MapReduce algorithms for big data analysis

Author:

Shim Kyuseok1

Affiliation:

1. Seoul National University

Abstract

There is a growing trend of applications that should handle big data. However, analyzing big data is a very challenging problem today. For such applications, the MapReduce framework has recently attracted a lot of attention. Google's MapReduce or its open-source equivalent Hadoop is a powerful tool for building such applications. In this tutorial, we will introduce the MapReduce framework based on Hadoop, discuss how to design efficient MapReduce algorithms and present the state-of-the-art in MapReduce algorithms for data mining, machine learning and similarity joins. The intended audience of this tutorial is professionals who plan to design and develop MapReduce algorithms and researchers who should be aware of the state-of-the-art in MapReduce algorithms available today for big data analysis.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

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1. Exponential Chimp Optimization Algorithm based Deep Neuro‐Fuzzy Network with MapReduce framework for fake news detection in big data analytics;International Journal of Adaptive Control and Signal Processing;2023-06-24

2. Big Data Mining and Analytics With MapReduce;Encyclopedia of Data Science and Machine Learning;2022-10-14

3. Accelerating Join of Distributed Datasets by a Given Criterion;2022 Moscow Workshop on Electronic and Networking Technologies (MWENT);2022-06-09

4. Big Data Analytics and Mining for Knowledge Discovery;Research Anthology on Big Data Analytics, Architectures, and Applications;2022

5. MapReduce-Based Dynamic Partition Join with Shannon Entropy for Data Skewness;Scientific Programming;2021-11-24

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