Nandita Yambem

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Papers by Nandita Yambem

A Technical Insight into Clustering Algorithms & Applications

Today is the age of data which is being produced at a tremendous rate. Cluster analysis divides d... more Today is the age of data which is being produced at a tremendous rate. Cluster analysis divides data into groups for the purposes of summarization or improved understanding to assist in decision making. Researchers in data mining and machine learning faces challenges due to many factors which can make use of this large data to extract useful knowledge. As a part of my ongoing Research work I have undertaken an exhaustive technical survey to cover the key aspects of Clustering like-the need, clusters types and various clustering algorithms, Clustering applications etc

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A Comprehensive Survey on Big Data Issues and Alternative Approaches to Hadoop MapReduce

as we all are aware that [Big] Data is created by human beings and gadgets every second, is ever ... more as we all are aware that [Big] Data is created by human beings and gadgets every second, is ever growing in phenomenal rate. These big Data needs to be stored and analyzed for reference purposes & to gain Intelligence or knowledge for futuristic decision making. We conducted exhaustive technological survey to cover the aspects like Cloud computing its advantages and disadvantages, Big Data & its issues, Tools & Techniques like – Hadoop, MapReduce, stages, Open source alternatives to Hadoop etc.. Keywords— BigData, Cloud Computing, Hadoop, MapReduce, Open source.

Big Data: Characteristics, Issues and Clustering Techniques

NCICCNDA, 2018

Now a days as Big Data is generated from sensor networks, IOTs, Social network and many collabora... more Now a days as Big Data is generated from sensor networks, IOTs, Social network and many collaborative networks, etc. continuously over a period of time line resulting in very large volumes of Data of various types at a phenomenal rate. Handling the characteristics of Big Data is a huge challenge. To handle this large data called big data a different approach or algorithm is required by organizations to handle all the challenges of Big data the Big data system requires a massive processing power and stable complex network configurations. Focuses should be on the analysis part of the big data classification by implementing different techniques in it. The various clustering techniques and the algorithms with the challenges they pose with Big data are discussed. To handle the dimensionality issues and approach the unsupervised machine learning, the use of clustering technique is the most common step in Big Data.

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Hybrid Meta Heuristics Optimization of Hadoop Parameters for Increased Performance Gains

Optimizing Hadoop parameter for speedup using Q-Learning Reinforcement Learning

2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT)

Hadoop is the most popular open source big data processing platform which is being used in many b... more Hadoop is the most popular open source big data processing platform which is being used in many big data analytics applications. The performance of Hadoop can be fine-tuned for application performance requirements by adjusting the value of the some of the configuration parameters. Various methods have been proposed in literature for fine tuning the configuration parameters of Hadoop. The relation between the Hadoop performance tuning parameters and speed up is dependent on the nature of the applications and environment dynamics. Tuning the parameters without consideration of these dynamics results in sub optimal configurations and lower performance.. Adaptive reinforcement learning using Q-Learning is proposed in this work to fine tune the configuration parameters with the objective of reducing the error between desired and achieved service level agreement (SLA).

AMPO: Algorithm for MapReduce Performance Optimization for enhancing big data analytics

2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 2017

The usage of cloud computing has lead to generation of petabytes of data just in a matter of seco... more The usage of cloud computing has lead to generation of petabytes of data just in a matter of second, which required a pivotal attention for analysis along with the storage. Although, storage issues in cloud has been solved to a large extent, but performing distributed analytical operation over the cloud is still a bigger challenge. The frequently used Hadoop MapReduce can perform distributed process modeling and inspite of its advantages, its pitfalls overshadow its potential advantageous features in terms of optimization. Hence, this paper presents a technique called as Algorithm for MapReduce Performance Optimization or AMPO for enhancing the performance of big data analytics. An analytical research methodology was adopted considering a case study of larger size traffic data to find that AMPO offers faster response time and lowered cost of resources as compared to the conventional MapReduce Programs without eliminating its major mapping and reducer operations.

A Survey on Data Mining Algorithms on Apache Hadoop Platform

Apache hadoop is a major innovation in the IT market place last decade. From humble beginnings Ap... more Apache hadoop is a major innovation in the IT market place last decade. From humble beginnings Apache Hadoop has become a world-wide adoption in data centers. It brings parallel processing in hands of average programmer. As more data centers supports hadoop platform, it becomes imperative to migrate existing data mining algorithms onto hadoop platform for increased parallel processing efficiency. With the introduction of big data analytics, this trend of migration of the existing data mining algorithms to hadoop platform has become rampant. In this survey paper, we explore the current migration activities and challenges in migration. This paper will guide the readers to propose solutions for the current challenges in the migration.

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National Conference on Image Processing, Computing, Communication, Networking and Data Analytics