Data Algorithms: Recipes for Scaling Up with Hadoop and by Mahmoud Parsian

By Mahmoud Parsian

When you are able to dive into the MapReduce framework for processing huge datasets, this functional publication takes you step-by-step in the course of the algorithms and instruments you want to construct allotted MapReduce purposes with Apache Hadoop or Apache Spark. each one bankruptcy offers a recipe for fixing a big computational challenge, similar to construction a suggestion process. You'll the right way to enforce the right MapReduce answer with code so that you can use on your projects.

Dr. Mahmoud Parsian covers easy layout styles, optimization concepts, and information mining and computer studying options for difficulties in bioinformatics, genomics, information, and social community research. This ebook additionally comprises an summary of MapReduce, Hadoop, and Spark.

Topics include:
• industry basket research for a wide set of transactions
• information mining algorithms (K-means, KNN, and Naive Bayes)
• utilizing large genomic facts to series DNA and RNA
• Naive Bayes theorem and Markov chains for facts and industry prediction
• advice algorithms and pairwise rfile similarity
• Linear regression, Cox regression, and Pearson correlation
• Allelic frequency and mining DNA
• Social community research (recommendation structures, counting triangles, sentiment research)

Show description

Read or Download Data Algorithms: Recipes for Scaling Up with Hadoop and Spark PDF

Best algorithms books

Methods in Algorithmic Analysis

Explores the effect of the research of Algorithms on Many components inside of and past laptop Science
A versatile, interactive educating layout more suitable by way of a wide number of examples and exercises

Developed from the author’s personal graduate-level direction, tools in Algorithmic research offers a number of theories, recommendations, and strategies used for interpreting algorithms. It exposes scholars to mathematical strategies and strategies which are functional and appropriate to theoretical elements of laptop science.

After introducing uncomplicated mathematical and combinatorial equipment, the textual content makes a speciality of a variety of features of chance, together with finite units, random variables, distributions, Bayes’ theorem, and Chebyshev inequality. It explores the function of recurrences in computing device technology, numerical research, engineering, and discrete arithmetic purposes. the writer then describes the robust software of producing features, that's proven in enumeration difficulties, equivalent to probabilistic algorithms, compositions and walls of integers, and shuffling. He additionally discusses the symbolic procedure, the main of inclusion and exclusion, and its purposes. The publication is going directly to exhibit how strings should be manipulated and counted, how the finite country computer and Markov chains may also help remedy probabilistic and combinatorial difficulties, how you can derive asymptotic effects, and the way convergence and singularities play major roles in deducing asymptotic details from producing capabilities. the ultimate bankruptcy offers the definitions and homes of the mathematical infrastructure had to accommodate producing functions.

Accompanied by way of greater than 1,000 examples and routines, this complete, classroom-tested textual content develops students’ realizing of the mathematical method at the back of the research of algorithms. It emphasizes the $64000 relation among non-stop (classical) arithmetic and discrete arithmetic, that's the foundation of desktop technology.

The Art of Computer Programming, Volume 1, Fascicle 1: MMIX -- A RISC Computer for the New Millennium

Ultimately, after a wait of greater than thirty-five years, the 1st a part of quantity four is eventually prepared for ebook. try out the boxed set that brings jointly Volumes 1 - 4A in a single dependent case, and provides the shopper a $50 off the cost of paying for the 4 volumes separately.   The paintings of laptop Programming, Volumes 1-4A Boxed Set, 3/e  ISBN: 0321751043    artwork of desktop Programming, quantity 1, Fascicle 1, The: MMIX -- A RISC desktop for the recent Millennium   This multivolume paintings at the research of algorithms has lengthy been well-known because the definitive description of classical computing device technological know-how.

Knowledge Acquisition: Approaches, Algorithms and Applications: Pacific Rim Knowledge Acquisition Workshop, PKAW 2008, Hanoi, Vietnam, December 15-16, 2008, Revised Selected Papers

This publication constitutes the completely refereed post-workshop complaints of the 2008 Pacific Rim wisdom Acquisition Workshop, PKAW 2008, held in Hanoi, Vietnam, in December 2008 as a part of tenth Pacific Rim overseas convention on synthetic Intelligence, PRICAI 2008. The 20 revised papers provided have been rigorously reviewed and chosen from fifty seven submissions and went via rounds of reviewing and development.

Extra resources for Data Algorithms: Recipes for Scaling Up with Hadoop and Spark

Sample text

Among the most traditional rules, we find the rule SPT which enables us to compute an optimal active schedule for the 1\\C problem. Rule SPT: {Shortest Processing Time first) sequences the jobs in increasing order of their processing time. The converse rule is the rule LPT {Longest Processing Time first). The 1\\C problem is solved optimally with the rule WSPT. Rule WSPT: (Weighted Shortest Processing Time first) sequences the jobs in increasing order of their ratio pt/wi. When we consider the due dates and the minimisation of criterion Lmax ?

An optimal algorithm for the l\prec\fmax problem Moore's algorithm for the l|di|f/ problem Consider the problem where n jobs have to be scheduled on a single machine and each job Ji has a due date di. No preemption is allowed. The objective is to minimise the number of late jobs, denoted by U. [Moore, 1968] provides an optimal polynomial time algorithm to solve this problem. It starts with the schedule obtained by the rule EDD. e. all jobs scheduled before are early or on time. Moore's algorithm puts Jk on time by removing the preceding job with the greatest processing time.

1 it is possible to derive straight complexity classes for optimisation problems. This is achieved by using a generalisation of polynomial reductions: the polynomial Turing reductions. 2 Complexity of problems 39 1. e. calculates for an instance / a solution of Sj if it exists. 2. A uses a procedure S which solves the problem 0\ 3. If 5 solves the problem O' in polynomial time, then A solves O in polynomial time. The complexity of the procedure S is not important in defining the polynomial Turing reduction.

Download PDF sample

Rated 4.01 of 5 – based on 31 votes