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As a significant part of discovering association rules, frequent item sets excavating plays a key role in removal associations, correlations, bass and other imperative data mining tasks. Since a little customary frequent item sets mining algorithms are incapable to knob gargantuan small file datasets effectively, such as high recall cost, high I/O operating price, and squat computing recitals, a better Parallel FP-Growth (EPFP) algorithm and converse its applications in this paper. In particular, a small file processing strategy for huge small file datasets to reimburse defects of squat read/write speed and low processing efficiency in Hadoop. Moreover, utilize of Map Reduce to execute the parallelization of FP-Growth algorithm, thereby improving the general performance of frequent item set mining. The investigation results demonstrate that the EPFP algorithm is practicable and suitable with a excellent speedup and a higher mining efficiency, and can convene the rapidly growing needs of frequent item sets mining for enormous petite file data sets.

[1] Khurana K and Sharma S, ―A comparative analysis of association rule mining algorithms, International Journal of Scientific and Research Publications, Volume 3, Issue 5, pp 38-45, May 2013.
[2] Peng Zhao, “Research Mining Frequent Items Algorithm in Massive High-dimensional Data Sets”, Computer Applications and Software, 2012.
[3] Ahilandeeswari.G, DR.R Manicka Chezian, “A Comparative analysis of Association rule excavating in Big Data Mining Algorithms ”, International Journal Of Computer Science and Engineering, Volume 3, Issue 6, pp 82-88,June 2015
[4] Ms. Dhamdhere Jyoti L., Prof. Deshpande Kiran B. "An Effective Algorithm for Frequent Itemset Mining on Hadoop.", International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 8, August 2014.
[5] Guojun Mao, Lijuan Duan, Shi Wang, Yun Shi, “data mining principles and algorithms (the second edition)”, Tsinghua University Press, Beijing, 2007.
[6] Tom White, “Hadoop: The Definitive Guide, Second Editon”, Tsinghua University Press, 2011.
[7] Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang, Edward Chang,“Pfp: Parallel Fp-Growth for Query Recommendation”, RecSys '08 Proceedings of the 2008 ACM conference on Recommender systems, Pages 107-114 ACM New York, NY, USA ©2008.
[8] Ferenc Kovacs and Janos Illes “Frequent Itemset Mining on Hadoop.”,IEEE 9th International conference on Computational Cybernetics, Volume 2 Issue 4, June 2013.
[9] A. Swami, T. Imielienski, R. Agrawal," Mining Association Rules between Sets of Items in Large databases.", ACM Press, pp 207–216, July 1993
[10] Yang Liu, Maozhen Li, Alham, N.K., Hammoud, S.,Ponraj, M. “Load balancing in MapReduce environments for data intensive applications”, Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on,Page(s): 2675 - 2678 ,2011.
[11] Ferenc Kovacs and Janos Illes “Frequent Itemset Mining on Hadoop.”,IEEE 9th International conference on Computational

Ahilandeeswari. G and R.Manicka Chezian, "A Streamlined Frequent Item set excavating using FP growth from Map Reduce," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.1-6, 2015.

Image re-ranking, as an effectual way to get better the outputs of web-based image search, has been legitimate by existing mercantile search engines such as Bing and Google. Specified a query keyword, a pond of images is first cultivated based on textual in sequence. By inquisitive the users to pick a query image from the pool, the outstanding pictures are re-ranked based on their ocular concurrences with the query image. A most important confront is that the correspondences of ocular features do not glowing correlate with images’ semantic meanings which construe users’ search intention. In recent time’s people wished-for to match pictures in a semantic space which worn essences or orientation classes closely allied to the semantic meanings of pictures as basis. However, wisdom a universal visual semantic space to illustrate extremely varied images from the web is difficult and ineffective. We put forward a novel image re-ranking context, which routinely offline learns diverse semantic spaces for dissimilar query keywords. The ocular features of pictures are predicted keen on their related semantic spaces to acquire semantic signatures. On the internet arena, images are re-ranked by examine their semantic signatures accomplish from the semantic space specified by the query keyword. The wished-for query-specific semantic signatures appreciably get better both the accurateness and efficiency of image re-ranking. The pioneering visual characteristics of thousands of proportions can be predicted to the semantic signatures as squat as 25 dimensions. Preliminary results show that 25-40 percent relative enrichment has been accomplished on re-ranking precisions contrasted with the state-of-the-art approaches.

Image Search, Semantic Space, Semantic Signature, Context, Query Image, Query Keyword

[1] Y. Cao, C. Wang, Z. Li, L. Zhang, and L. Zhang. “Spatial-bag-of features.” In Proc. CVPR, 2010.
[2] Bart and S. Ullman. “Single-example learning of novel classes using representation by similarity.” In Proc. BMVC, 2005.
[3] B. Luo, X. Wang, and X. Tang. “A world wide web based image search engine using text and image content features.” In Proceedings of the SPIE Electronic Imaging, 2003.
[4] J. Cui, F. Wen, and X. Tang, “Intent Search: Interactive on-Line Image Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia, 2008.
[5] J. Cui, F. Wen, and X. Tang, “Real Time Google and Live Image Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia, 2008.
[6] C. Lampert, H. Nickisch, and S. Harmeling. “Learning to detect unseen object classes by between-class attribute transfer.” In Proc. CVPR, 2005.
[7] J. Philbin, M. Isard, J. Sivic, and A. Zisserman. “Descriptor Learning for Efficient Retrieval.” In Proc. ECCV, 2010.
[8] N. Rasiwasia, P. J. Moreno, and N. Vasconcelos. “Bridging the gap: Query by semantic example”. IEEE Trans. On Multimedia, 2007.
[9] M. Rohrbach, M. Stark, G. Szarvas, I. Gurevych, and B. Schiele. “What helps where and why? Semantic relatedness for knowledge transfer”. In Proc. CVPR, 2010.
[10] Q. Yin, X. Tang, and J. Sun. “An associate-predict model for face recognition”. In Proc. CVPR, 2011.
[11] Lowe. “Distinctive image features from scale-invariant key points”. Int’l Journal of Computer Vision, 2004.
[12] Xiaogang Wang, Shi Qiu, Ke Liu, and Xiaoou Tang, “Web Image Re-Ranking Using Query-Specific Semantic Signatures”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 4, April 2014.

P. Amani and Maddali M. V. M. Kumar, "Conservative Procedures for Web Image Re-Ranking Precisions Using Semantic Signatures," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.7-10, 2015.

Cloud is the collection of computers on the internet that is being offered as a revolutionary storage method for files and Big Data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information. In our proposed system, we implement a safe method to save Big Data files using Apache Hadoop technology. And here we provide two web services: uploading and downloading of files. Once we upload the files, they are stored in different nodes across the cluster after partitioning. We make the data highly available, by keeping the different copies of data in several nodes. The uploaded files can be retrieved at any time according to our needs. Even if there is a failure at any node, the copy of data on some other nodes makes our system fault tolerant and reliable. Along with that, we can also make our files public or private and thus providing file sharing facility. We offer a security system for storing and managing Big Data files.

Big Data, Fault tolerant, Hadoop Distributed File System

[1]Sivaraman, E.; Manickachezian, R., "High Performance and Fault Tolerant Distributed File System for Big Data Storage and Processing Using Hadoop," International Conference on Intelligent Computing Applications (ICICA), 2014, vol., no., pp.32,36, 6-7 March 2014.
[2]Krishna, T.L.S.R.; Ragunathan, T.; Battula, S.K., "Performance Evaluation of Read and Write Operations in Hadoop Distributed File System," Parallel Architectures, Algorithms and Programming (PAAP), 2014 Sixth International Symposium on , vol., no., pp.110,113, 13-15 July 2014
[3]Shvachko, K.; Hairong Kuang; Radia, S.; Chansler, R., "The Hadoop Distributed File System," in Mass Storage Systems and Technologies (MSST), 2010 IEEE 26th Symposium on , vol., no., pp.1-10, 3-7 May 2010
[4]K.V. Shvachko, “HDFS Scalability: The limits to growth,” login:. April 2010, pp. 6-16.
[5]Manikandan, S.G.; Ravi, S., "Big Data Analysis Using Apache Hadoop," in IT Convergence and Security (ICITCS), 2014 International Conference on , vol., no., pp.1-4, 28-30 Oct. 2014
[6]Devakunchari, R., "Handling big data with Hadoop toolkit," in Information Communication and Embedded Systems (ICICES), 2014 International Conference on , vol., no., pp.1-5, 27-28 Feb. 2014

Shincy Anto Anila Ramesh K, Ladlie Dias and Sabna A.B, "Implementation of Hadoop Distributed File System Services in an Application," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.10-14, 2015.

Microarray technology has made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. But the high dimensionality property of gene expression data makes it difficult to be analyzed. Clustering associated with the concept of rough set theory is very effective in such situations. This paper gives a briefly introduction about the concepts of RST, clustering, gene expression, microarray technology and discuss the basic elements of clustering on gene expression data. It also explain why rough clustering is preferred over other conventional methods by presenting a survey on few clustering algorithms based on rough set theory for gene expression data. Finally it concludes by stating that this area proves to be potential research field for the research community.

Microarray technology, Rough Set, gene expression, rough clustering

[1]Z. Pawlak, “Rough sets”, International Journal of Computer and Information Sciences, 11:341-356 (1982).
[2] R.E. Kent, Rough Concept Analysis: a synthesis of rough sets and formal concept analysis, Fundamenta Informaticae , pp. 169–181, 1996.
[3] Lin T.Y. and Cercone N. “Rough Sets and Data Mining - Analysis of Imperfect Data”, Kluwer Academic Publishers, Boston, London, Dordrecht, P.430,1997.
[4] Thabet Slimani, “Application of Rough Set Theory in Data Mining “,2010.
[5] Ruizhi Wang , Tongji Univ, Shanghai , Miao, Duoqian ,Gang Li, Hongyun Zhang,” Rough Overlapping Biclustering of Gene Expression Data”, Bioinformatics and Bioengineering, 2007. BIBE 2007. Proceedings of the 7th IEEE International Conference,Oct. 2007.
[6] Sajid Nagi, Dhruba K. Bhattacharyya, Jugal K. Kalita,” Gene Expression Data Clustering Analysis: A Survey”,2008.
[7] Lijun Sun Duoqian Miao Hongyun Zhang,(2007) Gene Selection with Rough Sets for Cancer Classification, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)IEEE 2007.
[8] Jung-Hsien Chiang and Shing-Hua Ho,” A Combination of Rough-Based Feature Selection and RBF Neural Network for Classification Using Gene Expression Data”, IEEE Transactions On Nanobioscience, Vol. 7, No. 1,March 2008.
[9] Pradipta Maji,“Fuzzy–Rough Supervised Attribute Clustering Algorithm and Classification of Microarray Data”, IEEE Transactions On Systems, Man, And Cybernetics.
[10] Adhikary, K. ,Das, S. Roy, S, ” A Novel and Efficient Rough Set Based Clustering Technique for Gene Expression Data”, 2nd International Conference on Business and Information Management (ICBIM), 2014.
[11] Maji, P. ; Pal, S. “Clustering Functionally Similar Genes from Microarray Data”, WileyIEEE Press 2012, ISBN :9781118119723.
[12]Anasua Sarkar and Ujjwal Maulik,”Rough Based Symmetrical Clustering for Gene Expression Profile Analysis” IEEE transactions on nanobioscience, vol. 14, no. 4, june 2015.
[13] J. Jeba Emilyn and K. Ramar “ A Rough Set based Gene Expression Clustering Algorithm”, Journal of Computer Science 7 (7): 986-990, 2011,ISSN 1549-3636, Science Publications.
[14] Anasua Sarkar & Ujjwal Maulik,”Cancer Gene Expression Data Analysis using Rough Based Symmetrical Clustering”, 2013.
[15] Dhanalakshmi.K & Hannah Inbarani, “Fuzzy Soft Rough K-means Clustering for Gene Expression Data”,2011.
[16] Rudra Kalyan Nayak , ,Debahuti Mishra, Kailash Shaw & Sashikala Mishra,”Rough Set Based Attribute Clustering For Sample Classification Of Gene Expression Data”,ICMOC- ScienceDirect.2012.

C. Udhaya Bharathy and C. Rathika, "Rough Based Clustering For Gene Expression Data –A Survey," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.15-19, 2015.

Active Queue Management has specific role in computer networks. In this paper, we investigate the chances of Active Queue Management at serve side. The proposed system reduces packet loss ratio at server side to provide better load balancing at internal buffer levels. The system distinguishes responsive flows from unresponsive flows in a congested Hyper Text Transfer Protocol traffic, dynamically manages them and provides better transfer speed and maximum throughput in network.

AQM, CHOKeR, Server, Proxy server, Load balancing

[1] en.wikipedia.org/wiki/IP_address.
[2] B. Kiruthiga and Dr. E. George Dharma Prakash Raj, “Survey on AQM Congestion Control Algorithms”, IJCSMC, Vol. 2, Issue. 2, pp.38–44, Feb 2014.
[3] en.wikipedia.org/wiki/Fair_queuing.
[4] en.wikipedia.org/wiki/ Levenshtein distance.html.
[5] en.wikipedia.org/wiki/Explicit_Congestion_Notification.
[6] gettys.wordpress.com/active-queue-management-aqm-faq.
[7] G.F.Ali Ahammed, Reshma Banu, “Analyzing the Performance of Active Queue Management Algorithms”, IJCNC, Vol. 2, pp. 19, Mar 2010.
[8] searchnetworking.techtarget.com/definition/load-balancing.
[9] Rong Pan, Balaji Prabhakar, Konstantinos Psounis, “CHOKe: A stateless active queue management scheme for approximating fair bandwidth allocation”, INFOCOM 2000, vol.2, pp. 942-951, Mar 2000.
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[11] Jiang Ming, WU Chumming, Zhang Min and Bian Hao, “CSa-XCHOKe: A Congestion Adaptive CHOKe Algorithm”, Chinese Journal of Electronics, Vol.19, No.4, Oct 2010.
[12] Ying Jiang, and Jing Liu, “Self adjustable CHOKe: an active queue management algorithm for congestion control and fair bandwidth allocation”, IEEE computers and comm., Vol.2, No.4, pp. 1018-1024, Jul 2013.
[13] K.Chitra and Dr. G.Padamavathi, “Adaptive CHOKe: An algorithm to increase the fairness in Internet Routers”, IJANA, vol. 01, Issue. 06, pp. 382-386, Apr 2010.
[14] G. Sasikala and E. George Dharma Prakash Raj, “P-CHOKe: A Piggybacking-CHOKe AQM Congestion Control Method”, IJCSMC, Vol. 2, Issue. 8, pp.136–144, Aug 2013.
[15] Addisu Eshete and Yuming Jiang, “Protection from Unresponsive Flows with Geometric CHOKe”, Centre for Quantifiable Quality of Service in Communication Systems, Feb 2012.
[16] K.Chitra and Dr.G.Padmavathi, “FAVQCHOKE: To Allocate Fair Buffer To A Dynamically Varying Traffic In An Ip Network”, IJDPS, Vol. 2, Issue. 1, pp.73–82, Jan 2011.
[17] Shushan Wen, Yuguang Fang and Hairong Sun, “CHOKeW: Bandwidth Differentiation and TCP Protection in Core Networks”, IEEE Trans. Parallel and Distributed Sys. , Vol. 20, NO. 1, pp. 34-47, Jan 2009.
[18] Lingyun Lu, Haifeng Du and Ren Ping Liu, “CHOKeR: A Novel AQM Algorithm with Proportional Bandwidth Allocation and TCP Protection”, IEEE Trans. Industrail Informatics, Vol. 10, No. 1, pp.637–644, Feb 2014.
[19] Addisu Eshete and Yuming Jiang, “Generalizing the CHOKe Flow Protection”, Preprint submitted to Computer Networks, pp.1–28, Feb 2012.
[20] Shalki Chahar, “Social Networking Analysis”, International Journal of Computer Sciences and Engineering, Vol. 02, No. 5, pp.159–163, May 2014.
[21] Vijith C and Dr. M. Azath, “Survey on CHOKe AQM Family”, International Journal of Computer Sciences and Engineering, Vol. 02, No. 11, pp.81–85, Nov 2014.

Vijith C and M. Azath, "Performance evaluation of Unresponsive flow Management on Server for better load rebalancing in Internet," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.20-27, 2015.

This paper presents an Artificial Neural Network (ANN) based approach for the recognition of handwritten characters in the presence of noise because now a days the handwritten recognition place a crucial role in various industrial applications. In the Handwritten recognition noise has been regarded as one of the major issue that degrades the performance of character recognition system. So to recognize the handwritten characters in different noise levels, In order to overcome this limitation, the back propagation (BP based ANN) is designed for the handwritten character recognition. The recognition system is designed and tested in JAVA under different noise levels. Experimental results indicate that the proposed approach can obtain very high recognition rate for all handwritten characters in the presence of noise.

OCR, MCR, ANN, Character Recognition, Back Propagation Algorithm

[1] Mansi Shah and Gordhan b jethava “A literature review on hand written character recognition” Indian streams research journal,vol -3 , issue –2, march.2013,ISSN:-2230-7850.
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[3] Ankit Sharma,Dipti R Chaudhary “Character Recognition Using Neural Network” International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue4- April 2013.
[4] Vivek Shrivastava and Navdeep sharma “Artificial Neural Network based optical character recognition” an international journal (sipij) vol.3, no.5, october 2012.
[5] Anjali Chandavale, Suruchi Dedgaonkar, Dr. Ashok sapkal “An approach for character recognition using pattern matching with ANN “International journal of scientific & engineering research, volume 3, issue 10, october-2012 1 ISSN 2229-5518.
[6] J.Pradeep, E.Srinivasan and S.Himavathi “Diagonal based feature extraction for handwritten alphabets recognition system using neural network” International Journal of Computer Science & Information Technology (IJCSIT), Vol 3, No 1, Feb 2011.
[7] N. k. Bose, P. Liangneural “Network fundamentals with graphs, algorithms, and applications”.
[8] David Kriesel “A Brief introduction to Neural Networks”.
[9] Madhu Shahi, Dr. Anil K Ahlawat, Mr. B.N Pandey “ Literature Survey on Offline Recognition of Handwritten Hindi Curve Script Using ANN Approach” International Journal of Scientific and Research Publications, Volume 2, Issue 5, May 2012 1 ISSN 2250-3153 .
[10] Line Eikvil, “Optical Character Recognition”.
[11] Majida Aliabed,“Simplifying Handwritten Characters Recognition Using a Particle Swarm Optimization Approach”.
[12] Brandon Maharrey,“A Neural Network Implementation of Optical Character Recognition”.
[13] Girish Kumar jha, “Artficial Neural Networks”.
[14] prerna Kakkar, Umesh Dutta, “A Novel Approach to Recognition of English Characters Using Artificial Neural Network” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization)Vol. 3, Issue 6, June 2014.

K. Radha Revathi, A.N.L Kumar, Andey Krishnaji, "Neuro Recognizer: Neural Network Based Hand-Written Character Recognition," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.28-33, 2015.

Cryptography is one way of providing security using the process of encryption and decryption. An original message is known as the plaintext, while the coded message is called the cipher text. The process of converting from plaintext to cipher text is known as enciphering or encryption and that of restoring the plaintext from the cipher text is deciphering or decryption. The purpose of the proposed system is to encrypt the sending message using a very sophisticated 5-codes encryption method to encrypt the data so as to ensure no leakage of sensitive and confidential information while sending a message. The proposed technique converts each character represented as a 5 codes, each code consist of a 5 digits. In order to provide advanced level of security we proposed a multi-codes generation algorithm which generates the code dynamically every time a message is initiated from either sender or receiver. The main objective of the software is to maintain the security of information transmitted via modern means of transportation such as the internet, mobile and so on. In this work, a robust RMI-based Multi Client Chat application is designed in which multiple clients can communicate through a secure RMI communication channel by sending and receiving messages between/among them. The application uses advanced encryption and decryption algorithm which uses the character codes for encryption and decryption. Each character is represented by 5 codes, each code consists of 5 digits. For example, A= {95231, 45672, 11132, 22367, 95267}.

RMI (Remote Method Invocation), Data Encryption, multi codes, Cryptography

[1] Roshni Padate, Amana Patel,” Encryption and Decryption of Text Using AES Algorithm “, International Journal of Emerging Technology and Advanced Engineering , ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 5, May 2014.
[2] Karthik, Muruganandam, “Data Encryption and Decryption by Using Triple DES and Performance Analysis of Crypto System “, International Journal of Scientific Engineering and Research (IJSER), ISSN (Online): 2347-3878, Volume 2 Issue 11, November 2014.
[3] Rajan.S.Jamgekar, Geeta Shantanu Joshi, “File Encryption and Decryption Using Secure RSA “, International Journal of Emerging Science and Engineering (IJESE) ISSN: 2319–6378, Volume-1, Issue-4, February 2013.
[4] Prashanti.G, Deepthi.S, Sandhya Rani.K,” A Novel Approach for Data Encryption Standard Algorithm “, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-2, Issue-5, June 2013.
[5] Maryam Ahmed, Baharan Sanjabi, Difo Aldiaz, Amirhossein Rezaei, Habeeb Omotunde ,” Diffie-Hellman and Its Application in Security Protocols ”, International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 1, Issue 2, November 2012 .
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[7] Shraddha Soni, Himani Agrawal, Dr. (Mrs.) Monisha Sharma, ― Analysis and Comparison between AES and DES Cryptographic Algorithm‖, International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 6, December 2012, pp 362-365.
[8] Sattar J Aboud, “An efficient method for attacking RSA scheme”, IEEE 2009.
[9] About AES – Advanced Encryption Standard‖, Copyright 2007 Svante Seleborg Axantum Software AB.

[10] Grabbe J, Data Encryption Standard: The Triple DES algorithm illustrated Laissez faire city time, Volume: 2, No. 28, and 2003.
[11] Data Encryption Standard (DES), FIPS PUB 46-3 - 1999.
[12] "A public key cryptosystem and a signature scheme based on discrete locarithms" TaherElGamal 1998, Springer-Verlag.
[13] William Stallings, Network Security Essentials: Applications and Standards, 4th ed., Prentice Hall.
[14] New Approach of Data Encryption Standard Algorithm Shah Kruti R., Bhavika Gambhava.
[15] Abdul kader, Diaasalama and Mohiv Hadhoud, “Studying the Effect of Most Common Encryption Algorithms,” International Arab Journal of e-technology, Vol.2. No.1. 2014

R. Durga Prasad, R.N.D.S.S Kiran, Andey Krishnaji, "Advanced Data Encryption/Decryption using Multi Codes for One Character," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.34-38, 2015.

The data is organized by databases with the help of database management systems. DBMS is the collection of schemas, queries and other objects. To aggregate the data DBMS used Cartesian products between two or more tables and produce a result in a logical table. Where data is increasing rapidly day by day, so writing joins on large tables is difficult to data analysts and manage complex queries on large scale table is quite difficult to DBMS. To reduce complexity of manipulating large data schemaless databases are introduced. MongoDB process schemaless data and having more use cases to achieve parallel processing on data. Aggregation is one of the function which is applying on the data. To get fastest aggregation results use mongodb sharded cluster and mareduce.

NOSQL, MongoDB, Sharding, Parallelism, MapReduce

[1] J. M. Hellerstein, “The case for online aggregation”, Technical Report UCB//CSD-96-908, EECS Computer Science Division, University of California, Berkeley, CA,1996
[2] Jeffrey Dean and Sanjay Ghemawat “MapReduce: Simplified Data Processing on Large Clusters”, OSDI 2014
[3] Anju abraha,"A Dynamic Query Form System for Mongodb", SSRG-IJCSE, volume-1 issue-9, Nov 2014.
[4] MongoDB, “http://docs.mongodb.org/manual/”, Thursday, April 30, 2015.
[5] MongoDB, http://www.tutorialspoint.com/mongodb/, Monday, July 6, 2015
[6] Replication, http://stackoverflow.com, Tuesday, August 11, 2015
[7] Sharding, http://gist.github.com, Monday, August 17, 2015.

T. Mothilal and P. Anil Kumar, "Parallel Aggregation on Sharded Clusters," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.39-43, 2015.

A mobile ad-hoc network (MANET) is a temporary network that can change locations and configure itself on the fly. MANETs use wireless connections to connect various networks. The mobility and the easy use of mobile devices have motivated researches, to develop MANET protocols to exploit a reliable data transmission facilities provided by the mobile nodes. There are number of issues such as medium access control, routing, resource management, congestion control and power control which affects the reliability of secured communication in MANET. Due to the dynamic network topology, congestion control is an important issue in MANET. Congestion can occur in any intermediate node often due to limitation in resources, when data packets are being transmitted from the source to the destination nodes. The occurrence of congestion results in high data loss, long delay and waste of resource utilization time between mobile nodes. Hence the congestion control technique for detecting and overcoming of congestion is an important research work in MANET. The primary goal of congestion control is the maximum utilization of resources and keeps the load below the capacity. There are several techniques have been proposed for detecting and overcoming congestion in the MANET. This research paper reviews the various proposed congestion control techniques to control congestion in mobile ad-hoc network.

MANET, Congestion, Congestion Control

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[8] Geetika Maheshwari, Mahesh Gour,Umesh Kumar Chourasia A Survey on Congestion Control in MANET, International Journal of Computer Science and Information Technologies, Vol. 5, No.2 , pp 998-1001 , 2014.
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[14] Ritu Jain, Bhawana Pillai, A Survey on Congestion Problem of MANET” International Journal of Computer Science and Information Technologies, Vol. 6, No.2, pp. 947-951, 2015.
[15] Rong Pan, Balaji Prabhakar, and Konstantinos Psounis, A Stateless Active Queue Management Scheme for Approximating Fair Bandwidth Allocation, CHOKe, IEEE INFOCOM, pp. 942-951, Mar 2000.
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P. Dhivya and S. Meenakshi, "A Review of Congestion Control Techniques in Mobile Ad-hoc Network," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.44-49, 2015.

Wireless Sensor Network (WSN) is a technology that shows great promise for various futuristic applications both for public and private sectors. Its shows by few applications like disaster management system, battlefield environment and etc. In this connection many issues are considered among that Security is the main issue in the WSN. Unauthorized access in an entire networks leads to dilute the security. For providing security to this network between the nodes, we need some specialized algorithms. Previously many more algorithms are used to provide either data security or network security. Cryptographic strategies and Secure Algorithms are the key factor to ensuring that either data transmission or data handling between nodes are occurred securely. Id (Identity) Based Cryptography and Public Key Cryptography are the types of cryptography which are used to provide security to the data based on sharing keys between sender and receiver in the network.

Cryptography, Id Based Cryptography, Public Key Cryptography, Security, Wireless Sensor Network

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