] CASIA Iris Image Database Version 3.0 (CASIA-Iris-Interval). Available:
http://biometrics.idealtest.org/dbDetailForUser.do?id=3. Accessed 17April2017.
[12] K. Nguyen, C. Fookes, A. Ross, and S. Sridharan, "Iris Recognition with Off-the-Shelf CNN
Features: A Deep Learning Perspective," IEEE Access, 2017.
[13] A. Romero, C. Gatta, and G. Camps-Valls, "Unsupervised deep feature extraction for remote
sensing image classification," IEEE Transactions on Geoscience and Remote Sensing, vol.
54, pp. 1349-1362, 2016.

[14] O. Oyedotun and A. Khashman, "Iris nevus diagnosis: convolutional neural network and
deep belief network," Turkish Journal of Electrical Engineering & Computer Sciences, vol.
25, pp. 1106-1115, 2017.
[15] A. S. Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, and T. A. Nagem, "A multi-biometric
iris recognition system based on a deep learning approach," Pattern Analysis and
Applications, pp. 1-20, 2017.
[16] J. Nagi, F. Ducatelle, G. A. Di Caro, D. Cireşan, U. Meier, A. Giusti, F. Nagi, J.
Schmidhuber, and L. M. Gambardella, "Max-pooling convolutional neural networks for
vision-based hand gesture recognition," in Signal and Image Processing Applications
(ICSIPA), 2011 IEEE International Conference on, 2011, pp. 342-347.
[17] D. Scherer, A. Müller, and S. Behnke, "Evaluation of pooling operations in convolutional
architectures for object recognition," Artificial Neural Networks–ICANN 2010, pp. 92-101,
2010.
[18] J. van Doorn, "Analysis of deep convolutional neural network architectures," 2014.
[19] C. L. Lam and M. Eizenman, "Convolutional neural networks for eye detection in remote
gaze estimation systems," 2008.
[20] S. Ahmad Radzi, K.-H. Mohamad, S. S. Liew, and R. Bakhteri, "Convolutional neural
network for face recognition with pose and illumination variation," International Journal of
Engineering and Technology (IJET), vol. 6, pp. 44-57, 2014.
[21] K. Itqan, A. Syafeeza, F. Gong, N. Mustafa, Y. Wong, and M. Ibrahim, "User identification
system based on finger-vein patterns using Convolutional Neural Network," ARPN Journal
of Engineering and Applied Sciences, vol. 11, pp. 3316-3319, 2016.
[22] S. Sangwan and R. Rani, "A Review on: Iris Recognition," (IJCSIT) International Journal of
Computer Science and Information Technologies, vol. 6, pp. 3871-3873, 2015
[23] C. Jayachandra and H. V. Reddy, "Iris Recognition based on Pupil using Canny edge
detection and KMeans Algorithm," Int. J. Eng. Comput. Sci., vol. 2, pp. 221-225, 2013.
[24] L. A. Elrefaei, D. H. Hamid, A. A. Bayazed, S. S. Bushnak, and S. Y. Maasher, "Developing
Iris Recognition System for Smartphone Security," Multimedia Tools and Applications, pp.
1-25, 2017.
[25] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep
convolutional neural networks," in Advances in neural information processing systems,
2012, pp. 1097-1105.
[26] S. Minaee and Y. Wang, "Palmprint Recognition Using Deep Scattering Convolutional
Network," arXiv preprint arXiv:1603.09027, 2016.

[27] J. Weston and C. Watkins, "Multi-class support vector machines," Technical Report CSD-
TR-98-04, Department of Computer Science, Royal Holloway, University of London,
May1998.
[28] G. Xu, Z. Zhang, and Y. Ma, "A novel method for iris feature extraction based on
intersecting cortical model network," Journal of Applied Mathematics and Computing, vol.
26, pp. 341-352, 2008.
[29] M. Abhiram, C. Sadhu, K. Manikantan, and S. Ramachandran, "Novel DCT based feature
extraction for enhanced iris recognition," in Communication, Information & Computing
Technology (ICCICT), 2012 International Conference on, 2012, pp. 1-6.
[30] M. Elgamal and N. Al-Biqami, "An efficient feature extraction method for iris recognition
based on wavelet transformation," Int. J. Comput. Inf. Technol, vol. 2, pp. 521-527, 2013.
[31] B. Bharath, A. Vilas, K. Manikantan, and S. Ramachandran, "Iris recognition using radon
transform thresholding based feature extraction with Gradient-based Isolation as a pre-
processing technique," in Industrial and Information Systems (ICIIS), 2014 9th
International Conference on, 2014, pp. 1-8.
[32] S. S. Dhage, S. S. Hegde, K. Manikantan, and S. Ramachandran, "DWT-based feature
extraction and radon transform based contrast enhancement for improved iris recognition,"
Procedia Computer Science, vol. 45, pp. 256-265, 2015.
AUTHORS
Maram G. Alaslani Received her B.Sc. degree in Computer Science with Honors from King
Abdulaziz University in 2010. She works as Teaching Assistant from 2011 to date at Faculty
of Computers and Information Technology at King Abdulaziz University, Rabigh, Saudi
Arabia. Now she is working in her Master Degree at King Abdulaziz University, Jeddah,
Saudi Arabia. She has a research interest in image processing, pattern recognition, and
neural network..
Lamiaa A. Elrefaei received her B.Sc. degree with honors in Electrical Engineering (Electronics and
Telecommunications) in 1997, her M.Sc. in 2003 and Ph.D. in 2008 in Electrical Engineering
(Electronics) from faculty of Engineering at Shoubra, Benha University, Egypt. She held a number of
faculty positions at Benha University, as Teaching Assistant from 1998 to 2003, as an Assistant
Lecturer from 2003 to 2008, and has been a lecturer from 2008 to date. She is currently an Associate
Professor at the faculty of Computing and Information Technology, King Abdulaziz University,
Jeddah, Saudi Arabia. Her research interests include computational intelligence, biometrics,
multimedia security, wireless networks, and Nano networks. She is a senior member of IEEE.

QUERY OPTIMIZATION FOR BIG DATA ANALYTICS
Manoj Muniswamaiah, Tilak Agerwala and Charles Tappert
Seidenberg School of CSIS, Pace University, White Plains, New York
ABSTRACT
Organizations adopt different databases for big data which is huge in volume and have different data
models. Querying big data is challenging yet crucial for any business. The data warehouses
traditionally built with On-line Transaction Processing (OLTP) centric technologies must be
modernized to scale to the ever-growing demand of data. With rapid change in requirements it is
important to have near real time response from the big data gathered so that business decisions
needed to address new challenges can be made in a timely manner. The main focus of our research is
to improve the performance of query execution for big data.
KEYWORDS
Databases, Big data, Optimization, Analytical Query, Data Analysts and Data Scientists

REFERENCES
[1] Duggan, J., Elmore, A. J., Stonebraker, M., Balazinska, M., Howe, B., Kepner, J., et al. (2015).
The BigDAWG Polystore System. ACM Sigmod Record, 44(3)
[2] V. Srinivasan and M. Carey. Performance of B-Tree Concurrency Control Algorithms. In
Proc.ACM SIGMOD Conf., pages 416–425, 1991
[3] A. Elmore, J. Duggan, M. Stonebraker, M. Balazinska, U. Cetintemel,V. Gadepally, J. Heer, B.
Howe, J. Kepner, T. Kraskaet al., “A demonstration of the bigdawg polystore
system,”Proceedings of theVLDB Endowment, vol. 8, no. 12, pp. 1908–1911, 2015
[4] http://kylin.apache.org
[5] D. Halperin et al. Demonstration of the myria big data management service. In SIGMOD, pages
881–884, 2014.
[6] Fuad, A., Erwin, A. and Ipung, H.P., 2014, September. Processing performance on Apache Pig,
Apache Hive and MySQL cluster. In Information, Communication Technology and System
(ICTS), 2014 International Conference on (pp. 297-302). IEEE.
[7] Liu, Shaosu, et al. "Kodiak: leveraging materialized views for very low-latency analytics over
high-dimensional web-scale data." Proceedings of the VLDB Endowment9.13 (2016): 1269-
1280
[8] https://lens.apache.org/
[9] https://calcite.apache.org/
[10] Muniswamaiah, Manoj & Agerwala, Tilak & Tappert, Charles. (2019). Query Performance
Optimization in Databases for Big Data. 85-90. 10.5121/csit.2019.90908.
[11] https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
[12] Luke Welling, Laura Thomson, PHP and MySQL Web Development, Sams, Indianapolis, IN,
2001
[13] https://www.splicemachine.com/
[14] C. Bear, A. Lamb, and N. Tran. The vertica database: Sql rdbms for managing big data. In
Proceedings of the 2012 workshop on Management of big data systems, pages 37–38.ACM,
2012
[15] Cong Jin, Shuang Ran, "The research for storage scheme based on Hadoop", Computer and
Communications (ICCC) 2015 IEEE International Conference on, pp. 62-66, 2015.

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