Volume-5 , Issue-3 , Jun 2017, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  A Study on Big Data and its Applications in Retail Sector

  G.S. Sra, R. Kaur

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.3 , pp.124-128, Jun-2017

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  Big Data is no longer an unpopular word anymore. Any data which poses a challenge for currently existing database technologies is termed as Big Data. Today only big giants in the different fields like retail, medical, stock exchange etc. can think of handling it because of expensive and huge infrastructure involved in handling it. This research paper is based on highlighting how retail and cellular industry makes its use in targeting customers. In today’s busy life no one loves to receive unnecessary junk mails or messages. It is because of this that companies want to be crystal clear about each of its customer interest and his or her pattern of shopping and expenditure. For handling such a huge database of millions of customers, companies make use of powerful framework like Apache Hadoop. Apache Hadoop is one such framework which is capable of handling huge databases via its several components. It makes use of Map Reduce technology.

  Key-Words / Index Term
  Apache Hadoop; Big Data; Map Reduce; retail industry

  References
  [1] A. Katal, M. Wazid, R. Goudar “Big Data: Issues, Challenges, Tools and Good Practices”, 2013 Sixth International Conference on Contemporary Computing (IC3), Indian, pp. 404-409, 2013.
  [2] S. Kaisler, F. Armour, J. Espinosa “Big Data: Issues and Challenges Moving Forward” International Conference on System Sciences, Hawai, pp. 995-1004, 2013.
  [3] R. Kitchin, “Big data and human geography: Opportunities, challenges and risks”, Dialogues in human geography, Vol.3, Issue.3, pp.262-267.
  [4] J.Stuart Ward, Adam Barker “Undefined by Data: A Survey of Big Data Definitions”, School of Computer Science-University of St. Andrews, UK, pp.1-120, 2013.
  [5] M.S. Al-Hakeem, “A Proposed Big Data as a Service (BDaaS) Model”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.1-6, 2016.
  [6] Atsushi Sato, Runhe Huang, “Retail Business Intelligence”, IEEE International Conference on Data Science and Data Intensive Systems, USA, pp.211-219, 2015.
  [7] Sunny Kumar, “Big Data Platform-A Review”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.84-87, 2015.
  [8] Gagandeep Jagdev, “Affirmative aspects of Big Data assures new revolution in retail sector”, RTEST-2016 at Rayat-Bahra University, India, pp.23-28, 2016.
  [9] Gagandeep Jagdev, “Constructive aspects of Big Data in retail sector for financial growth”, CIOGEBS-2016 at Maharaja Ranjit Singh State Technical University, India, pp. 77-79 April 2016.
  

  Citation
  G.S. Sra, R. Kaur, "A Study on Big Data and its Applications in Retail Sector," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.124-128, 2017

• Open Access   Article

  Various Chunking and Deduduplication Techniques in Big Data

  Naresh Kumar, Ishu Devi

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.3 , pp.129-131, Jun-2017

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  In today’s environment very huge amount of data is generated with duplication. This huge amount of data is called big data. To handle this kind of big data and reduce duplicity from data chunking and deduplication mechanism is used. In deduplication mechanism duplicate data is removed by using chunking and hash functions. In this paper an attempt has been made to converse different chunking and deduplication techniques. A comparative analysis of these techniques with different pros and cons has been presented.

  Key-Words / Index Term
  Big Data, Chunking, Deduplication, FBC (Frequency Based Chunking) and CDC (Content Defined Chunking)

  References
  [1] M. Dirk, “Advanced data deduplication techniques and their application”, Ph.D. dissertation, Universit¨ at sbibliothek Mainz, pp.1-6, 2013.
  [2] M. Dirk, K. J¨urgen, B. Andre, C. Toni, K. Michael, K. Julian, “A study on data deduplication in hpc storage systems”, in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, USA, pp.1-7, 2012.
  [3] Chi Yang, Jinjun Chen, “A Scalable Data Chunk Similarity Based Compression Approach for Efficient Big Sensing Data Processing on Cloud”, IEEE Transactions on Knowledge and Data Engineering, China, pp.1144-1157, 2017.
  [4] R. Tuchinda, C. Knoblock, P. Szekely, "Building data integration queries by demonstration", Proceedings of the 12th international conference on Intelligent user interfaces, USA, pp. 170-179, 2007.
  [5] Q. He, X. Zhang, Z. Li, "Data deduplication techniques", 2010 International Conference on Future Information Technology and Management Engineering (FITME), , CA, pp. 430-433, 2010.
  [6] A. Banu and C. Chandrasekar, "A survey on deduplication methods", International Journal of Computer Trends and Technology, vol.3, no.3, pp. 364-368, 2012.
  [7] Zhi Tang, Youjip Won, “Multithread Content Based File Chunking System in CPU GPGPU Heterogeneous Architecture”, 2011 First International Conference on Data Compression, Communications and Processing, China, pp. 58-64, 2011.
  [8] Zhike Zhang, Zejun Jiang, Zhiqiang Liu, Cheng Zhang Peng, “LHS: A Nobel Method Of Information Retrieval Avoiding An Index Using Linear Hashing With Key Groups In Deduplication”, Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, China, pp.1312-1318, 2012.
  [9] Duane F. Shell, Leen-Kiat Soh, Vlad Chiriacescu, “Modeling Chunking Effects on Learning and Performance using the Computational-Unified Learning Model (C-ULM): A Multiagent Cognitive Process Model”, IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing, India, pp. 77-85, 2016.
  [10] Xingyu Zhang, Jian Zhang, “Data Deduplication Cluster Based on Similarity- Locality Approach”, IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, CA, pp.2168-2173, 2013.
  [11] Wen Xia, Hong Jiang, Dan Feng, Lei Tian, “Combining Deduplication and Delta Compression to Achieve Low-Overhead Data Reduction on Backup Datasets”, Data Compression Conference, France, pp. 203-212, 2014.
  [12] Bo Mao, Hong Jiang, Suzhen Wu, Lei Tian, “Leveraging Data Deduplication to Improve the Performance of Primary Storage Systems in the Cloud”, IEEE Transactions on Computers, NY, pp.1-14, 2015.
  [13] Sonali D. Chaure, M. U. Kulkarni and Pankaj M. Jadhav, "Web based ETL Approach to Transform Relational Database to Graph Database", International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.92-97, 2015.
  

  Citation
  Naresh Kumar, Ishu Devi, "Various Chunking and Deduduplication Techniques in Big Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.129-131, 2017

• Open Access   Article

  Privacy-Preserving Data Transmission protocol for Wireless Medical Sensor Data

  Y. Bala Supriya , C. Sudhakar Reddy

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.3 , pp.132-135, Jun-2017

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  Wireless Sensor Networks (WSN) in recent years has become a leading area of interest and research. A Sensor is a device that responds and detects some type of input from both the physical and environmental conditions. Wireless devices have great impact on the medicinal region with an extensive variety of ability, not just enhancing the personal satisfaction of patients and specialist tolerant productivity, remote innovation empowers clinicians to monitor patients remotely and give them timely health information, updates and support-potentially extending the reach of health care by making it accessible anyplace, anytime. In the meantime, there is a developing worry about the doctor`s facilities` capacity to give powerful care amid calamity occasions. Thus, instruments that robotize quiet checking can possibly incredibly enhance proficiency and nature of medicinal services. In healing centers, medicinal information sensors which screen patients create an undeniably substantial volume of ongoing information. The transmission of this information through remote systems in a healing facility turns into an essential issue in light of the fact that the therapeutic data of an individual is very crucial and it must be kept private and secure. In this paper, we propose a pragmatic approach that guarantees secure data transmission. The primary commitment of this paper is safely conveying the patient information by actualizing Security Safeguarding Information Transmission convention and utilizing the Paillier and ElGamal cryptosystems to perform statistical analysis on the patient information without compromising the patient`s privacy. We enhance this protocol to reduce the overhead by implementing secure data aggregation method Keywords: privacy protection; paillier cryptosystem; patient data privacy.

  Key-Words / Index Term
  Privacy-Preserving, Data Mining, Cryptography, WMSN

  References
  [1] Azzedine Boukerche, Yonglin Ren, “A Secure Mobile Healthcare System using Trust-Based Multicast Scheme”, IEEE Journal On Selected Areas In Communications, Vol. 27, No. 4, pp.316-325, 2009.
  [2] Daojing He, Sammy Chan, “A Novel and Lightweight System to Secure Wireless Medical Sensor Networks”, IEEE Journal Of Biomedical, Health Informatics, Vol. 18, No. 1, pp.23-32, 2014.
  [3] Geoffrey G. Messier, Ivars G. Finvers, “Traffic Models for Medical Wireless Sensor Networks”, IEEE Communications Letters, Vol. 11, No. 1, pp.,21-30, 2007.
  [4] Rongxing Lu, “SPOC: A Secure and Privacy-preserving Opportunistic Computing Framework for MobileHealthcare Emergency”, IEEE Transactions On Parallel And Distributed Systems, Vol. 12, No. 2, pp.452-461, 2012.
  [5] Ashminder Kaur, Ms. Lofty, "A Review of Colour Image Watermarking Scheme Based Image Normalized", International Journal of Computer Sciences and Engineering, Vol.4, Issue.7, pp.183-185, 2016.
  [6] Zhaoyang Zhang, Honggang Wang, Athanasios V. Vasilakos, Hua Fang, “ECG- Cryptography and Authentication in Body Area Networks”, IEEE Transactions On Information Technology In Biomedicine, Vol. 16, No. 6, pp.321-332, 2012.
  [7] Malan D., Jones T.F., Welsh M., Moulton S. “Code Blue: An Ad-Hoc Sensor Network Infrastructure for Emergency Medical Care”, Proceedings of the MobiSys 2004 Workshop on Applications of Mobile Embedded Systems (WAMES 2004), USA, pp.6-9, 2004.
  [8] Anamika Patil, Pooja Bafna, Mona Pounikar and Pranjal Badgujar, "Distinct Revocable Data Hiding In Ciphered Image", International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.90-94, 2014.
  [9] Surbhi Sharma, "Embedding more security in digital signature system by using combination of public key cryptography and secret sharing scheme", International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.111-115, 2016.
  [10] T. ElGamal, “A Public-Key Cryptosystem and a Signature Scheme Based on Discrete Logarithms”, IEEE Transactions onInformation Theory, Vol.31, Issue.4, pp.469-472, 1985.
  

  Citation
  Y. Bala Supriya , C. Sudhakar Reddy, "Privacy-Preserving Data Transmission protocol for Wireless Medical Sensor Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.132-135, 2017

• Open Access   Article

  Analysis of Efficient Classification Algorithm for Detection of Phishing Site

  Meenu Shukla, Sanjiv Sharma

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.3 , pp.136-141, Jun-2017

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  Abstract
  Phishing is mainly related to steal the confidential information and personal data of web users by making duplicate of the original one in which the content and images are almost similar to the legitimate website with small changes. Other method of phishing is to make changes in the URL that is approximately similar to legitimate website. Here we have discussed the different methods for phishing detection and some of the disadvantages of them. In this paper, we proposed phishing detection on the basis of web source and uses uniform resource locator (URL) features. We identified the features that contained in the phishing URLs. The technique is evaluated with a dataset of phishing URLs and White List URLs. The results evaluated shows that with this technique we are able to detect more phishing sites.

  Key-Words / Index Term
  Phishing sites, URL based features, Web Source Based Features, Machine learning, and Random Forest

  References
  [1] J. Hong, “The state of phishing attacks”, Communications of the ACM, vol. 55, Issue 1, pp. 74-81, 2012.
  [2] A. Sardana, A Naga Venkata Sunil, “A PageRank Based Detection Technique for Phishing Web Sites”, IEEE Symposium on Computer and Informatics (ICSI), vol. 123, Issue 13, pp.46-50, 2012.
  [3] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, “The WEKA Data Mining Software: An Update”, SIGKDD Explorations, Vol. 11, Issue 1, pp. 10-18 ,2009.
  [4] S. Ansari, J. Gadge, “Architecture For Checking Trustworthiness Of Websites”, International Journal of Computer Application, Vol. 44, Issue 14, pp. 22-26, 2012.
  [5] M. Shukla, S. Sharma, "A Comparative Study of Existing Data Mining Techniques for Phishing Detection", International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.182-187, 2017.
  [6] Ma, Justin, et al. "Beyond blacklists: learning to detect malicious web sites from suspicious URLs." Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp.681-688, 2009.
  [7] Nguyen, Luong Anh Tuan, ‘A novel approach for phishing detection using URL-based heuristic.” Computing, Management and Telecommunications (ComManTel), International Conference on. IEEE, Vol. 2, Issue 8, pp. 55-64, 2014.
  [8] Zhang Y, Hong JI, Cranor LF, “CANTINA: a content based approach to detecting phishing web sites”, In: Proceedings of the 16th international conference on world wide web, Banff, pp. 639-648, 2007.
  [9] Xiang G, Hong J, Rose CP, Cranor L, “CANTINA: a feature-rich machine learning framework for detecting phishing web sites”, ACM Transaction, pp. 14-21, 2011.
  [10] Huang H, Qian L, Wang Y, “A SVM based technique to detect phishing URLs”.Information Technology Journal, vol. 11, Issue 7, pp 921-925,2012.
  [11] Liébana-Cabanillas F, Nogueras R, Herrera LJ, Guillén A, “Analysing user trust in electronic banking using data mining methods”, Expert Systems with Applications, vol. 40, Issue 14, pp. 5439-5447, 2013.
  [12] Li Y, Xiao R, Feng J, Zhao L, “A semi-supervised learning approach for detection of phishing webpages”, Optik, Volume 124, Issue 23, pp. 6027-6033, 2013.
  [13] R Islam, J Abawajy, “A multi-tier phishing detection and filtering approach”, Journal of Network and Computer Applications, Volume 36, Issue 1, pp. 324-335, 2013.
  [14] Chen X, Bose I, Leung ACM, Guo C, “Assessing the severity of phishing attacks: a hybrid data mining approach”, Decision Support Systems, vol. 50, Issue 4, pp. 662-672, 2011.
  [15] Nishanth KJ, Ravi V, Ankaiah N, Bose I, “Soft computing based imputation and hybrid data and text mining: the case of predicting the severity of phishing alerts”, Expert Systems with Applications, Vol 39, Issue 12,pp. 10583-10589, 2012.
  [16] Liu W, Deng X, Huang G, Fu AY, “An antiphishing strategy based on visual similarity assessment. IEEE Computer Society”, IEEE Internet Computing, vol. 6, Issue 10, pp. 2-19, 2006.
  [17] Medvet E, Kirda E, Kruegel C, “Visual-similarity-based phishing detection. SecureComm.”, In: Proceedings of the 4th international conference on Security and privacy in communication netowrks”, vol. 6, Issue 10 , pp 22–25, 2008.
  [18] Fu AY, Wenyin L, Deng X “Detecting phishing web pages with visual similarity assessment based on earth mover’s distance”, IEEE Trans Dependable Secure Computer, vol. 3, Issue 4, pp. 301-321, 2006.
  [19] A K Jain, B B Gupta, “Phishing Detection: Analysis of Visual Similarity Based Approaches”, Hindawi Security and Communication Networks, vol. 4, Issue 8, pp.21-38, 2017.
  

  Citation
  Meenu Shukla, Sanjiv Sharma, "Analysis of Efficient Classification Algorithm for Detection of Phishing Site," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.136-141, 2017

• Open Access   Article

  Phylogenetic Tree Construction for Distance based Methods

  Pankaj Bhanbri, O.P. Gupta

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.3 , pp.142-159, Jun-2017

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  Abstract
  Bio-informatics is an upcoming area resulting from the combination of bio-technology and computer science. All the findings in bio-informatics are stored and utilized with the help of computer science to get the constructive results and elaborations. Phylogenetic trees are constructed from the molecular sequences of the different living organisms. These are actually needed to evaluate the relation between the different species and also the different time gaps from the actual origin. Sequence alignment is one of the applications of bioinformatics. Multiple Sequence Alignment is used to align the biological sequences along a column. Multiple sequence alignment arranges the sequences in such a way that evolutionarily equivalent positions across all sequences are matched. The process starts by generating distances of multiple alignments among the pairs of different species, then a phylogenetic tree is being formulated. Further, taking different data sets, bootstrapping of phylogenetics and consensus trees are being shown. Web based FASTA sequences are considered as input.

  Key-Words / Index Term
  Phylogenetic tree, FASTA, Basic Local Alignment Search Tool, Hidden Markov Model

  References
  [1]. Kakiuchi, I. and Kimura, M. (2011), “Characterization of a new neighborhood determined by three parameters”, Technical Report of the NAS, pp. 1-11.
  [2]. Vijan, S. and Mehra, R. (2011), “Biological Sequence Alignment for Bioinformatics Applications Using MATLAB”, IJCSET, vol. 2, 5, pp. 310-315.
  [3]. Zhou, J., Sander, J., Cai, Z., Wang, L. and Lin, G. (2010), “Finding the Nearest Neighbors in Biological Databases Using Less Distance Computations”, IEEE/ACM Transactions on computational biology and bioinformatics, vol. 7, 4, pp. 669-680.
  [4]. Zimek, A., Buckwald, F., Frank, E. and Kramer, S. (2010), “A Study of Hierarchical and Flat Classification of Proteins”, IEEE/ACM Transactions on computational biology and bioinformatics, vol. 7, 3, pp. 563-571.
  [5]. Liu, C. and Wang, F. (2012), “Pair-wise sequence alignment algorithm in bioinformatics”, EEESym.2012, pp. 36-38.
  [6]. Shehab, S.A., Keshk, A. and Mahgoub, H. (2012), “Fast Dynamic Algorithm for Sequence Alignment based on Bioinformatics”, IJCA, vol. 37, 7, pp. 54-61.
  [7]. Mukunthan, B., Nagaveni, N. and Pushpalatha, A. (2011), “Identification of unique repeated patterns, location of mutation in DNA finger printing using AI technique”, Journal of Bioinformatics and Sequence Analysis, vol. 3, 6, pp. 100-115.
  [8]. Naznin, F., Sarker, R. and Essam, D. (2010), “DGA: Decomposition with genetic algorithm for multiple sequence alignment”, CIBCB 2010, pp. 1-8.
  [9]. Lesk, A.M. (2002), “Introduction to Bioinformatics”, Oxford University Press, pp 180-200.
  [10]. Lin, X., Meng, Z., He, X., Liu, Q., Liu, Y., Li, J. and Zhou, Y. (2010), “A solution to integrate the phylogenetic tree’s generation based on web”, ICBBE2010, pp. 1-3.
  [11]. Huson, D.H. (2009), “Drawing Rooted Phylogenetic Networks”, IEEE Transactions on computational biology and bioinformatics, vol. 6, 1, pp. 103-109.
  [12]. Miyazawa, S. (2011), “Advantages of a Mechanistic Codon Substitution Model for Evolutionary Analysis of Protein-Coding Sequences”, PLoS ONE, vol. 6, 12, pp. 54-69.
  [13]. Mount, D.W. (2004), “Bioinformatics: Sequence and Genome Analysis”, 2nd ed. Cold Spring Harbor Laboratory Press: Cold Spring Harbor, NY.. ISBN 0-87969-608-7.
  [14]. Bogdanowicz, D. and Giaro, K. (2012), “Matching Split Distance for Unrooted Binary Phylogenetic Trees”, IEEE/ACM Transactions on computational biology and bioinformatics, vol. 9, 1, pp. 150-160.
  [15]. Nakhleh, L. (2010), “A Metric on the Space of Reduced Phylogenetic Networks", IEEE/ACM Transactions on computational biology and bioinformatics, vol. 7, 2, pp 1-5.
  [16]. Sundaravardhan, N., Patnaik, D., Marwah, M., Shah, A. and Ramakrishnan, N. (2012), “System and method for tree discovery”, United States Patent Application Publication, Pub. No.: US 2012/0185421 A1
  [17]. Krane, D. and Raymer, M. (2006), “Fundamental concepts of bioinformatics”, Pearson Education Publishers.
  [18]. Pevsner, J. (2009), “Bioinformatics and Functional Genomics”, A john wiley & sons, Inc. Publication, pp 215-221.
  [19]. Rastogi, S.C., Mendiratta, N., Rastogi, P. (2007), “Allignment of Multiple Sequences and Phylogenetic Analysi-Bioinformatics Methods and Applications”, 3rd edition, PHI publication, pp. 5-120.
  [20]. Wang, Y., E. and Sheth, D., N. (2011), “ Methods and collective reasoning framework for complex decision making”, United States Patent Application Publication, Pub. No.: US 2011/0320374 A1
  [21]. Heard, E. J., Riechmann, L. J., creelman, A. R., Ratkliffe, O., canals, D. R., Repetti, p. P., Kumimoto, W. R. and Libby, M. J. (2010), “Plant transcriptional regulators”, United States Patent Application Publication, Pub. No.: US 2010/0175145 A1
  [22]. Torres, M., Dias, G., Gonçalves, G. and Vieira, C. (2011), “Tool that Integrates Distance Based Programs for Reconstructing Phylogenetic Trees”, IEEE latin america transactions, vol. 9, 5, pp. 895-901.
  [23]. Gronau, I., Moran, S. and Yavneh, I. (2010), “Adaptive Distance Measures for Resolving K2P Quartets: Metric Separation versus Stochastic Noise”, Journal of Computational Biology, vol. 17, 11, pp. 1509-1518.
  [24]. Wang, L.S., Leebens-Mack, J., Wall, P.K., Beckmann, K., DePamphilis, C.W. and Warnow T. (2011), “The Impact of Multiple Protein Sequence Alignment on Phylogenetic Estimation”, IEEE/ACM Transactions on computational biology and bioinformatics, vol. 8, 4, pp. 1108-1119.
  [25]. Xiong J., (2006), “Essential Bioinformatics”, United States Of America, Cambridge University Press, New York.
  [26]. Guo, P., Chen, G. and Wang, Y. (2011), “Constructing phylogenetic tree based on three-parameter model”, Key Engineering Materials, vol. 474-476, pp. 2193-2197.
  [27]. Sul, J., S. and Williams, L., T. (2009), “An experimental Analysis of consensus tree algorithms for large-scale Tree Collections”, ISBRA, LNBI 5542, pp 100-111.
  [28]. Rwahnih, A. M., Turturo, C., Minafra, A., saldarelli, P., Myrta, A., Pallas, V. and savino, V., “Molecular variability of apple chlorotic leaf spot virus in different hosts and geographical regions”, journal of plant pathology, pp 117-122.
  

  Citation
  Pankaj Bhanbri, O.P. Gupta, "Phylogenetic Tree Construction for Distance based Methods," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.142-159, 2017