Nagesh H R

Work place: MITE/CSE, Moodabidri, 574227, India



Research Interests: Computer Science & Information Technology, Application Security, Information Security, Network Security, Digital Library, Information-Theoretic Security


Dr. Nagesh H.R, Dean (Academic), Professor & Head, Department of Computer Science & Engineering, Mangalore Institute of Technology & Engineering, Moodbidri, has got his M.Tech and Ph.D (Computer Engineering) from NITK Surathkal. He has published more than 50 research papers in National and International Conferences and journals. He has delivered more than 20 invited talks in topics like 'Component Based Software Development', 'Internet Security', 'Web Security', 'Web Engineering', 'Information Security' ,'Network Management', 'Promoting Global Cyber Security' ,'Security issues in Distributed Systems', 'Digital library and Information Search', 'Information Security Management' ,'Recent Trends in Information Technology' and 'Security issues in Cloud Computing'. He has also chaired many sessions in International and National level technical paper presentations. He has also published one chapter titled 'Proactive models for Mitigating Internet DoS/DDoS Attacks', in 'Selected Topics in Communication Networks and Distributed Systems', World Scientific, London, April 2010. He had also worked as Visiting faculty to NITK Surathkal and NITK-Science and Technology Entrepreneurs Park, Karnataka, Surathkal. Published two books titled 'Fundamentals of CMOS VLSI Design' for V semester Electronics & Communication Engineering students of VTU: Pearson Education & 'VLSI Design' for V semester Electronics & Communication Engineering students of JNTU: Pearson Education. Member of BOS for PG studies in Computer Science at Mangalore University and Manipal Institute of Technology for PG studies in Computer Science & Engineering. Worked as member of BOE and Exam coordinator in VTU Belgaum. Member of BOS in Computer Science & Engineering of VTU Belgaum for year 2013 to 2016.

Author Articles
High Performance Computation of Big Data: Performance Optimization Approach towards a Parallel Frequent Item Set Mining Algorithm for Transaction Data based on Hadoop MapReduce Framework

By Guru Prasad M S Nagesh H R Swathi Prabhu

DOI:, Pub. Date: 8 Jan. 2017

The Huge amount of Big Data is constantly arriving with the rapid development of business organizations and they are interested in extracting knowledgeable information from collected data. Frequent item mining of Big Data helps with business decision and to provide high quality service. The result of traditional frequent item set mining algorithm on Big Data is not an effective way which leads to high computation time. An Apache Hadoop MapReduce is the most popular data intensive distributed computing framework for large scale data applications such as data mining. In this paper, the author identifies the factors affecting on the performance of frequent item mining algorithm based on Hadoop MapReduce technology and proposed an approach for optimizing the performance of large scale frequent item set mining. The Experiments result shows the potential of the proposed approach. Performance is significantly optimized for large scale data mining in MapReduce technique. The author believes that it has a valuable contribution in the high performance computing of Big Data.

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Performance Analysis of Schedulers to Handle Multi Jobs in Hadoop Cluster

By Guru Prasad M S Nagesh H R Swathi Prabhu

DOI:, Pub. Date: 8 Dec. 2015

MapReduce is programming model to process the large set of data. Apache Hadoop an implementation of MapReduce has been developed to process the Big Data. Hadoop Cluster sharing introduces few challenges such as scheduling the jobs, processing data locality, efficient resource usage, fair usage of resources, fault tolerance. Accordingly, we focused on a job scheduling system in Hadoop in order to achieve efficiency. Schedulers are responsible for doing task assignment. When a user submits a job, it will move to a job queue. From the job queue, the job will be divided into tasks and distributed to different nodes. By the proper assignment of tasks, job completion time will reduce. This can ensure better performance of the jobs. By default, Hadoop uses the FIFO scheduler. In our experiment, we are discussing and comparing FIFO scheduler with Fair scheduler and Capacity scheduler job execution time.

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