Handbook on Data Centers

Handbook on Data Centers

Handbook on Data Centers

Handbook on Data Centers by Samee Ullah Khan and Albert Y. Zomaya
This handbook offers a comprehensive review of the state-of-the-art research achievements in the field of data centers. Contributions from international, leading researchers and scholars offer topics in cloud computing, virtualization in data centers, energy efficient data centers, and next generation data center architecture.  It also comprises current research trends in emerging areas, such as data security, data protection management, and network resource management in data centers.
Specific attention is devoted to industry needs associated with the challenges faced by data centers, such as various power, cooling, floor space, and associated environmental health and safety issues, while still working to support growth without disrupting quality of service. The contributions cut across various IT data technology domains as a single source to discuss the interdependencies that need to be supported to enable a virtualized, next-generation, energy efficient, economical, and environmentally friendly data center.
This book appeals to a broad spectrum of readers, including server, storage, networking, database, and applications analysts, administrators, and architects. It is intended for those seeking to gain a stronger grasp on data center networks: the fundamental protocol used by the applications and the network, the typical network technologies, and their design aspects. The Handbook of Data Centers is a leading reference on design and implementation for planning, implementing, and operating data center networks.Ebook format: PDF
Ebook page: 1309
File size: 39.97 MB

$120.00

Encyclopedia of the Elements: Technical Data - History - Processing - Applications

Encyclopedia of the Elements: Technical Data - History - Processing - Applications

Encyclopedia of the Elements: Technical Data - History - Processing - Applications

Encyclopedia of the Elements: Technical Data - History - Processing - Applications by Per Enghag
Famous for its history of numerous element discoverers, Sweden is the origin of this comprehensive encyclopedia of the elements. It provides both an important database for professionals as well as detailed reading ranging from historical facts, discoverers' portraits, colour plates of mineral types, natural occurrences, and industrial figures to winning and refining processes, biological roles and applications in modern chemistry, engineering and industry. Elemental data is presented in fact tables which include numerous physical and thermodynamic properties, isotope lists, radiation absorption characteristics, NMR parameters, and others. Further pertinent data is supplied in additional tables throughout the text. Published in Swedish in three volumes from 1998 to 2000, the contents have been revised and expanded by the author for this English edition.
Ebook format: PDF
Ebook page: 1311
File size: 9.94 MB

$165.00

Frequent Pattern Mining

Frequent Pattern Mining

Frequent Pattern Mining

Frequent Pattern Mining by Charu C. Aggarwal and Jiawei Han
This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.
Ebook format: PDF
Ebook page: 480
File size: 11.11 MB

$55.00

Advances in Databases and Information Systems

Advances in Databases and Information Systems

Advances in Databases and Information Systems

Advances in Databases and Information Systems (Advances in Intelligent Systems and Computing) by Tadeusz Morzy and Theo Härder

This volume is the second one of the 16th East-European Conference on Advances in Databases and Information Systems (ADBIS 2012), held on September 18-21, 2012, in Poznań, Poland. The first one has been published in the LNCS series.
This volume includes 27 research contributions, selected out of 90. The contributions cover a wide spectrum of topics in the database and information systems field, including: database foundation and theory, data modeling and database design, business process modeling, query optimization in relational and object databases, materialized view selection algorithms, index data structures, distributed systems, system and data integration, semi-structured data and databases, semantic data management, information retrieval, data mining techniques, data stream processing, trust and reputation in the Internet, and social networks. Thus, the content of this volume covers the research areas from fundamentals of databases, through still hot topic research problems (e.g., data mining, XML data processing), to novel research areas (e.g., social networks, trust and reputation, and data stream processing). The editors of this volume believe that its content will inspire the researchers with new ideas for future development. It may also serve as an overview of the ongoing work in the field of databases and information systems.

Ebook format: PDF
Ebook page: 314
File size: 6.62 MB

$95.00

Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining

Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining

Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining

Automated Data Collection with R: A Practical Guide to Web Scraping and Text Mining by Simon Munzert and Christian Rubba

A hands on guide to web scraping and text mining for both beginners and experienced users of R

• Introduces fundamental concepts of the main architecture of the web and databases and covers HTTP, HTML, XML, JSON, SQL.
• Provides basic techniques to query web documents and data sets (XPath and regular expressions).
• An extensive set of exercises are presented to guide the reader through each technique.
• Explores both supervised and unsupervised techniques as well as advanced techniques such as data scraping and text management.
• Case studies are featured throughout along with examples for each technique presented.
• R code and solutions to exercises featured in the book are provided on a supporting website.

Ebook format: PDF
Ebook page: 477
File size: 4.56 MB

$40.00

Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition

Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition

Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition

Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition by Brady T. West, Kathleen B. Welch and Andrzej T Galecki
Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM.
New to the Second Edition

• A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models
• Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations
• Use of the lmer() function in the lme4 R package
• New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs
• Updated graphical procedures in the software packages
• Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options
• More practical recommendations on using the software for analysis
• A new R package (WWGbook) that contains all of the data sets used in the examples

Ideal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures.
Ebook format: PDF
Ebook page: 434
File size: 5.16 MB

$35.00

Bursting the Big Data Bubble: The Case for Intuition-Based Decision Making

Bursting the Big Data Bubble: The Case for Intuition-Based Decision Making

Bursting the Big Data Bubble: The Case for Intuition-Based Decision Making

Bursting the Big Data Bubble: The Case for Intuition-Based Decision Making by Jay Liebowitz
Bursting the Big Data Bubble: The Case for Intuition-Based Decision Making focuses on this intuition-based decision making. The book does not discount data-based decision making, especially for decisions that are important and complex. Instead, it emphasizes the importance of applying intuition, gut feel, spirituality, experiential learning, and insight as key factors in the executive decision-making process.
Explaining how intuition is a product of past experience, learning, and ambient factors, the text outlines methods that will help to enhance your data-driven decision-making process with intuition-based decision making. The first part of the book, the "Research Track", presents contributions from leading researchers worldwide on the topic of intuition-based decision making as applied to management.
In the second part of the book, the "Practice Track," global executives and senior managers in industry, government, universities, and not-for-profits present vignettes that illustrate how they have used their intuition in making key decisions.
The research part of the book helps to frame the problem and address leading research in intuition-based decision making. The second part then explains how to apply these intuition-based concepts and issues in your own decision-making process.
Ebook format: PDF
Ebook page: 344
File size: 3.29 MB

$30.00

Combinatorial Maps: Efficient Data Structures for Computer Graphics and Image Processing

Combinatorial Maps: Efficient Data Structures for Computer Graphics and Image Processing

Combinatorial Maps: Efficient Data Structures for Computer Graphics and Image Processing

Combinatorial Maps: Efficient Data Structures for Computer Graphics and Image Processing by Guillaume Damiand and Pascal Lienhardt
Combinatorial Maps: Efficient Data Structures for Computer Graphics and Image Processing gathers important ideas related to combinatorial maps and explains how the maps are applied in geometric modeling and image processing. It focuses on two subclasses of combinatorial maps: n-Gmaps and n-maps.
Suitable for researchers and graduate students in geometric modeling, computational and discrete geometry, computer graphics, and image processing and analysis, the book presents the data structures, operations, and algorithms that are useful in handling subdivided geometric objects. It shows how to study data structures for the explicit representation of subdivided geometric objects and describes operations for handling the structures. The book also illustrates results of the design of data structures and operations.
Ebook format: PDF
Ebook page: 402
File size: 117.04 MB

$50.00

Data Classification: Algorithms and Applications

Data Classification: Algorithms and Applications

Data Classification: Algorithms and Applications

Data Classification: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) by Charu C. Aggarwal

Comprehensive Coverage of the Entire Area of Classification
Research on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data.
This comprehensive book focuses on three primary aspects of data classification:

• Methods-The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks.
• Domains-The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm.
• Variations-The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

Ebook format: PDF
Ebook page: 704
File size: 6.81 MB

$40.00

RapidMiner: Data Mining Use Cases and Business Analytics Applications

RapidMiner: Data Mining Use Cases and Business Analytics Applications

RapidMiner: Data Mining Use Cases and Business Analytics Applications

RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) by Markus Hofmann and Ralf Klinkenberg 

Powerful, Flexible Tools for a Data-Driven WorldAs the data deluge continues in today’s world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems.
Learn from the Creators of the RapidMiner Software Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com.
Understand Each Stage of the Data Mining ProcessThe book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining.
Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.

Ebook format: PDF
Ebook page: 518
File size: 26.02 MB

$50.00

Register-based Statistics: Statistical Methods for Administrative Data

Register-based Statistics: Statistical Methods for Administrative Data

Register-based Statistics: Statistical Methods for Administrative Data

Register-based Statistics: Statistical Methods for Administrative Data (Wiley Series in Survey Methodology) by Anders Wallgren and Britt Wallgren

This book provides a comprehensive and up to date treatment of  theory and practical implementation in Register-based statistics. It begins by defining the area, before explaining how to structure such systems, as well as detailing alternative approaches. It explains how to create statistical registers, how to implement quality assurance, and the use of IT systems for register-based statistics. Further to this, clear details are given about the practicalities of implementing such statistical methods, such as protection of privacy and the coordination and coherence of such an undertaking.
This edition offers a full understanding of both the principles and practices of this increasingly popular area of statistics, and can be considered a first step to a more systematic way of working with register-statistical issues. This book addresses the growing global interest in the topic and employs a much broader, more international approach than the 1^st edition. New chapters explore different kinds of register-based surveys, such as preconditions for register-based statistics and comparing sample survey and administrative data. Furthermore, the authors present discussions on register-based census, national accounts and the transition towards a register-based system as well as presenting new chapters on quality assessment of administrative sources and production process quality.

Ebook format: PDF
Ebook page: 324
File size: 1.96 MB

$40.00

Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data

Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data

Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data

Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data by Byron Ellis

Construct a robust end-to-end solution for analyzing and visualizing streaming dataReal-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.

Ebook format: PDF
Ebook page: 435
File size: 3.58 MB

$15.00

Mastering Data-Intensive Collaboration and Decision Making: Research and practical applications in the Dicode project

Mastering Data-Intensive Collaboration and Decision Making: Research and practical applications in the Dicode project

Mastering Data-Intensive Collaboration and Decision Making: Research and practical applications in the Dicode project

Mastering Data-Intensive Collaboration and Decision Making: Research and practical applications in the Dicode project (Studies in Big Data) by Nikos Karacapilidis
This book reports on cutting-edge research carried out within the context of the EU-funded Dicode project, which aims at facilitating and augmenting collaboration and decision making in data-intensive and cognitively complex settings. Whenever appropriate, Dicode builds on prominent high-performance computing paradigms and large data processing technologies to meaningfully search, analyze, and aggregate data from diverse, extremely large and rapidly evolving sources. The Dicode approach and services are fully explained and particular emphasis is placed on deepening insights regarding the exploitation of big data, as well as on collaboration and issues relating to sense-making support. Building on current advances, the solution developed in the Dicode project brings together the reasoning capabilities of both the machine and humans. It can be viewed as an innovative “workbench” incorporating and orchestrating a set of interoperable services that reduce the data intensiveness and complexity overload at critical decision points to a manageable level, thus permitting stakeholders to be more productive and effective in their work practices.
Ebook format: PDF
Ebook page: 234
File size: 7.39 MB

$55.00

Data Clustering: Algorithms and Applications

Data Clustering: Algorithms and Applications

Data Clustering: Algorithms and Applications

Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) by Charu C. Aggarwal and Chandan K. Reddy
Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.
The book focuses on three primary aspects of data clustering:

• Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization
• Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data
• Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation

In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
Ebook format: PDF
Ebook page: 648
File size: 12.70 MB

$41.22

Big Data: Techniques and Technologies in Geoinformatics

Big Data: Techniques and Technologies in Geoinformatics

Big Data: Techniques and Technologies in Geoinformatics

Big Data: Techniques and Technologies in Geoinformatics by Hassan A. Karimi
Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data.
Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information.
Ebook format: PDF
Ebook page: 306
File size: 4.87 MB

$65.78

Data-Intensive Science

Data-Intensive Science

Data-Intensive Science

Data-Intensive Science (Chapman & Hall/CRC Computational Science) by Terence Critchlow and Kerstin Kleese van Dam
Data-intensive science has the potential to transform scientific research and quickly translate scientific progress into complete solutions, policies, and economic success. But this collaborative science is still lacking the effective access and exchange of knowledge among scientists, researchers, and policy makers across a range of disciplines. Bringing together leaders from multiple scientific disciplines, Data-Intensive Science shows how a comprehensive integration of various techniques and technological advances can effectively harness the vast amount of data being generated and significantly accelerate scientific progress to address some of the world’s most challenging problems.
In the book, a diverse cross-section of application, computer, and data scientists explores the impact of data-intensive science on current research and describes emerging technologies that will enable future scientific breakthroughs. The book identifies best practices used to tackle challenges facing data-intensive science as well as gaps in these approaches. It also focuses on the integration of data-intensive science into standard research practice, explaining how components in the data-intensive science environment need to work together to provide the necessary infrastructure for community-scale scientific collaborations.
Ebook format: PDF
Ebook page: 433
File size: 39.03 MB

$40.00

Data Mining for Bioinformatics

Data Mining for Bioinformatics

Data Mining for Bioinformatics

Data Mining for Bioinformatics by Sumeet Dua and Pradeep Chowriappa
Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field.

The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections:

1. Supplies a complete overview of the evolution of the field and its intersection with computational learning
2. Describes the role of data mining in analyzing large biological databases—explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer
3. Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data
4. Covers supervised learning using classification techniques most commonly used in bioinformatics—addressing the need for validation and benchmarking of inferences derived using either clustering or classification

Ebook format: PDF
Ebook page: 341
File size: 14.20 MB

$36.00

Computational Medicine in Data Mining and Modeling

Computational Medicine in Data Mining and Modeling

Computational Medicine in Data Mining and Modeling

Computational Medicine in Data Mining and Modeling by Goran Rakocevic, Tijana Djukic, Nenad Filipovic and Veljko Milutinovic
This book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient’s medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more. This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter. Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book.
Ebook format: PDF
Ebook page: 383
File size: 9.30 MB

$67.07

Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition

Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition by Bruce Ratner
The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.
Ebook format: PDF
Ebook page: 524
File size: 3.22 MB

$35.00

Pocket Data Mining: Big Data on Small Devices

Pocket Data Mining: Big Data on Small Devices

Pocket Data Mining: Big Data on Small Devices

Pocket Data Mining: Big Data on Small Devices (Studies in Big Data) by Mohamed Medhat Gaber, Frederic Stahl and Joao Bártolo Gomes

Owing to continuous advances in the computational power of handheld devices like smartphones and tablet computers, it has become possible to perform Big Data operations including modern data mining processes onboard these small devices. A decade of research has proved the feasibility of what has been termed as Mobile Data Mining, with a focus on one mobile device running data mining processes. However, it is not before 2010 until the authors of this book initiated the Pocket Data Mining (PDM) project exploiting the seamless communication among handheld devices performing data analysis tasks that were infeasible until recently. PDM is the process of collaboratively extracting knowledge from distributed data streams in a mobile computing environment. This book provides the reader with an in-depth treatment on this emerging area of research. Details of techniques used and thorough experimental studies are given. More importantly and exclusive to this book, the authors provide detailed practical guide on the deployment of PDM in the mobile environment. An important extension to the basic implementation of PDM dealing with concept drift is also reported. In the era of Big Data, potential applications of paramount importance offered by PDM in a variety of domains including security, business and telemedicine are discussed.

Ebook format: PDF
Ebook page: 112
File size: 3.51 MB

$54.00