MapReduce
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A Dynamic Text Compression Model for Big Data Applications Using Hadoop
Abstract: In today’s data-driven era, efficiently handling vast amounts of information has become increasingly important. Data compression plays a vital role in this regard — it is essentially a method of encoding information in such a way that significantly reduces the number of bits required to store or transmit a file. By shrinking data to its most compact form, compression techniques help save storage space, reduce bandwidth consumption, and improve the …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 2, 2026 Read article
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Optimizing Data Processing Efficiency in Big Data: Advanced MapReduce Algorithm Innovations
Abstract: The exponential growth of big data in recent years has created an urgent need for innovative and efficient processing frameworks capable of managing and analyzing massive and complex datasets. Among these, MapReduce has gained prominence as a powerful tool for distributed data processing due to its simplicity and scalability. However, traditional MapReduce frameworks often encounter significant limitations in terms of efficiency, scalability, and resource optimization, particularly when handling large-scale and …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 1, 2025 · pp. 1–7 Read article
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Decoding Big Data: A Practical Comparison Between Hadoop and Spark
Abstract: This paper conducts a comprehensive comparison of Apache Hadoop and Apache Spark, two essential frameworks in the big data era. The rapid expansion of data possesses challenges in terms of volume, variety, and velocity, which necessitate advanced processing solutions. Hadoop, utilizing its MapReduce paradigm, provides scalable and fault-tolerant storage, whereas Spark, built upon Hadoop, introduces in-memory processing to increase speed and flexibility. This study includes a detailed examination of their …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 3, 2024 · pp. 15–23 Read article
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Unravelling the Power of Avro and Hadoop: Revolutionising Big Data Processing and Serialization
Abstract: As the technology is in progress, the accumulation of data is also increasing. As a result of which a lot of organisations are constantly seeking new yet innovative solutions to process and analyse this huge accumulation of data. Well Hadoop proved a gamе-changеr in thе rеalm of big data procеssing whereas Avro proved a solution provider to data Serialization. In this review work, wе will dеlvе into thе world of …
Published in International Journal of Data Structure Studies · Vol. 2, Issue 1, 2024 · pp. 8–13 Read article
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Big Data: A Survey Paper on MapReduce Big Data
Abstract: AbstractHadoop MapReduce is processed for analysis large volume of data through multiple nodes in parallel. However, MapReduce has two function Map and Reduce, large data is stored through HDFS. Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over This survey intends to assist the …
Published in Journal of Open Source Developments · Vol. 7, Issue 3, 2020 · pp. 18–27 Read article
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Mapreduce Based Framework for Social Media Post Visualization
Abstract: Handling huge amount of data scalable is a matter of concern for a long time. We all know that social media generates huge amounts of data the explosive growth of social media is one of the reasons that 90% of all the data in the world has been generated in the last two years alone. When we use services like Facebook and Twitter, we agree to let those to store …
Published in Journal of Advanced Database Management & Systems · Vol. 2, Issue 2, 2015 · pp. 49–52 Read article