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Recent technological advances and reduction in storage prices has led to accumulation of huge amount of data known as Big Data. This data, belonging to different applications and timelines, is difficult for organisations to process. In order to solve this difficulty, Doug Cutting and Mike Cafarella came up with a framework called Hadoop. Becoming open source in 2012, Hadoop went on to include Pig, Hive and many more products. Following this, Spark was developed by MatieZaharia in 2009 which was open sourced in 2010. Meanwhile, many organisations came up with their own platforms to deal with Big Data. Hence, sprouting from Google`s MapReduce paper, these tools have grown into a wide array of technologies. This project focusses on comparing three main big data technologies which are used widely these days namely Pig, Hive and R. Similar problem statements are executed on all three platforms and performance is judged based upon the query execution time.

Hadoop, HDFS, Big Data, Pig, Hive, R

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[6] SurajitChaudhuri, What Next? A Half-Dozen Data Management Research Goals for Big Data and the Cloud
[7] Daniel Nunan, Maria Di Domenico, Market research & the ethics of big data
[8] Jeffrey Dean, Sanjay Ghemawat, MapReduce Simplified Data Processing on Large Clusters.
[9] Hsinchun Chen, Roger H. L. Chiang, Veda C. Storey, BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT
[10] Kyong-Ha Lee Yoon-Joon Lee, Hyunsik Choi Yon Dohn Chung, Bongki Moon, Parallel Data Processing with MapReduce: A Survey, SIGMOD Record, December 2011 (Vol. 40, No. 4)
[11] AbdelrahmanElsayed, Osama Ismail, and Mohamed E. El-Sharkawi, MapReduce: State-of-the-Art and Research Directions.
[12] Xindong Wu, Xingquan Zhu, Gong-Qing Wu and Wei Ding, Data Mining with Big Data (IEEE), Vol. 26, NO. 1, JANUARY 2014
[13] Michele De Gennaro, Elena Paffumi, Giorgio Martini, Big Data for Supporting Low-Carbon Road Transport Policies in Europe: Applications, Challenges and Opportunities, Intl Journal of Big Data Research (Elsevier), 2 June 2016
[14] Cui Yu, Josef Boyd, FB+- tree for Big Data Management, Intl Big Data Research (Elsevier), Pg: 25-36, Vol. 4, June 2016
[15] Diamantoulakis, P.D., Kapinas, V.M. Karagiannidis, G.K., Big Data Analytics for Dynamic Energy Management in Smart Grids, Intl Big Data Research (Elsevier), Pg: 94-101, Vol. 2, September 01 2015
[16] Paakkonen, P., Pakkala, D., Reference Architecture and Classification of Technologies, Products and Services for Big Data Systems, Intl Big Data Research (Elsevier), Pg: 166-186, Vol. 2, December 01 2015
[17] Bong-Hwa-Hong and Hae-Jong Joo, A Study on The Monitoring Model for Traffic Analysis and Application of Big Data, Intl Research on Big Data (Elsevier), Pg.- 30-35, Vol. 43, 2013
[18] Mikin K. Dagli and Brijesh B. Mehta, Big Data and Hadoop: Review, Intl research on Big Data (Elsevier), Pg.-192-196, Vol.2, February 2014
[19] Thusoo, Ashish, JoydeepSenSarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu, and Raghotham Murthy. "Hive-a petabyte scale data warehouse using hadoop." In Data Engineering (ICDE), 2010 IEEE 26th International Conference on, pp. 996-1005. IEEE, 2010
[20] Olston, Christopher, Benjamin Reed, UtkarshSrivastava, Ravi Kumar, and Andrew Tomkins. "Pig latin: a not-so-foreign language for data processing." In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pp. 1099-1110. ACM, 2008
[21] Gates, Alan F., Olga Natkovich, Shubham Chopra, PradeepKamath, Shravan M. Narayanamurthy, Christopher Olston, Benjamin Reed, SanthoshSrinivasan, and UtkarshSrivastava. "Building a high-level dataflow system on top of Map-Reduce: the Pig experience." Proceedings of the VLDB Endowment 2, no. 2 (2009): 1414-1425

C. Jasmine, A. Abinaya, "Comparative Analysis of Big Data Technologies," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.49-57, 2019.

The enhancement of life time in a WSN mainly focus on the clustering and energy of nodes. The concern to select the cluster head in a network works on different techniques, the region based energy efficient technique for the data communication among nodes is one of them in a wireless sensor network. This paper focus on the region based called gateway based energy-efficient routing protocol. This paper mainly focus on distance based cluster head selection and the communication among region nodes with the base station depends upon the positive coordinates of the region following the base station.

Gateway node, network region, cluster heads, base station

[1] Preeti Jamwal1, Sonam Mahajan2, “Region Refinement Technique In MGEAR Protocol To Enhancing Sensor Node Life Time”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-8 ,2018.
[2] Nazia anjum, Maood ahmed et al,”, Gateway Based Energy Efficient Routing: GEER”, International Journal of Advance Research, Ideas and Innovations in Technology, Volume3, Issue4, 2017.
[3] Q. Nadeem1, M. B. Rasheed1 et al, “M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs”, ieee July 2016.
[4] Veena Anand , Deepika Agrawal, Preety Tirkeyb, Sudhakar Pandey, “An energy efficient approach to extend network life time of wireless sensor networks”, Elsevier, Procedia Computer Science, 425 – 430, 2016.
[5] Pallavi Jain1 and Harminder kaur2, ” An Improved Gateway Based Multi Hop Routing Protocol for Wireless Sensor Network”, International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 15,pp. 1567-1574, 2014.
[6] Pijus Kumar Pal1, Punyasha Chatterjee2, “ A Survey on TDMA-based MAC Protocols for Wireless Sensor Network”, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 6, June 2014.
[7] Velanati Mohana Gandhi1, M.V.H.Bhaskara Murthy2,M.Lakshmu Naidu3, “ Performance Analysis of Multihop-Gateway Energy Aware Routing (M-Gear) Protocol for Wireless Sensor Networks”, IOSR Journal Of Humanities And Social Science, Volume 21, Issue11,Ver. 9 Nov. 2016.
[8] Shakshi Mehta et al “ Improved Multi-Hop Routing Protocol in Wireless Body Area Networks “, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 7, July 2015.
[9] S. Rani and S.H. Ahmed, Multi-hop Routing in Wireless Sensor Networks, Springer Briefs in Electrical and Computer Engineering.
[10] Jung, W. S., Lim, K. W., Ko, Y. B., & Park, S. J. “A hybrid approach for clustering-based data aggregation in wireless sensor networks”, In Digital Society, IEEE, Third International Conference on, 2009.
[11] Li, Hongjuan, Kai Lin, and Keqiu Li. "Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks." Computer Communications, 2011.
[12] Sujata1, Brijbhushan2,” Energy Efficient PEGASIS Routing Protocol in Wireless Sensor Network”, International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 07 | July-2017.
[13] Jaswant Singh Raghuwanshi,2Neelesh Gupta,3Neetu Sharma, “ENERGY FFICIENT DATA COMMUNICATION APPROACH IN WIRELESS SENSOR NETWORKS”, International Journal of Advanced Smart Sensor Network Systems (IJASSN), Vol 4, No.3, July 2014.

Amandeep Kaur, Sukhbeer Singh, Neelam Chouhan, "The Enhanced M-GEAR Protocol for Wireless Sensor Network LifeTime," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.58-60, 2019.

Data mining research extends its wings to several domains and classification is one of the thrust areas for researchers. The curse of dimensionality is reduced by many optimization techniques and machine learning algorithms. In this research work, a particle swarm optimization based feature selection method is employed to deal with the curse of dimensionality. The PSO algorithm makes use of the fitness function that is obtained from the evolutionary outlay aware deep belief network which conducts classification. 20 datasets are taken for evaluating the conductance of the PSO – EOA – DBNC in terms of classification accuracy and elapsed time. From the results it is significant to notice that PSO-EOA-DBNC out conducts than that of other classifiers.

data mining, feature selection, particle swarm optimization, deep belief network, evolutionary algorithm.

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[14] M.Praveena, Dr.V.Jaiganesh, “Routine Correspondence Method with Grey Wolf Optimization based Imperforate Support Vector Machine Classifier (ISVMC) for High Dimensional Datasets”, Journal of Advanced Research in Dynamical & Control Systems, Vol. 11, 01-Special Issue, 2019, 652-660.
[15] M.Praveena, Dr.V.Jaiganesh, “Adaptive Particle Swarm Optimization based Credentialed Extreme Learning Machine Classifier (APSO-CELMC) for High Dimensional Datasets”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8, Issue-10S, August 2019.
[16] M.Praveena, Dr.V.Jaiganesh, “A Literature Review on Supervised Machine Learning Algorithms and Boosting Process”, International Journal of Computer Applications (0975 – 8887) Volume 169 – No.8, July 2017.

M. Praveena, V. Jaiganesh, "Particle Swarm Optimization based Feature Selection with Evolutionary Outlay-Aware Deep Belief Network Classifier (PSO-EOA-DBNC) for High Dimensional Datasets," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.61-69, 2019.

Vitiligo is growing at a good speed among the population and people have to go repeated surgeries to get rid of this disease. Though it’s not easy to define the stability, but it`s indispensable in the treatment of vitiligo. There have been many cases where people had gone for skin replacement surgery, but after sometime, white patches redeveloped on the skin. So the treatment goes on forever and patients get disheartened. The aim is to help people to identify the saturation of the disease before seeking the remedy which is skin transplantation. In this paper, improved J48 algorithm is used to predict the stability of vitiligo which gives optimal results. This algorithm uses the medical history of patients, Koebner phenomenon and VIDA score of sample data to feed into the systems and draw patterns to predict stability in the patients. We use various algorithms of data mining to extract useful information from data and check the accuracy of their medical history. The data includes the vitiligo patients, healthy people, the ones who have undergone surgery and the patients who haven’t undergone skin replacement and are still experiencing growth in their patches. With the prediction of various parameters, an optimal target value is predicted. In the end, we conclude with the most optimal algorithm which can be used to determine the stability of this disease and help the doctors and patients to determine the precise time of surgery.

J48 algorithm, Vitiligo, White patches, Patch development, data mining, prediction

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Gagandeep Singh, Kavita Rathi, "Applications of data mining in predicting the stability of Vitiligo," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.70-73, 2019.

An advanced vehicle scheduling, monitoring and tracking system is to be designed and implemented for booking and monitoring the vehicles from one location to other location in real time and provide safety environment to the traveller. The proposed system has to use GPS/GPRS/GSM Modules which includes all the three things namely GPS GPRS GSM. The GPS provides current location of the vehicle; GPRS sends the tracking information to the server and the GSM is used for sending alert message to vehicle allotted person mobile. The booking of vehicle is done through priority scheduling algorithm The proposed system would place inside the vehicle whose position is to be determined on the web page and monitored in real time. The system will compare the current trajectory of the vehicle with the predefined path specified. If any deviation is observed, it will send an alert message to the related person. The cost analysis is done and cost is estimated based on the distance travelled.

Arduino, GPS/GPRS/GSM, python, priority scheduling algorithm, cost estimation

[1]. R. Surender Reddy, M. Hymavathi, E. Shilpa. Real Time Vehicle Monitoring and Tracking System based on Embedded Linux Board -Vol. 4, Issue 10, October 2016
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[3]. Omar Abdulwahabe Mohamad , Rasha Talal Hameed. Design and Development of Real time Vehicle Tracking System- -2016 8th ECAI
[4]. Kunal Maurya Mandeep Singh Neelu Jain Real time vehicle tracking system using GSM and GPS technology- an anti-theft tracking system, IJECSE,Volume1,Number 3
[5]. Pankaj Verma , J.S Bhatia .Design and development of GPS-GSM based tracking system with google map based monitoring, International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.3, No.3, June 2013
[6]. SeokJu Lee, Girma Tewolde, Jaerock Kwon. Design and implementation of vehicle tracking system using GPS/GSM technology and smartphone application. Internet of Things (WF-IoT), IEEE World Forum. 6-8 March 2014

T.Swetha Reddy, K.Venkateswara Rao, Pullanna Satri, "Real Time System for Vehicle Scheduling, Tracking and Monitoring and Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.74-78, 2019.

Application of Business intelligence produces information that can be used as decision support. Companies in the E-Commerce require business intelligence applications to manage existing data to be useful. Manager using this information for decision in strategy or staff for operational support. In this study, researchers created a business intelligence dashboard application that can be used as a reference for e-Commerce companies in processing raw data to become useful information for decision. Development of business intelligence application based on business intelligence architecture. The business intelligence architecture consists of data source, ETL, data warehouse, Dashboard and BI users. Business Intelligence Application is created using Dashboard to display information. The results of this study are business intelligence dashboard application which has four dashboard options for e-Commerce companies. The summary dashboard displays a summary of information. The customer dashboard displays information based on the customer. The product dashboard displays information based on the product. Transaction dashboard displays information based on transaction. Module testing on dashboard is done using black box testing, the test results on four dashboards in the application, can function correctly.

Business Intelligence, Dashboard, Decision, E-Commerce

[1] N. Fatma, S. Jain, and M.A. Alam, “Visualisation and Analysis of Big Data through Business Intelligence”, IJCSE, Vol.7, Issue.4, pp.629-636, 2019.
[2] E.N.S. Yuliani, H. Subawanto, and A. Oktaviani, “Business Intelligence Dashboard Implementation on a Travel Agency in Jakarta”, IJAERS, Vol.4, Issue.6, pp.63-68, 2017.
[3] B. Hansoti, “Business Intelligence Dashboard in Decision Making”, Purdue University, Indiana, 2010.
[4] Kendall and Kendall, "System Analysis and Design 8th edition, 2010.
[5] R. Sherman, “Business intelligence guidebook : from data integration to analytics”, Elsevier, USA, 2015.
[6] I.L. Ong, P.H. Siew, and S.F. Wong, “A Five-Layered Business Intelligence Architecture”, Communications of the IBIMA, Vol.2011, pp.1-11.

Muhammad Naufal Prakoso, Nuryuliani, "Application Development of E-Commerce Business Intelligence Dashboard," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.79-83, 2019.

This paper the detection from the phishing web site and URLs. The aim is to realize the detection of URLs and websites. The technique will be classified to understand the spoofing attack and also the phishing techniques and techniques as follows the random forest and decision tree. Phishing detection strategies do endure low detection accuracy and high warning particularly once novel phishing methodologies are introduced. The best mutual technique used random forest and decision tree by that has to seek out the accuracy of the phishing dataset. These two strategies, have to seek out the accuracy of the real and faux phishing web site dataset.

random forest, decision tree, phish tank, confusion matrix, dataset

[1] Padmawati Soni, Dr. Mahesh Pawar, Dr. Sachin Goyal, A Survey on detection and defense from phishing.
[2] Mahmoud khon, Andrew jones, Phishing detection: A literature Survey.
[3] phish tank http://www.phishtank.com/what_is_phishing.php.
[4] Cybersecurity, Nina Godbole, Sunit Belapure foreword by Dr.Kamlesh Bajaj, Data Security Council of India.
[5] APWG can be visited at http://www.antiphishing.org/reports/apwg_report_Q4_2009.pdf
[6] A.-P.W.G 2010. Global phishing survey: Domain name use and trends in 2h2010.
[7] SHREE RAM, V., SUBAN, M., SHANTHI, P.andMANJULA, K. Anti-phishing detection of phishing attacks using a genetic algorithm. Communication Control and Computing Technologies (ICCCCT), 2010 IEEE International Conference on, 2010. IEEE, 447-450.

Padmawati Soni, Mahesh Pawar, Sachin Goyal, "Detecting the Phishing sites by using Machine Learning with Random Forest and Decision tree," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.84-87, 2019.

In todays’ life, the problems faced by the visually impaired persons are increases due to the huge growth in urbanization in cities. Even a normal person also gets confused, if they come across to the new locations. To handle this problem, in this paper, we have proposed a new robust system, which provide help to user while navigating in big industrial buildings. This system uses BLE (bluetooth low energy) devices to communicate with the hardware present at user and then it will direct the route to the user. The process includes user interaction through voice for the input location after that system will find desired location of user by connecting the hardware to various BLE devices and depending upon the signal strengths from each BLE user will be get navigated. If the range of BLE devices get less than that means, that user is going away from that BLE device and similarly if the range of particular device is getting increase then it means that user going towards the BLE device. Now to get accurate result we are implementing Three Dimensional Triangulation Technique where the hardware present at user will simultaneously connect with multiple BLE devices and then find the required route for navigation. Along with this we are providing IR(infra-red) SONAR sensors through which we can find any obstacle that comes between the user and its navigation. We have added buzzer and LED lights to notify the obstacle to others.

Indoor navigation, BLE beacons technology for triangulation. Blind navigation, wayfinding, robotic navigation aid, pose estimation

[1] He Zhang ; Cang Ye, "An Indoor Wayfinding System Based on Geometric Features Aided Graph SLAM for the Visually Impaired" IEEE Transactions on Neural Systems and Rehabilitation Engineering (Volume: 25 , Issue: 9 , Sept. 2017 )
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[6] C. Ye, S. Hong, and A. Tamjidi, “6-DOF pose estimation of a robotic navigation aid by tracking visual and geometric features,” IEEE Trans. Autom. Sci. Eng., vol. 12, no. 4, pp. 1169-1180, Oct. 2015.
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Kalyani Mule, J. V. Shinde, "Smart Approach for Finding Indoor Navigation Using BLE for Visually Impaired Person," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.88-93, 2019.

Securing Cloud data is a major task in current ongoing world. Now a days Cloud playing a major role in every technical aspect, for data storage. Here secure data management in Cloud is the major challenge to achieve. Attribute based encryption (ABE) is the most commonly used algorithm for circulated registering, where a data provider redistributes the data that is encoded, to a cloud master association, and can grant the data to customers having express accreditations (or qualities). Regardless, the standard ABE structure doesn’t reinforce secure Deduplication, the basic rule for discarding multiple copies of undefined data to save additional room and framework information move limit. Here a trademark based limit structure is presented with checked duplication in a cream cloud setting, where a private cloud is responsible for duplicate disclosure and an open cloud manages the limit. Differentiating the previous systems which support data deduplication, our structure has bi-ideal conditions. Generally it might be used in a rapid manner to secretly give data to customers by choosing access plans as opposed to sharing translating keys. However, it is very helpful in acquiring the thought of semantic security which follows standard mechanism for data protection while the previous mechanism just simply achieved it by describing a flimsier security thought. Also, we put forward a framework to change a figure message more than one access system into figure works of the identical plaintext yet under various access courses of action without revealing the major plaintext.

ABE, Storage, Deduplication

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A. Prasannakumar Reddy, M. Vikram, N. Sudhakar Reddy, "Securing Cloud Data by Using Multi Keyword Search System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.94-98, 2019.

Over a last few decade’s voltage instability is a concern phenomenon in power industry. The main cause of voltage instability is the deficit of reactive power in the system and the Active Power losses due to increased inductive reactance of transmission line. Flexible AC transmission system (FACTS) devices can improve the line losses by varying inductive reactance there by increasing Active Power in the system and controlling reactive power by Facts devices which tends to control voltage within the specific limit. In this paper the work is proposed with the help of IEEE 14 bus system in Power World Simulator and comparison have been made in MATLAB. The index of voltage stability Fast Voltage Stability Index (FVSI) gives information about the line which is prone to voltage collapse. At last the location and rating of SVC and TCSC are optimized with help of Particle Swarm Optimization Algorithm to get the voltage within a specific range at all the load buses.

FACTS devices, Fast voltage stability index, Power World Simulator , SVC, TCSC, Particle Swarm Optimization

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Rahul Pradip, Chetan D. Kotwal, Shital M. Pujara, "Particle Swarm Optimized Voltage Stability Analysis Of IEEE 14 Bus System with SVC and TCSC," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.99-115, 2019.