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Conventional simulation techniques generate massive amounts of data that are analyzed using various applications. These simulations produce petabytes of data that strains the I/O and storage subsystem. To overcome the high latency in I/O operations, data is analyzed as it is generated, in-situ. This can be successfully achieved by enabling analysis techniques on the same HPC machine that is producing simulation by using the same hardware resources or on a separate analysis machine. In this research paper, we discuss state of the art techniques in this domain and support our conclusion by comparing pros and cons of each approach.

Big Data, Service Oriented Approach, Big Data paradigm

[1] Sergey V. Kovalchuk1, Artem V. Zakharchuk1, Jiaqi Liao1, Sergey V. Ivanov1, Alexander V. Boukhanovsky “A Technology for BigData Analysis Task Description using Domain-Specific Languages “
[2] The SAS versus R Debate, http://insidebigdata.com/2014/03/01/sas-versus-r/, March 1, 2014.
[3] Heba Aly, Mohammed Elmogy and Shereif Barakat , “Big Data on Internet of Things: Applications, Architecture, Technologies, Techniques, and Future Directions”, Nov 2015, Vol 4 No. 06, ISSN : 2319-7323
[4] Understanding BigData Processing and Analytics, http://www.developer.com/db/understanding-big-data-processing-and-analytics.html, September 9, 2013
[5] Scott Klasky at. al., “In situ data processing for extreme scale computing”
[6] Marzia Rivia,*, Luigi Caloria, Giuseppa Muscianisia, Vladimir Slavnicb, “In-situ Visualization: State-of-the-art and Some Use Cases” 1ORNL, 2 U.T. Knoxville, 3LBNL, 4Georgia Tech, 5Sandia Labs, 6 Rutgers, 7NREL, 8Kitware, 9UCSD, 10PPPL, 11UC Irvine, 12U. Utah, 13 Cal. Tech, 14Auburn University, 15NCSU
[7] The Scalable Data Management, Analysis and Visualization (SDAV) Institute, http://sdav-scidac.org/, SciDAC PI meeting 2015
[8] Khanh Nguyen Kai Wang Yingyi Bu Lu Fang Jianfei Hu Guoqing Xu, “FACADE: A Compiler and Runtime for (Almost) Object-Bounded Big Data Applications “ University of California, Irvine
[9] Kwan- Liu Ma, Chaoli Wang, Hongfeng Yu, Anna Tikhonova, “In-Situ Processing and Visualization for Ultrascale Simulations” Department of Computer Science, University of California at Davis, One Shields Avenue, Davis, CA 95616 SciDAC Institute for Ultrascale Visualization (IUSV)
[10] 2015 SAS vs. R Survey Results, http://www.burtchworks.com/2015/05/21/2015-sas-vs-r-survey-results/, May 21, 2015
[11] David Loshin, “ Big Data Analytics “,Morgan Kaufmann Publishers In, ISBN: 9780124186644

Mehjabeen Sultana, "An Overview of Emerging Analytics in Big Data: In-Situ," International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.166-169, 2016.

Cooperative banks are an important and growing part of many financial systems. Co-operative banking Sector is an important constituent of Multi Agency banking system operation in the country. These sectors play an important role in the economic enlistment of lower and middle income group of people.Co-operative banks are contributing the constituent part in the India’s banking and financial system. In present times all major economic transactions have started taking place digitally. The major trend of modern digital transactions is substantiated by use of database management. The data update is done almost automatically and is much faster. Users in present days can access their accounts directly without going to a bank making transfers, transactions and accessing cash directly without standing in long queues as was prevalent earlier using ATM machines. Agent acts as a bridge between bank and the customer .On employee-side the data is much more organized and accessing and performing actions on user accounts is easier for them. Due to this the bank has better work efficiency and customer experience improves as well.

Employee,Atm machine,agents

[1] Sachin R. Agrawal, “Problems faced by co-operative banks and perspectives in the Indian Economy”, IRACST – International Journal of Commerce, Business and Management (IJCBM), ISSN: 2319–2828Vol. 1, No.2, October 2012
[2] Anil Kumar Soni,”ROLE OF COOPERATIVE BANK IN AGRICULTURALCREDIT”, Abhinav National Monthly Refereed Journal OF Research In Commerce& Management, ISSN: 2277-1166VOLUME NO.1, ISSUE NO.10, 2012
[3] ShantanuBose,”URBAN CO-OPERATIVE BANKS IN INDIA: CURRENT SCENARIO”,Abhinav International Monthly Refereed Journal of Research in Management & Technology,ISSN-2320-0073 Volume 3,Issue 6 (June, 2014)
[4] Dr.V.R.Palanivelu,” A Study on Role of Salem District Central Cooperative Bank in Agricultural Financing WithSpecial Reference to Crop Loan in Salem District”, PARIPEX - INDIAN JOURNAL OF RESEARCH, ISSN - 2250-1991 Volume 3, Issue 9, September 2014
[5] Dr E. Gnanasekaran,”A study on the Urban Cooperative Banks Success and growth in Vellore District – Statistical Analysis”,International Journal of Advanced Research in Computer Science and Software Engineering,ISSN: 2277 128X Volume 2, Issue 3, March 2012
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[7] R.K. Datir,” A Review of Co-Operative Banking in India”.Online International Interdisciplinary Research Journal, ISSN2249-9598, Volume-II, Issue-VI, Nov-Dec 2012
[8] George Papageorgiou,” Management for Motivation and JobSatisfaction in the Banking Industry”, International Journal Of Economic And Statistics, Issue 1, Volume 1, 2013
[9] CUEVAS C.E., FISHER K.P. (2006): CooperativeFinancial Institutions: Issues in governance,regulation and supervision. World Bank, WorkingPaper, 82: 1–59.
[10] Justin Paul and Padmalatha Suresh (2008),“Management of Banking and Financial Services”, Second impression, Dorling Kindersley (India) Pvt. Ltd., PHI, Chapter: 6, 78-116.

Nandadevi. M. Honkanadavar, Anita.S.Patil, "An Efficient Banking System for Cooperative Banks," International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.170-172, 2016.

The main unit of CBIR ( Content based image retrieval ) is an image retrieval technique that used to retrieve from the database the most similar images to the query image. CBIR is convenient , fast and efficient over image search approaches. In online image retrieval, the user can submit a query to the retrieval system to search for greed images. This paper begins a different and powerful image Texture illustration based on local binary pattern texture features. The input image is divided into several image from which the Local binary pattern feature circulation are clipped and concatenated into an enhanced feature vector. The achievement of the proposed method is determined in the image texture recognition problem under The aim of this work is to find the best way for characterize a given texture using a binary pattern based method. Among given features edge and color evolution are perform by various kind of techniques but for texture analysis there are few method are available .The key objective of the proposed work is to obtain and efficient Algorithm for texture analysis. To find out the hidden texture for a particular given image.

CBIR , image retrieval, texture analysis, LBP, segmentation methods

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[5] Zhenhua Guo, Lei Zhang, Member and David Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification” , IEEE Transactions on Image Processing , 2002.
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Akanxa Mishra, Namrata Sharma, "A Survey of Analyzing Image Texture Using LBP with k Mean Clustering," International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.172-175, 2016.

There are communication commonly extend across smart-phones and devices with wider screens. Indeed, data might be received on the Smart devices but more conveniently processed with an application on a smart device, or vice versa. Such communication require automatic data sharing from a sending location on one screen to a receiving location on the other device screen. We bring out a touch screen device Swipe n Share technique to facilitate these communication involving multiple touchscreen devices, with minimal effort for the user. The technique is a two-handed device gesture, where one hand is used to suitably align the mobile phone with the larger screen, while the other is used to select and swipe an object between devices and choose which device receive the data.

Touch Screen Devices; Mobile devices; data transfer; Swipe-and Share

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Praful Khokale, Narendra Gunjal , Suhas B. Gote, "Touch Screen Device Swipe n Share," International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.176-179, 2016.

This paper aims to make performance analysis of various disk scheduling algorithms based on various factors. In these disks scheduling algorithms we consider First Come First Serve (FCFS), Shortest Seek Time First (SSTF), Scan, Look, C-Scan, and C-Look Scheduling Algorithms. Based on head movements of various disk scheduling algorithms we made performance analysis. These head movements are calculated for different disk scheduling algorithms while serving disk requests. Here we consider the performance factors such as access time, Disk throughput, Disk Utilization etc.

Disk scheduling,Seek Time,Disk throughput,Disk utilization

[1]. William Stallings, “Operating Systems Internals and Design Principles”, Pearson Education, Sixth (6th) Edition , ISBN: 97881317 2528-3.
[2]. Elmasri, Carrick, “Operating System –A Spiral Approach”, Tata McGraw-Hill Education, First Edition, ISBN NO: 9780071070942, Page No: 302-314.
[3]. Silberchatz, Peter B.Galvin, Greg Gange,”Operating systems Principles”, Willey Edition, Eighth(8th) Edition, ISBN:978812650962-1, Page No:440-444.
[4] C.Mallikarjuna, P.Chitti Babu,” Priority Based Disk Scheduling Algorithm”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.3, Issue 9, Page No:15954 -15959, Sept2014.
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C.Mallikarjuna, P.Chitti Babu, "Performance Analysis of Disk Scheduling Algorithms," International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.180-184, 2016.

We present a formal check approach for recognizing plan issues related to client interaction, with a center on client interface of restorative devices. The approach makes a novel use of arrangement charts proposed by Rushby to formally check essential human variables properties of client interface implementation. In particular, it first deciphers the programming execution of client interface into an equivalent formal specification, from which a behavioral model is developed utilizing hypothesis proving; human variables properties are then confirmed against the behavioral model; lastly, an exhaustive set of test inputs are produced by exploring the behavioral model, which can be utilized to challenge the certifiable interface execution and to guarantee that the issues recognized in the conduct model do apply to the implementation. We have prototyped the approach based on the PVS verification system, and connected it to examine the client interface of a certifiable restorative device. The investigation recognized several collaboration plan issues in the device, which may conceivably lead to serious consequences.

Programming Verification; Restorative Devices; Client Interfaces

[1] R.Priyanka; R.Sivakumar, “The Evaluation of Medical Device Interaction Based Prototype Verification System Using Human Operator Model”, in International Journal of Computer Sciences and Engineering Volume-4, Issue-4, Year-2016.
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[3] Meng Zhang; Anand Raghunathan; Niraj K. Jha, “MedMon: Securing Medical Devices through Wireless Monitoring and Anomaly Detection”, IEEE Transactions on Biomedical Circuits and Systems, Year: 2013, Volume: 7, Issue: 6, Pages: 871 – 881.
[4] Seungwoo Lee; Nam Kim, “Measurement and analysis of the electromagnetic fields radiated by themedical devices”, 2015 9th International Symposium on Medical Information and Communication Technology (ISMICT), Year: 2015, Pages: 56 – 58.
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[7] Marcantonio Catelani; Lorenzo Ciani; Chiara Risaliti, “Risk assessment in the use of medical devices: A proposal to evaluate the impact of the human factor”, Medical Measurements and Applications (MeMeA), 2014 IEEE International Symposium on, Year: 2014, Pages: 1 – 6.
[8] Michael R. Neuman; Gail D. Baura; Stuart Meldrum; Orhan Soykan; Max E. Valentinuzzi; Ron S. Leder; Silvestro Micera; Yuan-Ting Zhang, “Advances in Medical Devices and Medical Electronics”, Proceedings of the IEEE, Year: 2012, Volume: 100, Issue: Special Centennial Issue, Pages: 1537 – 1550.
[9] Homa Alemzadeh; Ravishankar K. Iyer; Zbigniew Kalbarczyk; Jai Raman, “Analysis of Safety-Critical Computer Failures in Medical Devices”, IEEE Security & Privacy, Year: 2013, Volume: 11, Issue: 4, Pages: 14 – 26.

R.Priyanka, R.Sivakumar, "The Evaluation of Medical Device Interaction Based Prototype Verification System Using Human Operator Model," International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.185-189, 2016.

The sway and popularity of competition idea has been expanding in the last decades and this idea has escalated the significance of giving right choice for organizations. Choice producers have encountered the certainty of utilizing appropriate scientific techniques instead of utilizing intuitive and emotional choices in choice making process. In this context, numerous choice support models and applicable frameworks are still being created in request to help the vital administration mechanisms. There is moreover a basic need for robotized approaches for viable and efficient utilization of monstrous sum of Information to support corporate and individuals in vital planning and decision-making. Information mining systems have been utilized to reveal hidden designs and relations, to abridge the Information in novel ways that are both reasonable and profitable to the executives and moreover to foresee future patterns and behaviors in business. There has been a substantial body of relook and practice focus on diverse Information mining systems and methodologies. In this study, a substantial volume of record set extracted from an outpatient clinic’s therapeutic database is utilized to apply Information mining techniques. In the first stage of the study, the raw Information in the record set are collected, preprocessed, cleaned up and eventually changed into a reasonable design for Information mining. In the second phase, some of the affiliation guideline calculations are connected to the Information set in request to reveal rules for measuring the relationship between some of the properties in the therapeutic records. The results are watched and comparative investigation of the watched results among diverse affiliation calculations is made. The results appeared us that some basic and reasonable relations exist in the outpatient facility operations of the hospital which could help the hospital administration to change and improve their administrative methodologies regarding the quality of administrations given to outpatients.

Choice Making, Therapeutic Records, Information Mining, Affiliation Rules, Outpatient Clinic

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S.Narmatha, R.Sivakumar, "The Impact of Computerized Information in Health Centers for Efficient Patient Records," International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.190-195, 2016.