S. Saravanan joined Amrita School of Engineering, Bengaluru Campus in the year 2012. He is working as an Assistant Professor in the Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru. He has 12 years of teaching experience and is currently pursuing his Ph.D. in Amrita Vishwa Vidyapeetham, Bengaluru campus. He is a life time member of ISTE.


2011 M.E(CSE) Sathyabama University, Chennai
2005 B.E(CSE) Anna University, Chennai


Professional Activities

  • Life time member of ISTE


Publication Type: Conference Paper

Year of Publication Publication Type Title


Conference Paper

S. Saravanan and Athri, P., “HMSPKmerCounter: Hadoop based Parallel, Scalable, Distributed Kmer Counter for Large Datasets”, in International Conference on Bioinformatics and Systems Biology – 2018, IIIT, Allahabad, 2018.


Conference Paper

S. Saravanan, E., K. K., Balaji, A., and S., A., “Data Classification Using Machine Learning Approach”, in 3rd International Symposium on Intelligent Systems Technologies and Applications (ISTA'17), Manipal University, Karnataka , 2017.


Conference Paper

S. Saravanan, “Design of large-scale Content-based Recommender System Using Hadoop MapReduce Framework”, in 2015 Eighth International Conference on Contemporary Computing (IC3), 2015.[Abstract]

Nowadays, providing relevant product recommendations to customers plays an important role in retaining customers and improving their shopping experience. Recommender systems can be applied to industries such as an e-commerce, music, online radio, television, hospitality, finance and many more. It is proved over the years that a simple algorithm with a lot of data can always provide better results than a complex algorithm with an inadequate amount of data. To provide better product recommendations, retail businesses have to analyze huge amount of data. As the recommendation system has to analyze huge amount of data to provide better recommendations, it is considered as a data intensive application. Hadoop distributed cluster platform is developed by Apache Software Foundation to address the issues which are involved in designing data intensive applications. In this paper, the improved MapReduce based data preprocessing and Content based recommendation algorithms are proposed and implemented using hadoop framework. Also, graphical user interfaces are developed to interact with the recommender system. Experimental results on Amazon product co-purchasing network metadata show that Hadoop distributed cluster environment is an efficient and scalable platform for implementing large scale recommender system.

More »»

Publication Type: Journal Article

Year of Publication Publication Type Title


Journal Article

S. Saravanan and B. Uma Maheswari, “Analyzing Large Web Log Files in a Hadoop Distributed Cluster Environment”, International Journal of Computer Technology and Applications (IJCTA), vol. 5, no. 5, 2014.[Abstract]

Analysing web log files has become an important task for E-Commerce companies to predict their customer behaviour and to improve their business. Each click in an E-commerce web page creates 100 bytes of data. Large E-Commerce websites like flipkart.com, amazon.in and ebay.in are visited millions of customers simultaneously. As a result, these customers generate petabytes of data in their web log files. As the web log file size is huge we require parallel processing and reliable data storage system for processing the web log files. Both the requirements are provided by Hadoop framework. Hadoop provides Hadoop Distributed File System (HDFS) and MapReduce rogramming model for processing huge dataset efficiently and effectively. In this paper, NASA web log file is analysed and the total number of hits received by each web page in a website, the total number of hits received by a web site in each hour using Hadoop framework is calculated and it is shown that Hadoop framework takes less response time to produce accurate results. Keywords - Hadoop, MapReduce, Log Files, Parallel Processing, Hadoop Distributed File System, ECommerce More »»