Volume-3 , Issue-11 , Nov - 2015 Go Back

Currently suggestions are opening to appreciate the implication of operating additional data in application to care high-quality for their strategies. It was said and proved through study cases that “More Data usually beats better algorithms”. With this announcement suggestions ongoing to recognize that they be able to select to participate further in formulating superior collections of data moderately than advancing in costly algorithms. The significant volume of data is enhanced operated by way of a total the conceivable associations on a more extent, interactions that be able to certainly not be institute if the data is analyzed on separate sets or on a littler set. A greater sum of Data gives a better output but moreover working with it can become a challenge due to preparing limitations. This in this paper intends to characterize the idea of Big Data and stress the significance of Big Data Analytics.

Big Data, Big Data Analytics, Data Acquisition, Data Generation, Data Storage

[1] Jinson Zhang; “BigData visualization: Parallel coordinates using density approach“ Published in: Systems and Informatics (ICSAI), 2014 2nd International Conference on Date of Conference: 15-17 Nov. 2014 Page(s): 1056 – 1063.
[2] Yu, M. “Invited Speech: BigData application for 28nm and below “Published in: e-Manufacturing & Design Collaboration Symposium (eMDC), 2012 Date of Conference: 4-4 Sept. 2012] Page(s): 1
[3] D'Ambrosio, R.; Belhajali, W.; Barlaud, M. “Boosting Stochastic Newton Descent for Bigdata large scale classification“ Published in: Big Data (Big Data), 2014 IEEE International Conference on Date of Conference: 27-30 Oct. 2014 Page(s): 36 - 41.
[4] Devgun, A.; “A machine learning adaptive approach to remove impurities over Bigdata” Published in: Electronics, Communication and Computational Engineering (ICECCE), 2014 International Conference on Date of Conference: 17-18 Nov. 2014 Page(s): 220 - 225
[5] Ramamoorthy, S.; “Optimized data analysis in cloud using BigData analytics techniques” Published in: Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on Date of Conference: 4-6 July 2013 Page(s): 1 – 5
[6] Shidaganti, G.; Prakash, S. “Feedback analysis of unstructured data from collaborative networking a BigData analytics approach” Published in: Circuits, Communication, Control and Computing (I4C), 2014 International Conference on Date of Conference: 21-22 Nov. 2014 Page(s): 343 - 347
[7] Pradhananga, Y.; Karande, S.; Karande, C. “CBA: Cloud-Based Bigdata Analytics” Published in: Computing Communication Control and Automation (ICCUBEA), 2015 International Conference on Date of Conference: 26-27 Feb. 2015 Page(s): 47 – 51.
[8] Arun, J.; Hazaruthin, M.M. ; Karthik, M. “Analytics as a service delivery model for the cloud“ Published in: Engineering and Technology (ICETECH), 2015 IEEE International Conference on Date of Conference: 20-20 March 2015 Page(s): 1 - 5
[9] Ghazali, N.; Shahidan, W.N.W. ; Din, Z. ; Rahim, A. more authors “Remote data acquisation and QC tools for vehicle navigation mapping solution“ Published in: Space Science and Communication (IconSpace), 2011 IEEE International Conference on Date of Conference: 12-13 July 2011 Page(s): 217 – 220.
[10] Yasar, Onur; “Developing easy pluggable test equipments for factory and development testing of air defense gun system“ Published in: IEEE AUTOTESTCON, 2015 Date of Conference: 2-5 Nov. 2015 Page(s): 169 – 172.
[11] Konik, A. ; Johnson, K.L.; Dasari, P. “Digital anthropomorphic phantoms of non-rigid human respiratory and voluntary body motions: A tool-set for investigating motion correction in 3D reconstruction“ Published in: Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE Date of Conference: 23-29 Oct. 2011 Page(s): 3572 – 3578
[12] Yen-Feng Li; Taipei, Taiwan; Pai-Chi Li “Ultrasound beamforming with compressed data“ Published in: Ultrasonics Symposium (IUS), 2010 IEEE Date of Conference: 11-14 Oct. 2010Page(s): 1912 – 1915.
[13] Xinzhong Liu; Gaochao Xu; Xiaodong Fu; Yushuang Dong “Test Data Generation Considering Data Dependence” Published in: Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference on Date of Conference: 18-22 Aug. 2010 Page(s): 208 – 213
[14] Romli, R.; Sulaiman, S.; Zamli, K.Z. “Test data generation framework for Automatic Programming Assessment” Published in: Software Engineering Conference (MySEC), 2014 8th Malaysian Date of Conference: 23-24 Sept. 2014 Page(s): 84 - 89
[15] Hameed, S.A.; Int. Islamic Univ. Malaysia, Gombak, Malaysia; Deraman, A. ; Hamdan, A.R. “Framework for Intelligent Meaningful Test Data Generation model-IMTDG” Published in: TENCON 2000. Proceedings (Volume:2 ) Date of Conference: 2000 Page(s): 16 - 22 vol.2
[16] Zhao, R. ; Qing Li “Automatic Test Generation for Dynamic Data Structures” Published in: Software Engineering Research, Management & Applications, 2007. SERA 2007. 5th ACIS International Conference on Date of Conference: 20-22 Aug. 2007 Page(s): 545 – 549
[17] Alnafoosi, A.B.; Coll. of Comput. & Digital Media, DePaul Univ., Chicago, IL, USA; Steinbach, T. “An integrated framework for evaluating big-data storage solutions - IDA case study“ Published in: Science and Information Conference (SAI), 2013 Date of Conference: 7-9 Oct. 2013 Page(s):947 – 956
[18] Roos, A.; Drusedow, S. ; Hosseini, M.I. ; Coskun, G. “Trust Level Based Data Storage and Data Access Control in a Distributed Storage Environment” Published in: Mobile Cloud Computing, Services, and Engineering (MobileCloud), 2015 3rd IEEE International Conference on Date of Conference: March 30 2015-April 3 2015 Page(s): 169 – 176
[19] Wuling Ren ; Hangzhou; Pan Zhou “Research on the Data Storage and Access Model in Distributed Environment“ Published in: Computer Engineering and Technology, 2009. ICCET '09. International Conference on (Volume:1 ) Date of Conference: 22-24 Jan. 2009 Page(s): 134 – 136.
[20] Coufal, H.; “Holographic data storage or reliability; a status report“ Published in: Optical Data Storage Topical Meeting, 1997. ODS. Conference Digest Date of Conference: 7-9 April 1997 Page(s): 46 – 47.

P.Kodimalar, "A Study on Big Data and Big Data Analytical Research and Issues," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.171-179, 2015.

Heart infection is an alternately cause of horribleness also, mortality in modern society. Restorative finding is extremely essential but confounded undertaking that should be performed precisely also, efficiently. Although noteworthy progress has been made in the finding also, treatment of heart disease, further investigation is still needed. The capacity of colossal amounts of restorative Information leads to the need alternately powerful Information examination instruments to remove helpful knowledge. There is a colossal Information capable within the healthcare systems. However, there is a undertaking of powerful examination instruments to find hidden connections also, patterns in data. Information revelation also, Information mining have found various application in business also, exploratory domain. Researchers have long been concerned with applying factual also, Information mining instruments to improve Information examination on substantial Information sets. Infection finding is one of the applications where Information mining instruments are proving successful results. This relook paper proposed to find out the heart maladies unpleasant Information mining, Support alternately Machine (SVM), Hereditary Algorithm, unpleasant set theory, affiliation rules also, Neural Networks. In this study, we briefly examined that out of the above routines Choice tree also, SVM is most powerful alternately the heart disease. So it is observed that, the Information mining could help in the identification alternately the expectation of high alternately low hazard heart diseases.

Information Mining, Heart Disease, SVM, Unpleasant Sets Techniques, Affiliation Rules & Clustering

[1] Wang, Guoyin; Jun Hu; Qinghua Zhang; Xianquan Liu "Granular computing based data mining in the views of rough set and fuzzy set" Published in: Granular Computing, 2008. GrC 2008. IEEE International Conference on Date of Conference: 26-28 Aug. 2008 Page(s): 67.
[2] Jagannathan, G.; Wright, R.N. "Seventh IEEE International Conference on Data Mining Workshops - Title" Published in: Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on Date of Conference: 28-31 Oct. 2007 Page(s): i – iii
[3] Shahriar, M.S.; Anam, S. "Quality Data for Data Mining and Data Mining for Quality Data: A Constraint Based Approach in XML" Published in: Future Generation Communication and Networking Symposia, 2008. FGCNS '08. Second International Conference on (Volume: 2) Date of Conference: 13-15 Dec. 2008 Page(s): 46 – 49.
[4] Xiaoming Liu; Wuhan, China; Jinshan Tang "Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based Feature Selection Method" Published in: Systems Journal, IEEE (Volume:8 , Issue: 3 ) Date of Publication : 13 November 2013 Page(s) 910 – 920.
[5] Shahid, N.; Naqvi, I.H.; Bin Qaisar, S. "Quarter-Sphere SVM: Attribute and Spatio-Temporal correlations based Outlier & Event Detection in wireless sensor networks" Published in: Wireless Communications and Networking Conference (WCNC), 2012 IEEE Date of Conference: 1-4 April 2012 Page(s): 2048 – 2053.
[6] Sarafis, I.; Diou, C.; Tsikrika, T.; Delopoulos, A. "Weighted SVM from clickthrough data for image retrieval" Published in: Image Processing (ICIP), 2014 IEEE International Conference on Date of Conference: 27-30 Oct. 2014 Page(s): 3013 – 3017.
[7] Mishima, T.; Takahashi, K.; Kawamura, T.; Sugahara, K., “Meeting Scheduling System Using Unpleasant Notification”, Published in: ITS Convergence and Security (ICITCS), 2013 International Conference on Date of Conference: 16-18 Dec. 2013 Page(s): 1 – 4.
[8] Kashihara, K.; Fukumi, M. “Development of automatic filtering system for individually unpleasant data detected by pupil-size change”, Published in: Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on Date of Conference: 9-12 Oct. 2011 Page(s): 3311 – 3316.
[9] Shibuya, K.; Ryukoku Komatsu, S. “Influence of pleasant and unpleasant feelings on human gesture motion”, Published in: Intelligent Robots and Systems, 2002. IEEE/RSJ International Conference on (Volume: 2) Date of Conference: 2002 Page(s): 1240 - 1245 vol.2.
[10] Hruschka, E.R.; Ebecken, N.F.F. “Applying a clustering genetic algorithm for extracting rules from a supervised neural network”, Published in: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Volume: 3) Date of Conference: 2000 Page(s): 407 - 412 vol.3.
[11] Zheng Shangzhi; Chaohu, China; Zhao Xiaolong; Zhang Buqun; Bu Hualong, “Web Document Clustering Research Based on Granular Computing”, Published in: Electronic Commerce and Security, 2009. ISECS '09. Second International Symposium on (Volume: 2) Date of Conference: 22-24 May 2009 Page(s): 446 – 450.

K.Selvi, "Identify Heart Diseases Using Data Mining Techniques: an Overview," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.180-187, 2015.

Virtualization innovation has become commonplace in advanced information focuses and group systems, frequently referred as “Registering Clouds”. In particular, the capacity of virtual machine (VM) development brings various advantages such as higher performance, improved reasonability and flaw tolerance. Moreover, live development of VMs frequently permits workload development with a short administration downtime. However, administration levels of running applications are likely to be adversely influenced amid a live VM migration. Alternately this reason, a better understanding of its impacts on framework execution is exceedingly desirable. In this paper, we present a execution assessment on the impacts of live development of virtual machines on the execution of applications running inside Xen VMs. Results appear that in most cases, development overhead is worthy however can’t be disregarded, particularly in frameworks where administration accessibility and responsiveness are administered by strict Administration Level Understandings (SLAs). Despite that, there is a high potential alternately live development materialness in information focuses serving enterprise-class Web applications. Our results are based on a workload formed of a genuine application, covering the space of multi-level Web 2.0 applications.

Live Migration, Registering Clouds, Xen, Virtual Machine

[1] Chanchio, K.; Dept. of Comput. Sci., Thammasat Univ., Patumtani, Thailand; Thaenkaew, P.” Time-Bound, Thread-Based Live Migration of Virtual Machines”, Published in: Cluster, Cloud and Grid Computing (CCGrid), 2014 14th IEEE/ACM International Symposium on Date of Conference: 26-29 May 2014 Page(s):364 – 373.
[2] Anala, M.R.; Dept. of Comput. Sci. & Eng., RVCE, Bangalore, India; Shetty, J.; Shobha, G.” A framework for secure live migration of virtual machines”, Published in: Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on Date of Conference: 22-25 Aug. 2013 Page(s): 243 – 248.
[3] Shah, S.A.R.; Korea Univ. of Sci. & Technol., Daejeon, South Korea; Jaikar, A.H.; Seo-Young Noh,” A performance analysis of precopy, postcopy and hybrid live VM migration algorithms in scientific cloud computing environment”, Published in: High Performance Computing & Simulation (HPCS), 2015 International Conference on Date of Conference: 20-24 July 2015 Page(s): 229 – 236.
[4] Deshpande, U.; Binghamton Univ., Binghamton, NY, USA; Keahey, K.” Traffic-Sensitive Live Migration of Virtual Machines”, Published in: Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on Date of Conference: 4-7 May 2015 Page(s): 51 – 60.
[5] Kejiang Ye; Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China; Xiaohong Jiang; Ran Ma; Fengxi Yan” VC-Migration: Live Migration of Virtual Clusters in the Cloud”, Published in: Grid Computing (GRID), 2012 ACM/IEEE 13th International Conference on Date of Conference: 20-23 Sept. 2012 Page(s): 209 – 218.
[6] Ni, K.; MIT Lincoln Lab., Lexington, MA, USA; Armstrong-Crews, N.; Sawyer, S.” Geo-registering 3D point clouds to 2D maps with scan matching and the Hough Transform”, Published in: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on Date of Conference: 26-31 May 2013 Page(s):1864 – 1868.
[7] Heejeong Lee; Inf. Technol. Lab., Seoul Nat. Univ.; Sang-Chul Lee; Young-Wha Seo; Tae-Wan Kim,” Registering 3D Scanned Point Cloud Using Markers International Joint Conference 2006 (SICE-ICCAS 2006)”, Published in: SICE-ICASE, 2006. International Joint Conference Date of Conference: 18-21 Oct. 2006 Page(s): 4634 – 4638.
[8] Hall, D.J.; Stanford Research Institute; Endlich, R.M.; Wolf, D.E.; Brain, A.E.” Objective Methods for Registering Landmarks and Determining Cloud Motions from Satellite Data”, Published in: Computers, IEEE Transactions on (Volume:C-21 , Issue: 7 ) Page(s): 768 – 776.
[9] Bucksch, A.; Dept. of Geosci. & Remote Sensing, Delft Univ. of Technol., Delft, Netherlands; Khoshelham, K.” Localized Registration of Point Clouds of Botanic Trees”, Published in: Geoscience and Remote Sensing Letters, IEEE (Volume:10 , Issue: 3 ) Page(s): 631 – 635.
[10] Irfanoglu, M.O.; Bogazici Univ., Istanbul, Turkey; Gokberk, B.; Akarun, L.” 3D shape-based face recognition using registered surface similarity”, Published in: Signal Processing and Communications Applications Conference, 2004. Proceedings of the IEEE 12th Date of Conference: 28-30 April 2004 Page(s): 571 – 574.
[11] Sisu Xi; Dept. of Comput. Sci. & Eng., Washington Univ. in St. Louis, St. Louis, MO, USA; Wilson, J.; Chenyang Lu; Gill, C.” RT-Xen: Towards real-time hypervisor scheduling in Xen”, Published in: Embedded Software (EMSOFT), 2011 Proceedings of the International Conference on Date of Conference: 9-14 Oct. 2011 Page(s): 39 – 48.
[12] Yun Chan Cho; SungKyunKwan Univ., Suwon; Jae Wook Jeon” Sharing data between processes running on different domains in para-virtualized xen” Published in: Control, Automation and Systems, 2007. ICCAS '07. International Conference on Date of Conference: 17-20 Oct. 2007 Page(s):1255 – 1260.
[13] Weikuan Yu; Comput. Sci. & Math., Oak Ridge Nat. Lab., Oak Ridge, TN; Vetter, J.S.” Xen-Based HPC: A Parallel I/O Perspective”, Published in: Cluster Computing and the Grid, 2008. CCGRID '08. 8th IEEE International Symposium on Date of Conference: 19-22 May 2008 Page(s): 154 – 161.
[14] Tafa, I.; Inf. Technol. Fac., Polytech. Univ. of Tirana, Tirana, Albania; Beqiri, E.; Paci, H.; Kajo, E.” The Evaluation of Transfer Time, CPU Consumption and Memory Utilization in XEN-PV, XEN-HVM, OpenVZ, KVM-FV and KVM-PV Hypervisors Using FTP and HTTP Approaches”, Published in:Intelligent Networking and Collaborative Systems (INCoS), 2011 Third International Conference on Date of Conference: Nov. 30 2011-Dec. 2 2011 Page(s): 502 – 507.
[15] Kun Cheng; State Key Lab. of Software Dev. Environ., Beihang Univ., Beijing, China; Yuebin Bai; Rui Wang; Yao Ma” Optimizing Soft Real-Time Scheduling Performance for Virtual Machines with SRT-Xen”, Published in: Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on Date of Conference: 4-7 May 2015 Page(s): 169 – 178.
[16] Yifeng Sun; Dept. of Comput. Sci. & Technol., Peking Univ., Beijing, China; Yingwei Luo; Xiaolin Wang; Zhenlin Wang,” Fast Live Cloning of Virtual Machine Based on Xen”, Published in: High Performance Computing and Communications, 2009. HPCC '09. 11th IEEE International Conference on Date of Conference: 25-27 June 2009 Page(s):392 – 399.
[17] Jyun-Shiung Yang; Grad. Inst. of Networking & Multimedia, Nat. Taiwan Univ., Taipei, Taiwan; Pangfeng Liu; Jan-Jan Wu” Workload characteristics-aware virtual machine consolidation algorithms”, Published in: Cloud Computing Technology and Science (CloudCom), 2012 IEEE 4th International Conference on Date of Conference: 3-6 Dec. 2012 Page(s):42 – 49.
[18] Liu Guangqi; Shandong Comput. Sci. Center, Shandong Provincial Key Lab. of Comput. Network, Jinan, China; Wang Lianhai; Zhang Shuhui; Xu Shujiang,” Memory dump and forensic analysis based on virtual machine”, Published in: Mechatronics and Automation (ICMA), 2014 IEEE International Conference on Date of Conference: 3-6 Aug. 2014 Page(s): 1773 – 1777.
[19] Ajay Kumara, M.A.; Dept. of Inf. Technol., Nat. Inst. of Technol., Mangalore, India; Jaidhar, C.D.” Virtual machine introspection based spurious process detection in virtualized cloud computing environment”, Published in:
[20] Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), 2015 International Conference on Date of Conference: 25-27 Feb. 2015 Page(s): 309 - 315

D.Ragupathi and S.Santhanaarokiajohnsy, "Towards Live Migration Using Rule Trigger Secure Policies in Virtual Machines," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.188-195, 2015.

Given the widespread use of modern information technology, a substantial number of time arrangement might be gathered amid ordinary business operations. We use a fast-sustenance eatery establishment as a case to represent how information mining can be connected to such time series, and help the establishment harvest the advantages of such an effort. Time arrangement information mining at both the store level and corporate level are discussed. Box–Jenkins regular ARIMA models are utilized to investigate and gauge the time series. Instead of a customary manual approach of Box–Jenkins modelling, a programmed time arrangement displaying strategy is utilized to investigate a substantial number of exceedingly occasional time series. In addition, a programmed anomaly discovery and alteration strategy is utilized for both model estimation and forecasting. The change in gauge execution due to anomaly alteration is demonstrated. Alteration of gauges based on stored chronicled gauges of like occasions is moreover discussed. Anomaly discovery moreover leads to information that can be utilized not just for better stock administration and planning, but moreover to recognize potential deals opportunities. To represent the feasibility and straightforwardness of the above programmed strategies for time arrangement information mining, the SCA measurable framework is utilized to per structure the related analysis.

Programmed Time Arrangement Modeling; Programmed Exception Detection; Outliers; Forecasting; Master System; Learning Discovery

[1] Sui Yin ; Dept. of Open Teaching, Anyang Univ. of Radio & Telev., Anyang, China ; Lv Hong-jie ; Li Gang,” Modeling of the reasonable arrangement of hospital bed and its emulation”, Published in: Instrumentation & Measurement, Sensor Network and Automation (IMSNA), 2012 International Symposium on (Volume:1 ) Date of Conference: 25-28 Aug. 2012 Page(s): 330 – 333.
[2] Xin-lin Chen ; Shenzhen Inst. of Inf. Technol., Shenzhen, China,” Notice of Retraction Balanced production arrangement of orders under capacity uncertainty” Published in: Advanced Management Science (ICAMS), 2010 IEEE International Conference on (Volume:1 ) Date of Conference: 9-11 July 2010 Page(s): 435 – 439.
[3] Yingjun Ma ; Inst. of Math. & Stat., Central China Normal Univ., Wuhan, China ; Chu Chen ; Xiaona Sun ; Dongfang Zhao,” Comparing programming model with genetic algorithm model to the aircrew problem”, Published in: Natural Computation (ICNC), 2014 10th International Conference on Date of Conference: 19-21 Aug. 2014 Page(s): 1148 – 1152.
[4] Lippert, M. ; Dept. of Comput. Sci., Hamburg Univ., Germany ; Lopes, C.V.” A study on exception detection and handling using aspect-oriented programming”, Published in: Software Engineering, 2000. Proceedings of the 2000 International Conference on Date of Conference: 2000 Page(s): 418 – 427.
[5] Xiaoquan Wu ; Technol. Center of Software Eng., Chinese Acad. of Sci., Beijing, China ; Zhongxing Xu ; Jun Wei”, Static Detection of Bugs Caused by Incorrect Exception Handling in Java Programs”, Published in: Quality Software (QSIC), 2011 11th International Conference on Date of Conference: 13-14 July 2011 Page(s): 61 – 66.
[6] Priyambadha, B. ; Program Studi Teknik Informatika, Univ. Brawijaya, Malang, Indonesia ; Rochimah, S.” Case study on semantic clone detection based on code behavior”, Published in: Data and Software Engineering (ICODSE), 2014 International Conference on Date of Conference: 26-27 Nov. 2014 Page(s): 1 – 6.
[7] ZiHua Yang ; Sch. of Biosci., Univ. of Exeter, Exeter, UK ; Zheng Rong Yang,” Detection of non-structural outliers for microarray experiments”, Published in: Neural Networks (IJCNN), 2014 International Joint Conference on Date of Conference: 6-11 July 2014 Page(s): 1600 – 1605.
[8] Sitanggang, I.S. ; Comput. Sci. Dept., Bogor Agric. Univ., Bogor, Indonesia ; Baehaki, D.A.M.” Global and collective outliers detection on hotspot data as forest fires indicator in Riau Province, Indonesia”, Published in: Spatial Data Mining and Geographical Knowledge Services (ICSDM), 2015 2nd IEEE International Conference on Date of Conference: 8-10 July 2015 Page(s): 66 – 70.
[9] Taha, A. ; Fac. of Comput. & Inf., Cairo Univ., Giza, Egypt ; Hadi, A.S.,” A general approach for automating outliers identification in categorical data”, Published in: Computer Systems and Applications (AICCSA), 2013 ACS International Conference on Date of Conference: 27-30 May 2013 Page(s): 1 – 8.
[10] Shu Fan ; Bus. & Econ. Forecasting Unit, Monash Univ., Clayton, VIC, Australia ; Yuan-Kang Wu ; Wei-Jen Lee ; Ching-Yin Lee,” Comparative study on load forecasting technologies for different geographical distributed loads”, Published in: Power and Energy Society General Meeting, 2011 IEEE Date of Conference: 24-29 July 2011 Page(s): 1 – 8.
[11] Cao Ning ; Chongqing Electr. Power Co., Chongqing, China ; Huang Jian-jun ; Xie Xiao-min,” Study and Application of Dynamic Collocation of Variable Weights Combination Forecasting Model”, Published in: Dependable, Autonomic and Secure Computing (DASC), 2013 IEEE 11th International Conference on Date of Conference: 21-22 Dec. 2013 Page(s): 404 – 409.
[12] Guo-rui Ji ; North China Electr. Power Univ., Beijing ; Pu Han ; Yong-Jie Zhai,” Wind Speed Forecasting Based on Support Vector Machine with Forecasting Error Estimation”, Published in: Machine Learning and Cybernetics, 2007 International Conference on (Volume:5 ) Date of Conference: 19-22 Aug. 2007 Page(s): 2735 – 2739.
[13] Slattery, D.M. ; Limerick Univ.” Using Information and Communication Technologies to Support Deep Learning in a Third-Level On-Campus Programme: A Case Study of the taught Master of Arts in E-Learning Design and Development at the University of Limerick”, Published in: International Professional Communication Conference, 2006 IEEE Date of Conference: 23-25 Oct. 2006 Page(s): 170 – 182.
[14] Chuantao Yin ; Lab. LIESP, Ecole Centrale de Lyon, Ecully, France ; David, B. ; Chalon, R.” A contextual mobile learning system for mastering domestic and professional equipments”, Published in: IT in Medicine & Education, 2009. ITIME '09. IEEE International Symposium on (Volume:1 ) Date of Conference: 14-16 Aug. 2009 Page(s): 773 – 779.
[15] Murthy, S. ; IIC Acad., Visakhapatnam, India ; Naraharisetty, P.” Investigating the Effectiveness of Non-interactive Video-Based Lectures in e-Learning - Case Study at Master of Science in Information Technology (MSIT) Program”, Published in: Technology for Education (T4E), 2011 IEEE International Conference on Date of Conference: 14-16 July 2011 Page(s): 149 – 155.
[16] Sarah, L.L. ; Lab. Sch., Indonesia Univ. of Educ., Bandung, Indonesia ; Prihatmanto, A.S. ; Rusmin, P.H.” The design and implementation discovery learning method on virtual museum of Indonesia:(A case study museum of geology for rock materials)”, Published in: System Engineering and Technology (ICSET), 2012 International Conference on Date of Conference: 11-12 Sept. 2012 Page(s): 1 – 5.
[17] D'Mello, D.A. ; Dept. of Comput. Sci. & Eng., St. Joseph Eng. Coll., Mangalore, India ; Achar, R.” A broker based architecture for e-learning Web services discovery”, Published in: Information and Communication Technologies (WICT), 2011 World Congress on Date of Conference: 11-14 Dec. 2011 Page(s): 688 – 693.
[18] Li, Taihua ; The Research Center of Science Educataon Southwest University Chongqing, China ; Liao, Boqin,” An experimental study on the primary model of the application of computer simulation to scientific discovery learning in middle school physics”, Published in: E -Business and E -Government (ICEE), 2011 International Conference on Date of Conference: 6-8 May 2011 Page(s): 1 – 4.

G.Vijayasree and Pon Periasamy, "Data Mining Scheduled Phase Sequence: A Graphic Developing Fast-Food Formation Records," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.196-209, 2015.

Job scheduling is an elementary characteristic of an operating system. The perception is to have system resources shared by a number of processes. A number of steps need to be performed to execute a program. Instructions and data must be loaded into main memory, I/O devices and files must be initialized, and other resources must be prepared. The efficiency of a system solely is subject to the use of job scheduling algorithm in a multi-programmed system. This paper begins with a brief representation of task or job sets, followed by a discussion about different type of job scheduling algorithms. In addition, the elaboration of comparative study of the entire scheduling algorithm along with proposed work is also given. This manuscript represents the simulation design of proposed CPU scheduling algorithm called MIN-MAX which is both preemptive and non-preemptive in nature. This work encompasses a software tool which produces a wide-ranging simulation of a number of CPU scheduling algorithms and provides the output in the form of scheduling performance metrics. The main objective of the paper is to analyze the performance of different algorithms with the proposed algorithm that results in minimum average waiting time and context switches. The major focus is to improve the system efficiency in multi programming system and also reduces the starvation problem among minimum and maximum burst time processes.

Process Scheduling, First Come First Serve (FCFS), Round Robin (RR), MIN-MAX Algorithm, Simulation Design, Starvation, Complexity Analysis

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Karan Sukhija, "Min-Max Simulation: An Execution Design of Process Scheduling Algorithm," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.210-216, 2015.

Through in early fifties Watson (a biologist) and crick (a physicist) published their (Nature, 1953) and proposing double helix model for the DNA structure but unwinding of the two strands remained a problem and is still unsolved. The major criticism against the double helix proposition is that the two chains will have to unwound in order to expose the bases, whose ordered sequences gives the genetic information, so that replication may occur and a new complementary strand may from. Now our submission is to take up the problem with quantum approach and we refer to the two faces of DNA structure, both of which are equally satisfactory pictures and we might guess that they should equal energies. But there should be certain amplitude that all the electrons can flip from one condition to the other shifting the position of the unfiled position to the opposite end. There are nevertheless, the usual two stationary states. |1> and | 2> which may be the sum of and difference combination of two base states 1> and 2>. The rate of flipping and therefore |A> should be sensitive to the complete structure of the molecule, then by changing A the energy might be splitting and hence frequency getting changed effecting the mating behaviour of the gamete. It is the behavioural phenomenon which we called mood of the gamete. Also the molecule therein does not have perfectly symmetrical. The same basic phenomenon should exist with slide modifications, even if there is some treatment slight asymmetries in the molecules could as well be introduced changing the mood. Still another feature of such a molecule is that in the two base states shown the centre of electronic charge is found to be located at different palaces thereby, giving evidence of influences due to subjective magnetic is electronic field. The DNA molecules have a quantum Mechanical behaviour and that flipping of the electronics are responsible for such behaviour.

Quantum Computing, Qubits, Quantum mechanics, gates

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Md. Shibli Rahmani, "Interpretation of Behaviour of DNA – Molecules by Quantum Mechanics," International Journal of Computer Sciences and Engineering, Vol.3, Issue.11, pp.217-220, 2015.