Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Hegde, A.Subject: search.filters.subject.Big Data

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    Effective Resource Utilization in Hadoop Using Ganglia
    (Institute of Electrical and Electronics Engineers Inc., 2024) Srungarapati, B.; Pamarthi, M.; Vakada, V.; Hegde, A.; Bhowmik, B.
    The exponential growth of big data has led to the widespread adoption of Hadoop clusters for storing and processing large volumes of data. Efficient management of resources within these clusters is crucial for achieving optimal performance and cost efficiency. This research paper explores the use of Hadoop and Ganglia for monitoring and optimizing resource utilization in Hadoop clusters. The study demonstrates that leveraging Hadoop and Ganglia is an effective strategy for improving cluster performance and resource efficiency. Results show significant enhancements in cluster performance and resource utilization, highlighting the importance of proactive resource management in Hadoop environments. © 2024 IEEE.
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    Optimizing Feature Selection in Big Data: A Hybrid Spark and Fuzzy Approach
    (Institute of Electrical and Electronics Engineers Inc., 2024) Hada, A.S.; Sahoo, G.S.; Vamsi, C.K.; Hegde, A.; Bhowmik, B.
    The exponential growth of big data presents both immense opportunities and significant challenges. While vast datasets hold the key to unlocking groundbreaking insights, efficiently extracting value requires sophisticated feature selection techniques. Traditional methods often struggle with the sheer volume and complexity of big data. This paper addresses this challenge by proposing a novel hybrid feature selection algorithm by leveraging Apache PySpark's distributed computing power. Combining a robust feature selection technique with a novel weighting scheme, our method outperforms existing hypercuboid and fuzzy Rough Set methods. The hybrid approach achieves superior accuracy of 72.1% with a reduced feature set, demonstrating its effectiveness in identifying salient features for big data analysis. © 2024 IEEE.