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Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

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If you are ready to dive into the MapReduce framework for processing large datasets, this practical book takes you step by step through the algorithms and tools you need to build distributed MapReduce applications with Apache Hadoop or Apache Spark. Each chapter provides a recipe for solving a massive computational problem, such as building a recommendation system. You’ll learn how to implement the appropriate MapReduce solution with code that you can use in your projects.
Dr. Mahmoud Parsian covers basic design patterns, optimization techniques, and data mining and machine learning solutions for problems in bioinformatics, genomics, statistics, and social network analysis. This book also includes an overview of MapReduce, Hadoop, and Spark.

Topics include:

  • Market basket analysis for a large set of transactions
  • Data mining algorithms (K-means, KNN, and Naive Bayes)
  • Using huge genomic data to sequence DNA and RNA
  • Naive Bayes theorem and Markov chains for data and market prediction
  • Recommendation algorithms and pairwise document similarity
  • Linear regression, Cox regression, and Pearson correlation
  • Allelic frequency and mining DNA
  • Social network analysis (recommendation systems, counting triangles, sentiment analysis)

About the Author

Mahmoud Parsian, Ph.D. in Computer Science, is a practicingsoftware professional with 30 years of experience as a developer,designer, architect, and author. For the past 15 years, he hasbeen involved in Java server-side, databases, MapReduce, anddistributed computing. Dr. Parsian currently leads Illumina'sBig Data team, which is focused on large-scale genome analyticsand distributed computing. He leads and develops scalableregression algorithms; DNA sequencing and RNA sequencing pipelinesusing Java, MapReduce, Hadoop, HBase, and Spark; and open sourcetools. He is also the author of JDBC Recipes and JDBC Metadata (bothfrom Apress).


Mahmoud Parsian
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Table of Contents (9789352132034_toc.pdf, 199 Kb) [Download]

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