Data Algorithms

Recipes for Scaling Up with Hadoop and Spark

Author: Mahmoud Parsian

Publisher: "O'Reilly Media, Inc."

ISBN: 1491906154

Category: Computers

Page: 778

View: 7707

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)

Data Algorithms

Recipes for Scaling Up with Hadoop and Spark

Author: Mahmoud Parsian

Publisher: "O'Reilly Media, Inc."

ISBN: 1491906138

Category: Computers

Page: 778

View: 658

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)

Data Algorithms

Recipes for Scaling Up with Hadoop and Spark

Author: Mahmoud Parsian

Publisher: O'Reilly Media

ISBN: 9781491906187

Category: Computers

Page: 778

View: 1801

Learn the algorithms and tools you need to build MapReduce applications with Hadoop and Spark for processing gigabyte, terabyte, or petabyte-sized datasets on clusters of commodity hardware. With this practical book, author Mahmoud Parsian, head of the big data team at Illumina, takes you step-by-stepthrough the design of machine-learning algorithms, such as Naive Bayes and Markov Chain, and shows you how apply them to clinical and biological datasets, using MapReduce design patterns. Apply MapReduce algorithms to clinical and biological data, such as DNA-Seq and RNA-Seq Use the most relevant regression/analytical algorithms used for different biological data types Apply t-test, joins, top-10, and correlation algorithms using MapReduce/Hadoop and Spark

We Are Data

Algorithms and the Making of Our Digital Selves

Author: John Cheney-Lippold

Publisher: NYU Press

ISBN: 1479808709

Category: Business & Economics

Page: 320

View: 8013

What identity means in an algorithmic age: how it works, how our lives are controlled by it, and how we can resist it Algorithms are everywhere, organizing the near limitless data that exists in our world. Derived from our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world, but also determine who we are and who we can be, both on and offline. Algorithms create and recreate us, using our data to assign and reassign our gender, race, sexuality, and citizenship status. They can recognize us as celebrities or mark us as terrorists. In this era of ubiquitous surveillance, contemporary data collection entails more than gathering information about us. Entities like Google, Facebook, and the NSA also decide what that information means, constructing our worlds and the identities we inhabit in the process. We have little control over who we algorithmically are. Our identities are made useful not for us—but for someone else. Through a series of entertaining and engaging examples, John Cheney-Lippold draws on the social constructions of identity to advance a new understanding of our algorithmic identities. We Are Data will educate and inspire readers who want to wrest back some freedom in our increasingly surveilled and algorithmically-constructed world.

Big Data

Algorithms, Analytics, and Applications

Author: Kuan-Ching Li,Hai Jiang,Laurence T. Yang,Alfredo Cuzzocrea

Publisher: Chapman and Hall/CRC

ISBN: 9781482240559

Category: Computers

Page: 498

View: 1683

As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management—considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing—addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms—explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy—focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications—illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

R Data Structures and Algorithms

Author: Dr. PKS Prakash,Achyutuni Sri Krishna Rao

Publisher: Packt Publishing Ltd

ISBN: 1786464160

Category: Computers

Page: 276

View: 5249

Increase speed and performance of your applications with efficient data structures and algorithms About This Book See how to use data structures such as arrays, stacks, trees, lists, and graphs through real-world examples Find out about important and advanced data structures such as searching and sorting algorithms Understand important concepts such as big-o notation, dynamic programming, and functional data structured Who This Book Is For This book is for R developers who want to use data structures efficiently. Basic knowledge of R is expected. What You Will Learn Understand the rationality behind data structures and algorithms Understand computation evaluation of a program featuring asymptotic and empirical algorithm analysis Get to know the fundamentals of arrays and linked-based data structures Analyze types of sorting algorithms Search algorithms along with hashing Understand linear and tree-based indexing Be able to implement a graph including topological sort, shortest path problem, and Prim's algorithm Understand dynamic programming (Knapsack) and randomized algorithms In Detail In this book, we cover not only classical data structures, but also functional data structures. We begin by answering the fundamental question: why data structures? We then move on to cover the relationship between data structures and algorithms, followed by an analysis and evaluation of algorithms. We introduce the fundamentals of data structures, such as lists, stacks, queues, and dictionaries, using real-world examples. We also cover topics such as indexing, sorting, and searching in depth. Later on, you will be exposed to advanced topics such as graph data structures, dynamic programming, and randomized algorithms. You will come to appreciate the intricacies of high performance and scalable programming using R. We also cover special R data structures such as vectors, data frames, and atomic vectors. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. We will also explore the application of binary search and will go in depth into sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. Style and approach This easy-to-read book with its fast-paced nature will improve the productivity of an R programmer and improve the performance of R applications. It is packed with real-world examples.

Data Structures and Algorithms Using C#

Author: Michael McMillan

Publisher: Cambridge University Press

ISBN: 0521670152

Category: Computers

Page: 355

View: 8685

Michael McMillan discusses the implementation of data structures and algorithms from the .NET framework. The comprehensive text includes basic data structures and algorithms plus advanced algorithms such as probabilistic algorithms and dynamics programming.

Data Structures and Network Algorithms

Author: Robert Endre Tarjan

Publisher: SIAM

ISBN: 9781611970265

Category: Algorithms

Page: 131

View: 5909

There has been an explosive growth in the field of combinatorial algorithms. These algorithms depend not only on results in combinatorics and especially in graph theory, but also on the development of new data structures and new techniques for analyzing algorithms. Four classical problems in network optimization are covered in detail, including a development of the data structures they use and an analysis of their running time. Data Structures and Network Algorithms attempts to provide the reader with both a practical understanding of the algorithms, described to facilitate their easy implementation, and an appreciation of the depth and beauty of the field of graph algorithms.

Python Data Structures and Algorithms

Author: Benjamin Baka

Publisher: Packt Publishing Ltd

ISBN: 1786465337

Category: Computers

Page: 310

View: 8418

Implement classic and functional data structures and algorithms using Python About This Book A step by step guide, which will provide you with a thorough discussion on the analysis and design of fundamental Python data structures. Get a better understanding of advanced Python concepts such as big-o notation, dynamic programming, and functional data structures. Explore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner. Who This Book Is For The book will appeal to Python developers. A basic knowledge of Python is expected. What You Will Learn Gain a solid understanding of Python data structures. Build sophisticated data applications. Understand the common programming patterns and algorithms used in Python data science. Write efficient robust code. In Detail Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications. Style and Approach The easy-to-read book with its fast-paced nature will improve the productivity of Python programmers and improve the performance of Python applications.

An Introduction to Data Structures and Algorithms

Author: J.A. Storer

Publisher: Springer Science & Business Media

ISBN: 9780817642532

Category: Computers

Page: 599

View: 7820

Data structures and algorithms are presented at the college level in a way that is unique in content and presentation from current available texts. A highly accessible format presents algorithms with one page displays that will appeal to both students and teachers of computer science. The thirteen chapters systematically and comprehensively cover Models of Computation, Lists, Induction and Recursion, Trees, Algorithm Design, Hashing, Heaps, Balanced Trees, Sets Over a Small Universe, Discrete Fourier Transform, Strings, Graphs, Parallel Computation.

Algorithms and Data Structures for External Memory

Author: Jeffrey Scott Vitter

Publisher: Now Publishers Inc

ISBN: 1601981066

Category: Computers

Page: 174

View: 4529

Algorithms and Data Structures for External Memory describes several useful paradigms for the design and implementation of efficient external memory (EM) algorithms and data structures. The problem domains considered include sorting, permuting, FFT, scientific computing, computational geometry, graphs, databases, geographic information systems, and text and string processing.

Introduction to Algorithms

Author: Thomas H. Cormen

Publisher: MIT Press

ISBN: 0262533057

Category: Computers

Page: 1292

View: 8504

A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.

Data Structures And Algorithms

Author: N.A.Deshpande S.S.Sane

Publisher: Technical Publications

ISBN: 9788184310849

Category:

Page: 476

View: 9471

Fundamental ConceptsData Structures : Data, Data objects, Data types, Abstract Data Types (ADT) and Data structure, Concept of primitive and non primitive, Linear and non-linear, Static and dynamic, Persistent and ephemeral data structures,Introduction to algorithms : Definition and characteristics of an algorithm, Algorithm design.Tools : Flowcharts and pseudo code, Notations : Algorithm Header, Purpose, Conditions and return, Statements, Statement numbers, Variables, Comments, Statement constructs : Sequence, Selection, Loops and sub-algorithms.Program development : Analysis design, Coding, Testing and verification.Linear Data Structures using Sequential OrganizationConcept of sequential organization, Arrays as ADT, Storage representation of array (row major and column major). Representation of polynomials using arrays, Representation of sparse matrix, Addition, Transpose and fast transpose of sparse matrix, Time and space complexity analysis for simple and fast transpose for sparse matrix.Stacks Fundamentals, Stacks as ADT, Representation and implementation of stack using arrays, Applications of stack : Expression evaluation and conversion, Reversing a string, Parsing : Well-form parenthesis, Decimal to binary conversion, Representation of multiple stacks using single array.Recursion : Definition, Writing recursive functions, How recursion works ? Simulating recursion using stack.QueuesFundamentals, Queue as ADT, Representation and implementation of queue using arrays, Circular queue : Representation and implementation, Applications of queue : Josephus problem, Job scheduling, Queue simulation, Categorizing data, Doubly ended queue, Representation of multiple queues using single array, Priority queue.Searching and SortingSearching : Sequential, Binary and index sequential search.Sorting : General concepts : Sort order, Sort stability, Efficiency and passes, Bubble sort, Selection sort, Insertion sort, Shell, Radix, Quick and merge sort.Algorithm Analysis and StrategiesAlgorithm analysis : Time complexity : Real time and frequency count, Big 'O' and notations, Space complexity : Compile-time and run-time, Best, Average and worst cases. Algorithmic strategies : Use and the peculiar characteristics of each type, Divide and conquer (Quick sort/Tower of Hanoi), Backtracking (Eight queens problem), Greedy (Job scheduling with deadlines), Dynamic programming (Example triangulation problem) (Implementation not expected for all the examples).Programming Laboratory

Data Structures and Algorithms

Author: Shi Kuo Chang

Publisher: World Scientific

ISBN: 9812383484

Category: Computers

Page: 347

View: 1647

This is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. The thirteen chapters, written by an international group of experienced teachers, cover the fundamental concepts of algorithms and most of the important data structures as well as the concept of interface design. The book contains many examples and diagrams. Whenever appropriate, program codes are included to facilitate learning.This book is supported by an international group of authors who are experts on data structures and algorithms, through its website at http: //www.cs.pitt.edu/ jung/GrowingBook/, so that both teachers and students can benefit from their expertise

Leadership Strategies in the Age of Big Data, Algorithms, and Analytics

Author: Norton Paley

Publisher: CRC Press

ISBN: 1498764150

Category: Business & Economics

Page: 308

View: 5763

Harnessing the power of technology is one of the key measures of effective leadership. Leadership Strategies in the Age of Big Data, Algorithms, and Analytics will help leaders think and act like strategists to maintain a leading-edge competitive advantage. Written by a leading expert in the field, this book provides new insights on how to successfully transition companies by aligning an organization’s culture to accept the benefits of digital technology. The author emphasizes the importance of creating a team spirit with employees to embrace the digital age and develop strategic business plans that pinpoint new markets for growth, strengthen customer relationships, and develop competitive strategies. Understanding how to deal with inconsistencies when facts generated by data analytics disagree with your own experience, intuition, and knowledge of the competitive situation is key to successful leadership.

Data Structures & Their Algorithms

Author: Harry R. Lewis,Larry Denenberg

Publisher: Addison-Wesley

ISBN: N.A

Category: Computers

Page: 509

View: 5800

This book in an all-inclusive presentation introduces the datastructures (and their algorithms) that comprise the foundationof software engineering. Designed to show students at the sophomorelevel the connection between a programming approach and mathematicaltheory, the text focuses on practical techniques for studentsto master data structures and efficient algorithm implementation.Other topics pertinent to programmers also receive coverage. Chapter-endingproblems and references give students a helpful review and solidifychapter concepts. 067339736XB04062001

Data Structures and Algorithms in Python

Author: Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser

Publisher: Wiley

ISBN: 1118290275

Category: Computers

Page: 768

View: 9068

Based on the authors’ market leading data structures booksin Java and C++, this book offers a comprehensive, definitiveintroduction to data structures in Python by authoritative authors.Data Structures and Algorithms in Python is the firstauthoritative object-oriented book available for Python datastructures. Designed to provide a comprehensive introduction todata structures and algorithms, including their design, analysis,and implementation, the text will maintain the same generalstructure as Data Structures and Algorithms in Java andData Structures and Algorithms in C++. Begins by discussing Python’s conceptually simple syntax,which allows for a greater focus on concepts. Employs a consistent object-oriented viewpoint throughout thetext. Presents each data structure using ADTs and their respectiveimplementations and introduces important design patterns as a meansto organize those implementations into classes, methods, andobjects. Provides a thorough discussion on the analysis and design offundamental data structures. Includes many helpful Python code examples, with source codeprovided on the website. Uses illustrations to present data structures and algorithms,as well as their analysis, in a clear, visual manner. Provides hundreds of exercises that promote creativity, helpreaders learn how to think like programmers, and reinforceimportant concepts. Contains many Python-code and pseudo-code fragments, andhundreds of exercises, which are divided into roughly 40%reinforcement exercises, 40% creativity exercises, and 20%programming projects.

Machine Learning and Security

Protecting Systems with Data and Algorithms

Author: Clarence Chio,David Freeman

Publisher: "O'Reilly Media, Inc."

ISBN: 1491979879

Category: Computers

Page: 386

View: 8301

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself! With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions