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Author: Rajesh Bordawekar
Publisher: Springer Nature
ISBN: 3031017498
Category : Technology & Engineering
Languages : en
Pages : 118
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Book Description
This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.
Author: Rajesh Bordawekar
Publisher: Springer Nature
ISBN: 3031017498
Category : Technology & Engineering
Languages : en
Pages : 118
Get Book Here
Book Description
This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains such as deep learning, text analytics, and business intelligence (BI), we demonstrate how various software and hardware acceleration strategies are implemented in practice. This book is intended for both experienced professionals and students who are interested in understanding core algorithms behind analytics workloads. It is designed to serve as a guide for addressing various open problems in accelerating analytics workloads, e.g., new architectural features for supporting analytics workloads, impact on programming models and runtime systems, and designing analytics systems.
Author: Seyed-Mehdi-Reza Beheshti
Publisher: Springer
ISBN: 331925037X
Category : Computers
Languages : en
Pages : 194
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Book Description
This book starts with an introduction to process modeling and process paradigms, then explains how to query and analyze process models, and how to analyze the process execution data. In this way, readers receive a comprehensive overview of what is needed to identify, understand and improve business processes. The book chiefly focuses on concepts, techniques and methods. It covers a large body of knowledge on process analytics – including process data querying, analysis, matching and correlating process data and models – to help practitioners and researchers understand the underlying concepts, problems, methods, tools and techniques involved in modern process analytics. Following an introduction to basic business process and process analytics concepts, it describes the state of the art in this area before examining different analytics techniques in detail. In this regard, the book covers analytics over different levels of process abstractions, from process execution data and methods for linking and correlating process execution data, to inferring process models, querying process execution data and process models, and scalable process data analytics methods. In addition, it provides a review of commercial process analytics tools and their practical applications. The book is intended for a broad readership interested in business process management and process analytics. It provides researchers with an introduction to these fields by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in business process management to graduate courses in business process analytics. Lastly, it offers professionals a reference guide to the state of the art in commercial tools and techniques, complemented by many real-world use case scenarios.
Author: EMC Education Services
Publisher: John Wiley & Sons
ISBN: 1118876229
Category : Computers
Languages : en
Pages : 432
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Book Description
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
Author: Byron Ellis
Publisher: John Wiley & Sons
ISBN: 1118838025
Category : Computers
Languages : en
Pages : 432
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Book Description
Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's "recipe" layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website.
Author: Lev Manovich
Publisher: MIT Press
ISBN: 0262360632
Category : Computers
Languages : en
Pages : 332
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Book Description
A book at the intersection of data science and media studies, presenting concepts and methods for computational analysis of cultural data. How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of digital culture--the billions of photographs shared on social media every day, the hundreds of millions of songs created by twenty million musicians on Soundcloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data. Drawing on more than a decade of research and projects from his own lab, Manovich offers a gentle, nontechnical introduction to the core ideas of data analytics and discusses the ways that our society uses data and algorithms.
Author: Rokia Missaoui
Publisher:
ISBN: 9783030932794
Category :
Languages : en
Pages : 0
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Book Description
FCA is an important formalism that is associated with a variety of research areas such as lattice theory, knowledge representation, data mining, machine learning, and semantic Web. It is successfully exploited in an increasing number of application domains such as software engineering, information retrieval, social network analysis, and bioinformatics. Its mathematical power comes from its concept lattice formalization in which each element in the lattice captures a formal concept while the whole structure represents a conceptual hierarchy that offers browsing, clustering and association rule mining. Complex data analytics refers to advanced methods and tools for mining and analyzing data with complex structures such as XML/Json data, text and image data, multidimensional data, graphs, sequences and streaming data. It also covers visualization mechanisms used to highlight the discovered knowledge. This edited book examines a set of important and relevant research directions in complex data management, and updates the contribution of the FCA community in analyzing complex and large data such as knowledge graphs and interlinked contexts. For example, Formal Concept Analysis and some of its extensions are exploited, revisited and coupled with recent processing parallel and distributed paradigms to maximize the benefits in analyzing large data.
Author: Judah Phillips
Publisher: FT Press
ISBN: 0134177967
Category : Computers
Languages : en
Pages : 523
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Book Description
Ecommerce analytics encompasses specific, powerful techniques for collecting, measuring, analyzing, dashboarding, optimizing, personalizing, and automating data related to online sales and customers. If you participate in the $220 billion ecommerce space, you need expert advice on applying these techniques in your unique environment. Ecommerce Analytics is the only book to deliver the focused, coherent, and practical guidance you’re looking for. Authored by leading consultant and analytics team leader Judah Phillips, it shows how to leverage your massive, complex data resources to improve efficiency, grow revenue, reduce cost, and above all, boost profitability. This landmark guide focuses on using analytics to solve critical problems ecommerce organizations face, from improving brand awareness and favorability through generating demand; shaping digital behavior to accelerating conversion, improving experience to nurturing and re-engaging customers. Phillips shows how to: Implement and unify ecommerce analytics related to product, transactions, customers, merchandising, and marketing More effectively measure performance associated with customer acquisition, conversion, outcomes, and business impact Use analytics to identify the tactics that will create the most value, and execute them more effectively Think about and analyze the behavior of customers, prospects, and leads in ecommerce experiences Optimize paid/owned/earned marketing channels, product mix, merchandising, pricing/promotions/sales, browsing/shopping/purchasing, and other ecommerce functions Understand and model attribution Structure and socialize ecommerce teams for success Evaluate the potential impact of technology choices and platforms Understand the implications of ecommerce analytics on customer privacy, life, and society Preview the future of ecommerce analytics over the next 20 years
Author: Jody Hoffer Gittell
Publisher: Routledge
ISBN: 1000391639
Category : Business & Economics
Languages : en
Pages : 196
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Book Description
This guidebook goes beyond people analytics to provide a research-based, practice-tested methodology for doing relational analytics, based on the science of relational coordination. We are witnessing a revolution in people analytics, where data are used to identify and leverage human talent to drive performance outcomes. Today’s workplace is interdependent, however, and individuals drive performance through networks that span department, organization and sector boundaries. This book shares the relational coordination framework, with a validated scalable analytic tool that has been used successfully across dozens of countries and industries to understand, measure and influence networks of relationships in and across organizations, and which can be applied at any level in the private and public sectors worldwide. Graduate students and practitioners in human resource management, health policy and management, organizational behavior, engineering and network analysis will appreciate the methodology and hands-on guidance this book provides, with its focus on identifying, analyzing and building networks of productive interdependence. Online resources include data appendices and statistical commands that can be used to conduct all these analyses in readers’ own organizations.
Author: Brent Dykes
Publisher: Adobe Press
ISBN: 0132837943
Category : Business & Economics
Languages : en
Pages : 476
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Book Description
Companies need more than just web analysts and data-savvy marketers to be successful–they need action heroes! While most of us never battle evil scientists or defuse nuclear warheads, successful web analysts benefit from the same attributes that fictional action heroes embody. As a web analyst, your main goal is to improve your organization’s online performance. You can become an “action hero” by translating analysis insights into action that generates significant returns for your company. How you approach analysis is critical to your overall success. In this book, web analytics expert Brent Dykes addresses the unique challenges facing analysts and online marketers working within small and large companies, teaching you how to move beyond reporting and toward analysis to drive action and change. Taking a principle-based rather than a tool-specific approach, Brent introduces you to the Action Hero Framework that breaks down the analysis process into three key stages: Prioritize (what to analyze), Analyze (how to analyze), and Mobilize (how to drive action). And he reinforces these topics with real-world examples and practical tips from seasoned analysts at leading companies. Defines the type of environment in which action heroes thrive–not just survive–as well as how to defeat the villains of web analytics that stand in the way Arms web professionals with a strategic framework for executing online analysis, as well as an arsenal of analysis techniques Reveals how companies need to be both data-driven and action-agile to drive business value from web analytics For more action hero resources and information, check out the book’s companion site at www.Analyticshero.com. "The ideas in this book will take you days (or even weeks) to work your way through, and they fly in the face of the emotional approach to marketing. The question is: would you rather have your competition lead the way with data and science when it comes to reaching your market, or are you going to go first? That's how it is with action heroes--no guts, no glory." - Seth Godin Author We Are All Weird "Don't let the jaunty, breezy style of this book throw you off. Brent successfully - and entertainingly - packs years of experience into these pages along with case studies and insightful help on getting the most out of web analytics, adding value to your company and boosting your career trajectory." - Jim Sterne Founder of eMetrics Marketing Optimization Summit, author of "Social Media Metrics" and Chairman of the Digital Analytics Association
Author: Eric Siegel
Publisher: John Wiley & Sons
ISBN: 1119153654
Category : Business & Economics
Languages : en
Pages : 368
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Book Description
"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a
