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There are lots of disease evolving currently due to change in lifestyle, food habits and sleeping habits and there is a lack of technology to identity these. Disease identification using manual checkups is an accurate way but it consumes a lot of time so we need an alternative that performs diseases diagnosis quick and accurate, this leads to need for data analytics and machine learning. Data analytics we analyze the user data and provide insights to the user. We use machine learning techniques to analyze user data and supervised algorithm such as SVM and unsupervised algorithm such as K-Means clustering are used for classification of the datasets .Random forest is used to create decision trees using user data and important data can be extracted from the decision tree.

Support vector machine (SVM), Random Forest(RF).

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[3] Jyoti Soni, Ujma Ansari, Dipesh Sharma, Sunita Soni Predictive data mining for medical diagnosis: an overview of heart disease prediction International Journal of Computer Science and Engineering, vol. 3 ,2011.
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Ajay Kumar, Kamaleshwar M, Sanjay Kumar K, Sanjith Kumaar R S, Arunnehru J, "An Improved Disease Prediction System Using Machine Learning," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.81-85, 2018.

Multitasking or multiprocessing is the need of today’s modern embedded system. Multitasking can reach the desired increase in performance as needed by today’s embedded systems. Multitasking can be achieved by scheduling the competing tasks and allocate them to the shared resources one by one at very high speed (so fast and frequently) so as to create an illusion that all tasks are running in parallel. There are various algorithms to implement a scheduling scheme depending upon the need and constraints of the application. The proposed research work aims at evaluating and comparing the performance of Round Robin and Static Priority Scheduling algorithm by implementing them the on single Microblaze soft core processor and porting xilkernel on FPGA platform.

Microblaze, Xilkerenel, Scheduling, FPGA

[1] Mohammad T. Kawser, Hasib M. A. B. Farid, Abduhu R. Hasin, Adil M. J. Sadik, and Ibrahim K. Razu “Performance Comparison between Round Robin and Proportional Fair Scheduling Methods for LTE” , International Journal of Information and Electronics Engineering, Vol. 2, No. 5, September 2012
[2] Ms. Rukhsar Khan, Mr. Gaurav Kakhani “Analysis of Priority Scheduling Algorithm on the Basis of FCFS & SJF for Similar Priority Jobs” International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 4, Issue. 9, September 2015, pg.324 – 331
[3] Alban Allkoci, Elona Dhima, Igli Tafa “Comparing Priority and Round Robin Scheduling Algorithms” IJCSI International Journal of Computer Science Issues, Vol. 11, Issue 3, No 1, May 2014
[4] E.O. Oyetunji and A. E. Oluleye, “Performance Assessment of Some CPU Scheduling Algorithms”, Res. J. Inform. Technol., 1(1): 22-26, 2009, ISSN: 2041-3114, Aug 2009.
[5] Lalit Kishor ,Dinesh Goyal , “Time Quantum Based Improved Scheduling Algorithm ” International Journal of Advanced Research in Computer Science and Software Engineering
[6] Abdulrazaq Abdulrahim ,Saleh E Abdullahi ,Junaidu B. Sahalu “ A New Improved Round Robin (NIRR) CPU Scheduling Algorithm International Journal of Computer Applications (0975 – 8887) Volume 90 – No 4, March 2014
[7] Sharmik V Admane, Jitendra B Zalke, Manisha Das,” Implementation of Speed Efficient Image Processing algorithm on Multi-Processor System on Chip (MPSoC)”, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763 Issue 01, Volume 4 (January 2017)
[8] Tong, J.G. Anderson, I.D.L., Khalid, “Soft-Core Processors for Embedded Systems” M.A.S., Microelectronics, 2006. ICM `06. International Conference (IEEE)
[9] Dirk Koch, Frank Hannig, Daniel Ziener (eds.) “FPGAs for Software Programmers” Springer International Publishing (2016)
[10] David E. Simon-“An Embedded Software Primer” Addison Wesley (1999), pearson education D. David.Simon
[11] MicroBlaze Processor Reference Guide Embedded Development Kit.
[12] xilkernel Reference Guide Embedded Development Kit.

Manisha Das, S.V.Admane, Jitendra B. Zalke, "Performance Estimation of Scheduling Algorithms on Microblaze Softcore Processor," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.86-89, 2018.

Wireless Sensor Networks (WSN’s) are decentralized type of networks used in sensing and processing physical data without any pre-existing infrastructure. WSN consists of sensing nodes that can interact among themselves and with the physical environment for sensing, measuring and controlling various parameters. For effective and efficient transmission of information in a seamless manner, the choice of routing protocol is still a major constraint in WSN design. In this paper, Reactive Routing protocols using IEEE 802.15.4 application have been evaluated on the designed network scenario. The parameters we have used are (i) Throughput (ii) AEED (iii) Number of Packets Forwarded (iv) Number of Packets Dropped as performance metrices in both Static and Mobile environment for 50, 75 and 100 nodes.

Ad-hoc On-Demand Distance Vector (AODV), Dynamic MANET On-demand (DYMO), Dynamic Source Routing (DSR), Ad Hoc On-Demand Multipath Distance Vector (AOMDV) ), Unipath Routing Protocols (URP)

[1] A. Mallikarjuna V. Reddy, A.V.U. Phani Kumar, D. Janakiram, G. Ashok Kumar, “ Wireless sensor network operating systems: a survey”. International Journal of Sensor Networks 2009 -Vol. 5, No.4 pp. 236 - 255 doi 10.1504/IJSNET.2009.027631.
[2] K Mor, S. Kumar, &D. Sharma, “Ad-Hoc Wireless Sensor Network Based on IEEE 802.15.4: Theoretical Review”. International Journal of Computer Sciences and Engineering. Vol.6. Issue 3, pp (219-224) March 2018. ISSN 2347-2693.doi: 10.26438/ijcse/v6i3.219224.
[3] Mohamed Younis, Izzet F. Senturk, Kemal Akkaya, Sookyoung Lee and Fatih Senel, “Topology management techniques for tolerating node failures in Wireless Sensor Networks: A Survey” Computer Networks, Volume 58, January 2014, pages 254-283, https://doi.org/10.1016/j.comnet.2013.08.021
[4] Mandeep Kaur Gulati, Krishan Kumar, “QoS routing protocols for mobile ad hoc networks: a survey” International Journal of Wireless and Mobile Computing 2012 - Vol. 5, No.2 pp. 107 - 118 doi 10.1504/IJWMC.2012.046783
[5] Mohammed Abazeed, Norshiela Faisal, Suleiman Zubair, and Adel Ali, “Routing Protocols for Wireless Multimedia Sensor Network: A Survey” Journal of Sensors Volume 2013 (2013), Article ID 469824, 11 pages http://dx.doi.org/10.1155/2013/469824
[6] Shailendra Aswale and Vijay R. Ghorpade, “Survey of QoS Routing Protocols in Wireless Multimedia Sensor Networks Hindawi Publishing Corporation Journal of Computer Networks and Communications Volume 2015, Article ID 824619, 29 pages http://dx.doi.org/10.1155/2015/824619
[7] Guangjie Han, Xu Jiang, Aihua Qian, Joel J. P. C. Rodrigues, and Long Cheng, “A Comparative Study of Routing Protocols of Heterogeneous Wireless Sensor Networks” The Scientific World Journal, Volume 2014 (2014), Article ID 415415, 11 pages http://dx.doi.org/10.1155/2014/415415
[8] Pitchaipillai, Periyasamy & Eswaramurthy, Karthikeyan, “Impact of Variation in Pause Time and Network Load in AODV and AOMDV Protocols”. International Journal of Information Technology and Computer Science.Vol.4 Issue 3, pp(38-44) ISSN: 2074-9015 April,2012.doi: 10.5815/ijitcs.2012.03.06.
[9] R. Paulus, P.D. Kumar, P.C. Philiips , A. Kumar “Performance Analysis of Various Ad Hoc Routing Protocols in MANET using Variation in Pause Time and Mobility Speed”, International Journal of Computer Applications Vol.73, Issue 8,pp( 35-39) July2013. ISSN:0975-8887. Doi: 10.5120/12764-9736.
[10] P.K. Maurya, R. Paulus, A K Jaiswal and M Srivastva. “Performance Analysis of ZRP over AODV, DSR and DYMO for MANET under Various Network Conditions using QualNet Simulator”. International Journal of Computer Applications Vol.66,Issue 17 pp(31-35), March 2013. ISSN:0975-8887.doi: 10.5120/11177-6340
[11] Waheb A. Jabbar, Mahamod Ismail and Rosdiadee Nordin “On the Performance of the Current MANET Routing Protocols for VoIP, HTTP, and FTP Applications” Hindawi Publishing Corporation Journal of Computer Networks and Communications Volume 2014, Article ID 154983, 16 pages http://dx.doi.org/10.1155/2014/154983.
[12] San San Naing, Zaw Min Naing, Hla Myo Tun “Performance Of Routing Protocols For Mobile Ad Hoc Networks” International Journal Of Scientific & Technology Research Volume 3, Issue 5, May 2014 ISSN 2277-8616.
[13] P. Khandnor, T.C. Aseri, “ Performance analysis of routing protocols in mobile wireless sensor network” International Journal of Information and Communication Technology”, Volume 8, Issue 2/3, February 2016 doi 10.1504/IJICT.2016.074846.
[14] K. Mor & S. Kumar, “Evaluation of QoS Metrics in Ad-Hoc Wireless Sensor Networks using Zigbee”. International Journal of Computer Sciences and Engineering. Vol.6. Issue 3, pp (90-94) March 2018. ISSN 2347-2693.doi 10.26438/ijcse/v6i3.9094.

Payal, D Sharma, S Kumar, "Performance Evaluation of Reactive Routing Protocols Using IEEE 802.15.4 Application in Designed Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.90-96, 2018.

Before proposing a new model and implementing it in the Intrusion Detection Systems, First find out how intrusion detection is performed on Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) offerings, along with the available host, network and hypervisor-based intrusion detection options. The ability to perform intrusion detection in the cloud is heavily dependent on the model of cloud computing. In cloud computing, most of the attacks till today traced are the remote attacks. In this paper, we are proposing a model for Cloud Based Intruder Detection System [CBIDS]. This model is created for tracing the attacks on the online storage at SaaS and PaaS layer of cloud computing and appropriately the recommended action will be taken to protect the stored data and executing the handler accordingly. Further modifications in this model will be done on the basis of obtaining requirements and gaps in tracing the attacks.

Cloud, Intruder, IDS, Intrusion Detection System, HIDS, NIDS, Attack.

[1] S 1. S. Akter, S.F.Wamba, A. Gunasekaran , R. Dubey, S. J.Childe, “How to improve firm performance using big data analytics capability and business strategy alignment?”, Elsevier, Int. J. Production Economics 182 (2016), pp.113–131
[2] 2. C. Saadi, H. Chaoui, “Cloud Computing Security Using IDS-AM-Clust, Honeyd, Honeywall and Honeycomb”, International Conference on Computational Modeling and Security (CMS 2016), pp.433-442
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[13] 13. J. Khan, H. Abbasa, J. Al-Muhtadia, “Survey on Mobile User`s Data Privacy Threats and Defense Mechanisms”, International Workshop on Cyber Security and Digital Investigation (CSDI 2015), Elsevier, Procedia Computer Science 56 ( 2015 ), pp. 376 – 383
[14] 14. Al Haddad Zayed, H. Mostafa, M. Abdelaziz, B.Youness, “Hybrid Intrusion Detection Systems (HIDS) in Cloud Computing: Challenges and Opportunities”, IJEECSE, Volume 3, Issue 3 (June, 2016) | E-ISSN : 2348-2273, pp. 7-13
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[17] 17. Oktay and O.K. Sahingoz, “Attack Types and Intrusion Detection Systems in Cloud Computing”, 6th international information security & cryptology conference, Sep 2013, pp 71-76
[18] 18. C. Ambikavathi and S. K. Srivatsa, “Integrated Intrusion Detection Approach for Cloud Computing”, Indian Journal of Science and Technology, Vol 9(22), DOI: 10.17485/ijst/2016/v9i22/95170, June 2016, ISSN (Online) : 0974-5645.
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A.K. Chaturvedi, F.A. Lone, "Proposing Cloud Based Intrusion Detection System for Tracing Intruder Attacks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.97-104, 2018.

Feature model`s analysis is a booming area and should be automated is a thriving research topic, an area of attraction for both practitioners and researchers from last two decades. Meanwhile, a number of methods and tools facilitate to increase the analysis of feature models and also check complexity of feature model. As numerous of tools are given by researchers and practitioners, but why a tool is used and for which purpose, it`s a basic problem and creates a blurry scenario and this blurriness generate hurdles to select an analyzing tool to analyze a feature model. To clear this picture, we present a paper, where we compare four analysis tools (FeatureIDE, SPLOT, FaMa and BeTTy) on the basis of some fundamental factors (Availability of Tool, Cross Tree Constraint, Support Testing, Fault Detection, Product Generation, Statistics of Model and Model Composer). The comparison will show in form of a table at the end of this paper, through which users can choose a tool for their work.

Software product line, Feature models, Automated analysis, Testing, Validation

[1] K. Satendra, Rajkumar, "Test Case Prioritization Techniques for Software Product Line: A Survey" International Conference on Computing, Communication, and Automation (ICCCA), 2016.
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[4] S.P.L.O.T.: Software Product Lines Online Tools.http://www.splot-research.org.
[5] S. Segura. “Functional and Performance Testing of Feature Model Analysis Tools Extending the FaMa
Ecosystem”, Ph.D. thesis, Dept. of Computer Languages and Systems, University of Seville, 2011.
[6] T. Thomas, M. Jens, FeatureIDE: Overview October 23, 2015.
[7] B. David, S. Sergio, T. Pablo R.C. Antonio, “FAMA: Tooling a Framework for the Automated Analysis of Feature Models”.
[8] B. Don, B. David, R.C. Antino, “Automated analysis of feature models: Challenges ahead”, Communications of the ACM, December 2006.
[9] FaMa Tool Suite. http://www.isa.us.es/fama.
[10] Mendonça, M., Branco, M., and Cowan, D. (2009). “S.p.l.o.t.: Software product lines online tools”. In 24th Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), pages 761–762.
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on Software Engineering, pages 254-264, 2009.
[12] S.Sergio, G. José A., B. David, P. José A. and R.C. Antonio, “BeTTy: Benchmarking and Testing on the Automated Analysis of Feature Models”, VAMOS ’12, January 25-27, 2012 Leipzig, Germany.
[13] S. Kumar and Rajkumar, “Cost-Based Test Case Prioritization Technique for Software Product Line,” Int. J. Sci. Prog. Res., vol. 40, no. 115, 2017.

A. Saini, Rajkumar, S. Kumar, "Software Product Line Configurations Generation using Different Types of Tools – A Comparison," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.105-109, 2018.

Ransomware is a malware that either encrypts files with specific extension on the system or locks the user out of the system demanding for the ransom in exchange of decryption key. The approach used here is to assess numerous aspects of ransomware so as to comprehend different techniques utilized by it. Ransomware has rapidly affected individuals, public and private organizations across the globe. This occurs due to system flaws and lack of recovery mechanisms. The challenging part is to recover vital data from the encrypted files. This has created severe security issues to companies of all sizes as several have lost valuable data and business proprietary information. Considering the above information, this research paper aims at examining the characteristics of a Microsoft Windows-based ransomware and potential recovery of encrypted files from the ransomware affected system. The sample was examined in an isolated environment using static and dynamic analysis techniques with open source tools. The results were encouraging as we were able to recover encrypted files with specific extensions.

Vipasana Ransomware, Ransomware Forensics, Ransomware Analysis, Offline Ransomware, Static Analysis, Dynamic Analysis

[1] A. Bhardwaj, V. Avasthi, H. Sastry, G. V. B. Subrahmanyam, “Ransomware Digital Extortion: A Rising New Age Threat”, Indian Journal of Science and Technology, Vol.9, Issue.14, 2016.
[2] A. Ali, “Ransomware: a research and a personal case study of dealing with this nasty malware”, Issues in Informing Science and Information Technology, Vol.14, pp.087-099, 2017.
[3] C. Everett, “Ransomware: to Pay or Not to Pay?” Computer Fraud & Security, Vol.2016, Issue.4, pp.8–12, 2016.
[4] A. Kharraz, W. Robertson, D. Balzarotti, L. Bilge, E. Kirda, “Cutting the gordian knot: A look under the hood of ransomware attacks”, In: M. Almgren, V. Gulisano, F. Maggi (eds) Detection of Intrusions and Malware, and Vulnerability Assessment. DIMVA 2015. Lecture Notes in Computer Science, Springer, Cham, Vol.9148, pp. 3-24, 2015.
[5] S. Homayoun, A. Dehghantanha, M. Ahmadzadeh, S. Hashemi, R. Khayami, “Know abnormal, find evil: frequent pattern mining for ransomware threat hunting and intelligence”, IEEE Transactions on Emerging Topics in Computing, pp. 1–1, 2017.
[6] E. Kirda, “UNVEIL: a large-scale, automated approach to detecting ransomware (keynote)”, In Software Analysis, Evolution and Reengineering (SANER-2017) IEEE 24th International Conference, pp.1-1, 2017.
[7] K. Cabaj, M. Gregorczyk, W. Mazurczyk, “Software-Defined Networking-Based Crypto Ransomware Detection Using HTTP Traffic Characteristics”, Computers & Electrical Engineering, Vol.66, pp.353–368, 2018.
[8] J. K. Lee, S. Y. Moon, J. H. Park, “CloudRPS: a Cloud Analysis Based Enhanced Ransomware Prevention System”, The Journal of Supercomputing, Vol.73, Issue.7, pp.3065–3084, 2017.
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[10] V. U. Bala, B.D.C.N.Prasad "A Study on- Identifying and Evading Ransomware (Ransomware)", SSRG International Journal of Computer Science and Engineering (SSRG - IJCSE), Vol.5, Issue.2,pp.9-13, 2018
[11] S. Mohurle, M. Patil, “A brief study of wannacry threat: Ransomware attack 2017”, International Journal, Vol.8, Issue.5, pp.1938-1940, 2017.
[12] “North Korea Blamed for WannaCry, PoS Attacks and Bitcoin Phishing”, Network Security, Vol.2018, Issue.1, pp.1-2, 2018.
[13] J. MacRae, V.N.L. Franqueira, “On Locky Ransomware, Al Capone and Brexit,” In: P. Matoušek, M. Schmiedecker (eds) Digital Forensics and Cyber Crime (ICDF2C 2017) Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer, Cham, Vol.216, pp.33-45, 2017.
[14] P. P. Kulkarni, T. Nafis, S.S. Biswas, “Preventive Measures and Incident Response for Locky Ransomware”, International Journal of Advanced Research in Computer Science, Vol.8, Issue.5, 2017.
[15] A. Zahra, M.A. Shah “IoT Based Ransomware Growth Rate Evaluation and Detection Using Command and Control Blacklisting”, In proceeding of 23rd International Conference on Automation and Computing (ICAC- 2017), pp.1-6, 2017.
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Francis Byabazaire, Parag H. Rughani, "Comprehensive Analysis and Forensic Recovery of Vipasana Ransomware," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.110-117, 2018.

The importance of requirements elicitation has been well recognized by the software development community. Clear and correct user requirement is critical to the success of software systems. There are numbers of requirements elicitation techniques available, which are used to understand and gather user requirements. In this paper, we have tried to analyze and evaluate the performance of different requirement elicitation techniques, which are under the categories of conversational, observational, analytic and synthetic methods of requirements elicitation. This study was performed in Ethiopia on analyst and requirement engineers working in various software development companies, staffs and senior students from University of Gondar who are involved in software development activities and bank employees who are involved in similar activities in their respective banks. In our study, we found that the requirement engineers prefer to use combination of different requirement elicitation methods. Interviews technique is the most preferred requirements elicitation technique in software development community followed by observation method.

Requirement elicitation, Conversational, Observational, Analytic, Synthetic

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[4] S. J. Kalayathankal1, J. T. Abraham, J. V. Kureethara, “An Intuitionistic Fuzzy Soft Software Life Cycle Model”, International Journal of Computer Sciences and Engineering, Vol. 6, Issue. 1, pp. 42-48, 2018.
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Theodros Tiruneh, Manish Kumar Mishra, "Analysis and Performance Evaluation of Requirement Elicitation Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.118-123, 2018.

Digital image processing is the usage of computer algorithms for the analysis and manipulation of images. This work emphasis local thresholding technique for the segmentation of characters in palm leaf manuscript images. The preprocessing stage comprises of filtering and image enhancement. The filtering of noise was done by decision based median filter and contrast local adaptive histogram equalization was applied for enhancement. For segmentation, Otsu global thresholding and local thresholding techniques like Niblack, Sauvola and Bernsen algorithms were evaluated. The Sauvola local thresholding generates more efficient results than the global thresholding and other local thresholding techniques. The computational complexity of Sauvola thresholding is considerably low and the performance of thresholding techniques was evaluated by entropy measure. The Sauvola thresholding resultant image has low entropy value when compared with other thresholding techniques. The algorithms were developed in Matlab 2010a and evaluated on the real-time images acquired by canon SX600HS camera.

Palm leaf manuscript; Decision-based median filter; CLAHE; thresholding; Shannon entropy

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[2] H. K. Anasuya Devi” Thresholding: A pixel-level image processing methodology preprocessing technique for an OCR System for the Brahmi Script” Ancient Asia. 1, pp.161–165
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A. Lenin Fred, S.N. Kumar, Ajay Kumar H, Ashy V Daniel, W. Abisha, "Evaluation of local thresholding techniques in Palm-leaf Manuscript images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.124-131, 2018.

Prolego aims to predict results of Premier League football matches accurately by applying machine learning techniques to historical data. The historical data consists of rows where each row consists of several statistics for both the Home Team and the Away Team. The historical data is generated using web scraping libraries such as Selenium and BeautifulSoup. Based on the scraped data, data cleaning and feature engineering is done to generate several features of a football match like Shots, Shots On Target, Possession, Tackles, Corners, Ratting etc. Finally, the features are represented in a vector format and fed as inputs to different Machine Learning classifier algorithms like Multinomial Logistic Regression, SVM, Gradient Boosting Classifier and DecisionTreeClassifier. After the classification, accuracy is measured by calculating percentage of correct predictions and percentage of correct draw predictions. Error analysis is performed using techniques like Region under Curve to tune hyperparameters and identify the features which are more prominent/useful in accurately predicting the results.

Prolego, Dataset, Collection

[1]. Ben Ulmer and Matthew Fernandez, Predicting Soccer Match Results in the English Premier League. (http://cs229.stanford.edu/proj2014/Ben%20Ulmer, %20Matt%20Fernandez,%20Predicting%20Soccer% 20Results% 20in%20the%20English%20Premier%20League.pdf)
[2]. A. S. Timmaraju, A. Palnitkar,& V. Khanna, Game ON! Predicting English Premier League Match Outcomes, 2013. (http://cs229.stanford.edu/proj2013/TimmarajuPalnit karKhanna-GameON!PredictionOfEPLMatchOutcomes.pdf)

[3]. Kaggle March Machine Learning Mania https://www.kaggle.com/c/march- machine-learning-mania-2017
[4]. Adit Deshpande, Applying Machine Learning to March Madness - Applying Machine Learning To March Madness (https://adeshpande3.github.io/Applying-Machine-Learning-to-March-Madness)
[5]. Premier League website - https://www.premierleague.com/
[6]. EA Sports FIFA Rating - https://www. faindex.com

Sourabh Swain, Shriya Mishra, "Prolego: A Data Science Approach to Predict the Outcome of a Football Match," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.132-136, 2018.

Cloud computing is “a style of computing in which scalable and elastic IT-enabled capabilities are delivered as a service to external customers using Internet technologies.”. Cloud computing is capable of enhancing business productivity and agility while allowing for greater efficiency and reducing costs. It is also possible to migrate big data to cloud. Increasingly, the focus of organizations has been on the ways that cloud will power data analytics. Cloud analytics enable organizations to move business intelligence, data warehouse operations and Online Analytical Processing (OLAP) to cloud. Data management, security, integration, compliance issues, poor software architecture and design are some of the issues that motivates with cloud inclusion for analytics solution. This paper aims in outlining the data analytics, cloud computing, cloud Analytics, and its Architecture, applications of cloud analytics and implementation challenges along with suggested solutions.

Data analytics, cloud computing, cloud analytics.

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G. Kalpana, NV. Chaitanya, "Cloud Analytics and its Implementation Challenges," International Journal of Computer Sciences and Engineering, Vol.6, Issue.4, pp.137-142, 2018.