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Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
by Mitra Basu Tin Kam HoAutomatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.
Data Compression: The Complete Reference
by David SalomonFrom archiving data to CD-ROMs, and from coding theory to image analysis, many facets of computing make use of data compression in one form or another. This is an overview of the many different types of compression, including a taxonomy, an analysis of the most common systems of compression, discussion of their relative benefits and disadvantages, and their most common uses. Readers are presupposed to have a basic understanding of computer science -- essentially the storage of data in bytes and bits and computing terminology -- but otherwise this book is self-contained. It divides neatly into four main parts based on the main branches of data compression: run length encoding, statistical methods, dictionary-based methods, and lossy image compression. All of the most well-known compression techniques are covered including Zip, BinHex, Huffman coding, and GIF.
Data Compression: The Complete Reference
by David SalomonA comprehensive reference for the many different types and methods of compression, including a detailed and helpful taxonomy, an analysis of the most common methods, and discussions on their use and comparative benefits. The presentation is organized into the main branches of the field: run length encoding, statistical methods, dictionary-based methods, image compression, audio compression, and video compression. Detailed descriptions and explanations of the most well- known and frequently used methods are covered in a self-contained fashion, with an accessible style and technical level for specialists and nonspecialists. In short, the book provides an invaluable reference and guide for all computer scientists, computer engineers, electrical engineers, signal/image processing engineers and other scientists needing a comprehensive compilation for a broad range of compression methods.
Data Compression: The Complete Reference
by David SalomonThis book provides a comprehensive reference for the many different types and methods of compression. Included are a detailed and helpful taxonomy, analysis of most common methods, and discussions on the use and comparative benefits of methods and description of "how to" use them. Detailed descriptions and explanations of the most well-known and frequently used compression methods are covered in a self-contained fashion, with an accessible style and technical level for specialists and nonspecialists. Comments and suggestions of many readers have been included as a benefit to future readers, and a website is maintained and updated by the author.
Data Compression: The Complete Reference
by David Salomonviii • The second new chapter, Chapter 6, discusses video compression. The chapter opens with a general description of CRT operation and basic analog and digital video concepts. It continues with a general discussion of video compression, and it concludes with a description of MPEG-1 and H.261. • Audio compression is the topic of the third new chapter, Chapter 7. The first topic in this chapter is the properties of the human audible system and how they can be exploited to achieve lossy audio compression. A discussion of a few simple audio compression methods follows, and the chapter concludes with a description of the three audio layers of MPEG-1, including the very popular mp3 format. Other new material consists of the following: • Conditional image RLE (Section 1.4.2). • Scalar quantization (Section 1.6). • The QM coder used in JPEG, JPEG 2000, and JBIG is now included in Sec tion 2.16. • Context-tree weighting is discussed in Section 2.19. Its extension to lossless image compression is the topic of Section 4.24. • Section 3.4 discusses a sliding buffer method called repetition times. • The troublesome issue of patents is now also included (Section 3.25). • The relatively unknown Gray codes are discussed in Section 4.2.1, in connection with image compression. • Section 4.3 discusses intuitive methods for image compression, such as subs- pling and vector quantization.
Data Compression in Digital Systems
by Roy HoffmanData compression is now indispensable to products and services of many industries including computers, communications, healthcare, publishing and entertainment. This invaluable resource introduces this area to information system managers and others who need to understand how it is changing the world of digital systems. For those who know the technology well, it reveals what happens when data compression is used in real-world applications and provides guidance for future technology development.
Data Conscience: Algorithmic Siege on our Humanity
by Brandeis Hill MarshallDATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with: Discussions of the importance of transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms
Data Conscience: Algorithmic Siege on our Humanity
by Brandeis Hill MarshallDATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with: Discussions of the importance of transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms
Data Control: Major Challenge for the Digital Society
by Jean-Louis MoninoBusinesses are becoming increasingly aware of the importance of data and information. As such, they are eager to develop ways to "manage" them, to enrich them and take advantage of them. Indeed, the recent explosion of a phenomenal amount of data, and the need to analyze it, brings to the forefront the well-known hierarchical model: "Data, Information, Knowledge". "Data"– this new intangible manna – is produced in real time. It arrives in a continuous stream and comes from a multitude of sources that are generally heterogeneous. This accumulation of data of all kinds is generating new activities designed to analyze these huge amounts of information. It is therefore necessary to adapt and try new approaches, methods, new knowledge and new ways of working. This leads to new properties and new issues as a logical reference must be created and implemented. At the company level, this mass of data is difficult to manage; interpreting it is the predominant challenge.
Data Control: Major Challenge for the Digital Society
by Jean-Louis MoninoBusinesses are becoming increasingly aware of the importance of data and information. As such, they are eager to develop ways to "manage" them, to enrich them and take advantage of them. Indeed, the recent explosion of a phenomenal amount of data, and the need to analyze it, brings to the forefront the well-known hierarchical model: "Data, Information, Knowledge". "Data"– this new intangible manna – is produced in real time. It arrives in a continuous stream and comes from a multitude of sources that are generally heterogeneous. This accumulation of data of all kinds is generating new activities designed to analyze these huge amounts of information. It is therefore necessary to adapt and try new approaches, methods, new knowledge and new ways of working. This leads to new properties and new issues as a logical reference must be created and implemented. At the company level, this mass of data is difficult to manage; interpreting it is the predominant challenge.
Data Correcting Approaches in Combinatorial Optimization (SpringerBriefs in Optimization)
by Boris I. Goldengorin Panos M. PardalosData Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems. Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.
Data Culture: Develop An Effective Data-Driven Organization
by Dr Shorful IslamOrganizations often start their data journey by either procuring the technology or hiring the people. However, without an effective data-driven culture in place, they can struggle to derive value from their investments.Data Culture explores how data leaders can develop and nurture a data-driven culture tailored to their organization's needs. It outlines the types of data leadership and teams needed and the key building blocks for success, such as team recruitment, building and training, leadership, process, behavioural change management, developing, sustaining and measuring a data culture, company values and everyday decision making. It also explores the nuances of how different types of data cultures work with different types of companies, what to avoid and the differences between building a data culture from scratch and changing an existing data culture from within.With this hands-on guide, senior data leader Shorful Islam takes readers through how to successfully establish or change a data culture, sharing his expertise in behavioural change psychology and two decades of experience in fostering data culture in organizations. Supported throughout by real-world examples and cases, this will be an essential read for all data leaders and anyone involved in developing a data-driven organizational culture.
Data Curation and Information Systems Design from Australasia: Implications for Cataloguing of Vernacular Knowledge in Galleries, Libraries, Archives, and Museums (Advances in Librarianship #54)
by Julie Nichols and Bharat MehraThe need for decolonizing cultural institutions and their mismanagement practices in galleries, libraries, archives, and museums, of First Nations peoples’ materials and knowledge has been widely recognised. However, this has not translated into an information systems design, nor a complementary solution representing an alternative world view. Instead, the entrenched legacy of the neoliberal sector’s curatorial and archival practices remains intact, and their authority stays unquestioned. This edited book’s unique viewpoint is its exploration of projects that investigate innovative data curation strategies through the thematics of visual representation of infrastructure, and bodies of knowledge. Authors from Indigenous and non-Indigenous backgrounds underpin their chapters with a social justice approach to investigations around different knowledge systems. They powerfully challenge entrenched assumptions of knowledge capture and dissemination of the western academy. An emphasis on visualisations of cultural heritage materials across a variety of case studies using technologies that range from augmented and virtual realities to mixed reality aims to raise questions for debate in the way Indigenous data is collected, managed, curated, governed, and represented and by whom.
Data Curation and Information Systems Design from Australasia: Implications for Cataloguing of Vernacular Knowledge in Galleries, Libraries, Archives, and Museums (Advances in Librarianship #54)
by Julie Nichols Bharat MehraThe need for decolonizing cultural institutions and their mismanagement practices in galleries, libraries, archives, and museums, of First Nations peoples’ materials and knowledge has been widely recognised. However, this has not translated into an information systems design, nor a complementary solution representing an alternative world view. Instead, the entrenched legacy of the neoliberal sector’s curatorial and archival practices remains intact, and their authority stays unquestioned. This edited book’s unique viewpoint is its exploration of projects that investigate innovative data curation strategies through the thematics of visual representation of infrastructure, and bodies of knowledge. Authors from Indigenous and non-Indigenous backgrounds underpin their chapters with a social justice approach to investigations around different knowledge systems. They powerfully challenge entrenched assumptions of knowledge capture and dissemination of the western academy. An emphasis on visualisations of cultural heritage materials across a variety of case studies using technologies that range from augmented and virtual realities to mixed reality aims to raise questions for debate in the way Indigenous data is collected, managed, curated, governed, and represented and by whom.
Data Deduplication for Data Optimization for Storage and Network Systems
by Daehee Kim Sejun Song Baek-Young ChoiThis book introduces fundamentals and trade-offs of data de-duplication techniques. It describes novel emerging de-duplication techniques that remove duplicate data both in storage and network in an efficient and effective manner. It explains places where duplicate data are originated, and provides solutions that remove the duplicate data. It classifies existing de-duplication techniques depending on size of unit data to be compared, the place of de-duplication, and the time of de-duplication. Chapter 3 considers redundancies in email servers and a de-duplication technique to increase reduction performance with low overhead by switching chunk-based de-duplication and file-based de-duplication. Chapter 4 develops a de-duplication technique applied for cloud-storage service where unit data to be compared are not physical-format but logical structured-format, reducing processing time efficiently. Chapter 5 displays a network de-duplication where redundant data packets sent by clients are encoded (shrunk to small-sized payload) and decoded (restored to original size payload) in routers or switches on the way to remote servers through network. Chapter 6 introduces a mobile de-duplication technique with image (JPEG) or video (MPEG) considering performance and overhead of encryption algorithm for security on mobile device.
Data Deduplication for High Performance Storage System
by Dan FengThis book comprehensively introduces data deduplication technologies for storage systems. It first presents the overview of data deduplication including its theoretical basis, basic workflow, application scenarios and its key technologies, and then the book focuses on each key technology of the deduplication to provide an insight into the evolution of the technology over the years including chunking algorithms, indexing schemes, fragmentation reduced schemes, rewriting algorithm and security solution. In particular, the state-of-the-art solutions and the newly proposed solutions are both elaborated. At the end of the book, the author discusses the fundamental trade-offs in each of deduplication design choices and propose an open-source deduplication prototype. The book with its fundamental theories and complete survey can guide the beginners, students and practitioners working on data deduplication in storage system. It also provides a compact reference in the perspective of key data deduplication technologies for those researchers in developing high performance storage solutions.
Data, Disruption & Digital Leadership: How to Win the Innovation Game
by Philipp Futterknecht Tobias HertfelderSince the theory of relativity we know that massive objects attract things by their gravitation. The greater the mass, the greater the force of attraction. It is the same in strategy projects. Each project participant is a massive participant and has an impact on the interaction. What has changed dramatically is the influence of data on this process. Those who do not take this into account will suffer enormous losses in the future. As this change creates a new equilibrium, the chances of success of the methods and behaviors used also change. In this book, you will learn how to master this change and what you need to do so.
Data Dissemination and Query in Mobile Social Networks (SpringerBriefs in Computer Science)
by Jiming Chen Jialu Fan Youxian SunWith the increasing popularization of personal hand-held mobile devices, more people use them to establish network connectivity and to query and share data among themselves in the absence of network infrastructure, creating mobile social networks (MSNet). Since users are only intermittently connected to MSNets, user mobility should be exploited to bridge network partitions and forward data. Currently, data route/forward approaches for such intermittently connected networks are commonly "store-carry-and-forward" schemes, which exploit the physical user movements to carry data around the network and overcome path disconnection. And since the source and destination may be far away from each other, the delay for the destination to receive the data from the source may be long. MSNets can be viewed as one type of socially-aware delay tolerant networks (DTNs). Observed from social networks, the contact frequencies are probably different between two friends and two strangers, and this difference should be taken into consideration when designing data dissemination and query schemes in MSNets. In this book, the fundamental concepts of MSNets are introduced including the background, key features and potential applications of MSNets, while also presenting research topics, such as, MSNets as realistic social contact traces and user mobility models. Because the ultimate goal is to establish networks that allow mobile users to quickly and efficiently access interesting information, particular attention is paid to data dissemination and query schemes in subsequent sections. Combined with geography information, the concepts of community and centrality are employed from a social network perspective to propose several data dissemination and query schemes, and further use real social contact traces to evaluate their performance, demonstrating that such schemes achieve better performance when exploiting more social relationships between users.
Data Dissemination in Wireless Computing Environments (Advances in Database Systems #19)
by Kian-Lee Tan Beng Chin OoiIn our increasingly mobile world the ability to access information on demand at any time and place can satisfy people's information needs as well as confer on them a competitive advantage. The emergence of battery-operated, low-cost and portable computers such as palmtops and PDAs, coupled with the availability and exploitation of wireless networks, have made possible the potential for ubiquitous computing. Through the wireless networks, portable equipments will become an integrated part of existing distributed computing environments, and mobile users can have access to data stored at information servers located at the static portion of the network even while they are on the move. Traditionally, information is retrieved following a request-response model. However, this model is no longer adequate in a wireless computing environment. First, the wireless channel is unreliable and the bandwidth is low compared to the wired counterpart. Second, the environment is essentially asymmetric with a large number of mobile users accessing a small number of servers. Third, battery-operated portable devices can typically operate only for a short time because of the short battery lifespan. Thus, clients are expected to be disconnected most of the time. To overcome these limitations, there has been a proliferation of research efforts on designing data delivery mechanisms to support wireless computing more effectively. Data Dissemination in Wireless Computing Environments focuses on such mechanisms. The purpose is to provide a thorough and comprehensive review of recent advances on energy-efficient data delivery protocols, efficient wireless channel bandwidth utilization, reliable broadcasting and cache invalidation strategies for clients with long disconnection time. Besides surveying existing methods, this book also compares and evaluates some of the more promising schemes.
Data Driven: An Introduction to Management Consulting in the 21st Century (Management for Professionals)
by Jeremy David CuruksuThis book is a “scientific” introduction to management consulting that covers elementary and more advanced concepts, such as strategy and client-relationship. It discusses the emerging role of information technologies in consulting activities and introduces the essential tools in data science, assuming no technical background. Drawing on extensive literature reviews with more than 200 peer reviewed articles, reports, books and surveys referenced, this book has at least four objectives: to be scientific, modern, complete and concise. An interactive version of some sections (industry snapshots, method toolbox) is freely accessible at econsultingdata.com.
Data Driven: Truckers, Technology, and the New Workplace Surveillance
by Karen LevyA behind-the-scenes look at how digital surveillance is affecting the trucking way of lifeLong-haul truckers are the backbone of the American economy, transporting goods under grueling conditions and immense economic pressure. Truckers have long valued the day-to-day independence of their work, sharing a strong occupational identity rooted in a tradition of autonomy. Yet these workers increasingly find themselves under many watchful eyes. Data Driven examines how digital surveillance is upending life and work on the open road, and raises crucial questions about the role of data collection in broader systems of social control.Karen Levy takes readers inside a world few ever see, painting a bracing portrait of one of the last great American frontiers. Federal regulations now require truckers to buy and install digital monitors that capture data about their locations and behaviors. Intended to address the pervasive problem of trucker fatigue by regulating the number of hours driven each day, these devices support additional surveillance by trucking firms and other companies. Traveling from industry trade shows to law offices and truck-stop bars, Levy reveals how these invasive technologies are reconfiguring industry relationships and providing new tools for managerial and legal control—and how truckers are challenging and resisting them.Data Driven contributes to an emerging conversation about how technology affects our work, institutions, and personal lives, and helps to guide our thinking about how to protect public interests and safeguard human dignity in the digital age.
Data-Driven 3D Facial Animation
by Zhigang Deng Ulrich NeumannData-Driven 3D Facial Animation systematically describes the important techniques developed over the last ten years or so. Although 3D facial animation is used more and more in the entertainment industries, to date there have been very few books that address the techniques involved. Comprehensive in scope, the book covers not only traditional lip-sync (speech animation), but also expressive facial motion, facial gestures, facial modeling, editing and sketching, and facial animation transferring. It provides an up-to-date reference source for academic research and for professionals working in the facial animation field.
Data-Driven Alexa Skills: Voice Access to Rich Data Sources for Enterprise Applications
by Simon A. KingabyDesign and build innovative, custom, data-driven Alexa skills for home or business. Working through several projects, this book teaches you how to build Alexa skills and integrate them with online APIs. If you have basic Python skills, this book will show you how to build data-driven Alexa skills. You will learn to use data to give your Alexa skills dynamic intelligence, in-depth knowledge, and the ability to remember. Data-Driven Alexa Skills takes a step-by-step approach to skill development. You will begin by configuring simple skills in the Alexa Skill Builder Console. Then you will develop advanced custom skills that use several Alexa Skill Development Kit features to integrate with lambda functions, Amazon Web Services (AWS), and Internet data feeds. These advanced skills enable you to link user accounts, query and store data using a NoSQL database, and access real estate listings and stock prices via web APIs.What You Will LearnSet up and configure your development environment properly the first timeBuild Alexa skills quickly and efficiently using Agile tools and techniquesCreate a variety of data-driven Alexa skills for home and businessAccess data from web applications and Internet data sources via their APIsTest with unit-testing frameworks throughout the development life cycleManage and query your data using the DynamoDb NoSQL database enginesWho This Book Is ForDevelopers who wish to go beyond Hello World and build complex, data-driven applications on Amazon's Alexa platform; developers who want to learn how to use Lambda functions, the Alexa Skills SDK, Alexa Presentation Language, and Alexa Conversations; developers interested in integrating with public APIs such as real estate listings and stock market prices. Readers will need to have basic Python skills.
Data-Driven Approach for Bio-medical and Healthcare (Data-Intensive Research)
by Nilanjan DeyThe book presents current research advances, both academic and industrial, in machine learning, artificial intelligence, and data analytics for biomedical and healthcare applications. The book deals with key challenges associated with biomedical data analysis including higher dimensions, class imbalances, smaller database sizes, etc. It also highlights development of novel pattern recognition and machine learning methods specific to medical and genomic data, which is extremely necessary but highly challenging. The book will be useful for healthcare professionals who have access to interesting data sources but lack the expertise to use data mining effectively.
Data Driven Approach Towards Disruptive Technologies: Proceedings of MIDAS 2020 (Studies in Autonomic, Data-driven and Industrial Computing)
by T. P. Singh Ravi Tomar Tanupriya Choudhury Thinagaran Perumal Hussain Falih MahdiThis book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India, during 4–5 September 2020. The book addresses the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like data-driven industrial IoT, bioinformatics, network and security, autonomous computing and various other aligned areas. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber-physical system and smart agriculture. All papers have been carefully reviewed. The book is of interest to computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.