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Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications (Tech Today)
by Shreyas SubramanianLearn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications (Tech Today)
by Shreyas SubramanianLearn to build cost-effective apps using Large Language Models In Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications, Principal Data Scientist at Amazon Web Services, Shreyas Subramanian, delivers a practical guide for developers and data scientists who wish to build and deploy cost-effective large language model (LLM)-based solutions. In the book, you'll find coverage of a wide range of key topics, including how to select a model, pre- and post-processing of data, prompt engineering, and instruction fine tuning. The author sheds light on techniques for optimizing inference, like model quantization and pruning, as well as different and affordable architectures for typical generative AI (GenAI) applications, including search systems, agent assists, and autonomous agents. You'll also find: Effective strategies to address the challenge of the high computational cost associated with LLMs Assistance with the complexities of building and deploying affordable generative AI apps, including tuning and inference techniques Selection criteria for choosing a model, with particular consideration given to compact, nimble, and domain-specific models Perfect for developers and data scientists interested in deploying foundational models, or business leaders planning to scale out their use of GenAI, Large Language Model-Based Solutions will also benefit project leaders and managers, technical support staff, and administrators with an interest or stake in the subject.
Large Language Models: Concepts, Techniques and Applications
by John Atkinson-AbutridyThis book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more.At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction:•You will explore the fascinating world of LLMs, from its foundations to its most powerful applications•You will learn how to build your own simple applications with some of the LLMsDesigned to guide you step by step, with six chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in NLP.From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond.
Large Language Models: Concepts, Techniques and Applications
by John Atkinson-AbutridyThis book serves as an introduction to the science and applications of Large Language Models (LLMs). You'll discover the common thread that drives some of the most revolutionary recent applications of artificial intelligence (AI): from conversational systems like ChatGPT or BARD, to machine translation, summary generation, question answering, and much more.At the heart of these innovative applications is a powerful and rapidly evolving discipline, natural language processing (NLP). For more than 60 years, research in this science has been focused on enabling machines to efficiently understand and generate human language. The secrets behind these technological advances lie in LLMs, whose power lies in their ability to capture complex patterns and learn contextual representations of language. How do these LLMs work? What are the available models and how are they evaluated? This book will help you answer these and many other questions. With a technical but accessible introduction:•You will explore the fascinating world of LLMs, from its foundations to its most powerful applications•You will learn how to build your own simple applications with some of the LLMsDesigned to guide you step by step, with six chapters combining theory and practice, along with exercises in Python on the Colab platform, you will master the secrets of LLMs and their application in NLP.From deep neural networks and attention mechanisms, to the most relevant LLMs such as BERT, GPT-4, LLaMA, Palm-2 and Falcon, this book guides you through the most important achievements in NLP. Not only will you learn the benchmarks used to evaluate the capabilities of these models, but you will also gain the skill to create your own NLP applications. It will be of great value to professionals, researchers and students within AI, data science and beyond.
Large Language Models: Bridging Theory and Practice
by Uday Kamath Kevin Keenan Garrett Somers Sarah SorensonLarge Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs—their intricate architecture, underlying algorithms, and ethical considerations—require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs. Key Features: Over 100 techniques and state-of-the-art methods, including pre-training, prompt-based tuning, instruction tuning, parameter-efficient and compute-efficient fine-tuning, end-user prompt engineering, and building and optimizing Retrieval-Augmented Generation systems, along with strategies for aligning LLMs with human values using reinforcement learning Over 200 datasets compiled in one place, covering everything from pre- training to multimodal tuning, providing a robust foundation for diverse LLM applications Over 50 strategies to address key ethical issues such as hallucination, toxicity, bias, fairness, and privacy. Gain comprehensive methods for measuring, evaluating, and mitigating these challenges to ensure responsible LLM deployment Over 200 benchmarks covering LLM performance across various tasks, ethical considerations, multimodal applications, and more than 50 evaluation metrics for the LLM lifecycle Nine detailed tutorials that guide readers through pre-training, fine- tuning, alignment tuning, bias mitigation, multimodal training, and deploying large language models using tools and libraries compatible with Google Colab, ensuring practical application of theoretical concepts Over 100 practical tips for data scientists and practitioners, offering implementation details, tricks, and tools to successfully navigate the LLM life- cycle and accomplish tasks efficiently
Large Language Models in Cybersecurity: Threats, Exposure and Mitigation
This open access book provides cybersecurity practitioners with the knowledge needed to understand the risks of the increased availability of powerful large language models (LLMs) and how they can be mitigated. It attempts to outrun the malicious attackers by anticipating what they could do. It also alerts LLM developers to understand their work's risks for cybersecurity and provides them with tools to mitigate those risks. The book starts in Part I with a general introduction to LLMs and their main application areas. Part II collects a description of the most salient threats LLMs represent in cybersecurity, be they as tools for cybercriminals or as novel attack surfaces if integrated into existing software. Part III focuses on attempting to forecast the exposure and the development of technologies and science underpinning LLMs, as well as macro levers available to regulators to further cybersecurity in the age of LLMs. Eventually, in Part IV, mitigation techniques that should allow safe and secure development and deployment of LLMs are presented. The book concludes with two final chapters in Part V, one speculating what a secure design and integration of LLMs from first principles would look like and the other presenting a summary of the duality of LLMs in cyber-security. This book represents the second in a series published by the Technology Monitoring (TM) team of the Cyber-Defence Campus. The first book entitled "Trends in Data Protection and Encryption Technologies" appeared in 2023. This book series provides technology and trend anticipation for government, industry, and academic decision-makers as well as technical experts.
Large Language Models Projects: Apply and Implement Strategies for Large Language Models
by Pere MartraThis book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will Learn Gain practical experience by working with models from OpenAI and the Hugging Face library Use essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databases Create and implement projects using LLM while understanding the design decisions involved Understand the role of Large Language Models in larger corporate settings Who This Book Is For Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks
Large Plastic Deformation of Crystalline Aggregates (CISM International Centre for Mechanical Sciences #376)
by Cristian TeodosiuThe book gives a comprehensive view of the present ability to take into account the microstructure and texture evolution in building up engineering models of the plastic behaviour of polycrystalline materials at large strains. It is designed for postgraduate students, research engineers and academics that are interested in using advanced models of the mechanical behaviour of polycrystalline materials.
Large-Scale Agile Frameworks: Agile Frameworks, agile Infrastruktur und pragmatische Lösungen zur digitalen Transformation
by Sascha BlockDas Buch Large-Scale Agile Frameworks bietet praktische Lösungen für die team- und funktionsübergreifende Priorisierung von Anforderungen und Dokumentation für Unternehmen. Es spiegelt das Zusammenspiel aktueller Technologietrends wie Cloud Computing und organisatorischer Anforderungen an Microservices wider. Organisationen sind zunehmend gefordert, ihre IT-Strategie an den Kundenbedürfnissen nach kundenzentrierten und serviceorientierten Produkten und Dienstleistungen auszurichten. Das Buch analysiert die besonderen Anforderungen an ein differenziertes Software-Dienstleistungsangebot und zeigt, wie agile Prinzipien zur Lösung dieser Probleme wirksam sind. Das Buch hebt auch die Bedeutung einer groß angelegten agilen Entwicklung hervor und bietet Organisationen eine Anleitung zur Transformation ihrer Struktur in Richtung einer agilen Priorisierung. Das Buch behandelt verschiedene geeignete Modelle, Methoden und agile Werkzeuge und gibt Empfehlungen zur funktionsübergreifenden Priorisierung von Anforderungen. Es berücksichtigt auch die Notwendigkeit von IT-Sicherheit und zeigt, wie diese in den gesamten agilen Entwicklungsprozess integriert werden kann.
Large-Scale Agile Frameworks: Agile Frameworks, Agile Infrastructure and Pragmatic Solutions for Digital Transformation
by Sascha BlockThe book Large-Scale Agile Frameworks provides practical solutions for cross-team and cross-functional prioritization of requirements and documentation for enterprises. It reflects the interplay of current technology trends such as cloud computing and organizational requirements for microservices. Organizations are increasingly required to align their IT strategy with customer needs for customer-centric and service-oriented products and services. The book analyzes the unique requirements of a differentiated software service offering and shows how agile principles are effective in addressing these issues. The book also highlights the importance of large-scale agile development and provides guidance to organizations on how to transform their structure towards agile prioritization. The book covers various appropriate models, methodologies, and agile tools and provides recommendations for cross-functional prioritization of requirements. It also considers the need for IT security and shows how it can be integrated into the overall agile development process.
Large Scale and Big Data: Processing and Management
by Sherif Sakr Mohamed GaberLarge Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing t
Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention: International Workshops, LABELS 2019, HAL-MICCAI 2019, and CuRIOUS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings (Lecture Notes in Computer Science #11851)
by Luping Zhou Nicholas Heller Yiyu Shi Yiming Xiao Raphael Sznitman Veronika Cheplygina Diana Mateus Emanuele Trucco X. Sharon Hu Danny Chen Matthieu Chabanas Hassan Rivaz Ingerid ReinertsenThis book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications in medical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection.
Large Scale Collaborative Virtual Environments (Distinguished Dissertations)
by Chris GreenhalghCollaborative virtual environments are multi-user virtual realities which actively support communication and co-operation. This book addresses the theory, design, realisation and evaluation of such systems, with a particular emphasis on support for large numbers of distributed users. A broad approach is taken, which ranges from the sociology of interpersonal communication to the management of communication in distributed systems. The emphasis on multi-user environments distinguishes this book from the many general books on virtual reality which only deal with single-user systems. This book presents: models of multi-party awareness and interaction in space-based systems; detailed designs of two prototypes (MASSIVE-1 and MASSIVE-2); experiences with collaborative virtual environments created using these; and analyses of the corresponding network requirements. Many of these results and ideas are applicable to other systems and approaches.
Large-Scale Complex IT Systems. Development, Operation and Management: 17th Monterey Workshop 2012, Oxford, UK, March 19-21, 2012, Revised Selected Papers (Lecture Notes in Computer Science #7539)
by Radu Calinescu David GarlanThis book presents the thoroughly refereed and revised post-workshop proceedings of the 17th Monterey Workshop, held in Oxford, UK, in March 2012. The workshop explored the challenges associated with the Development, Operation and Management of Large-Scale complex IT Systems. The 21 revised full papers presented were significantly extended and improved by the insights gained from the productive and lively discussions at the workshop, and the feedback from the post-workshop peer reviews.
Large Scale Computations in Air Pollution Modelling (NATO Science Partnership Subseries: 2 #57)
by Zahari Zlatev Jørgen Brandt Peter J. H. Builtjes Gregory Carmichael Ivan Dimov Jack Dongarra H. Van Dop Krassimir Georgiev Heinz Hass Roberto San José1. Contents of these proceedings. These proceedings contain most of the papers which were presented at the NATO ARW (Advanced Research Workshop) on "Large Scale Computations in Air Pollution Modelling". The workshop was held, from June 6 to June to, 1998, in Residence Bistritza, a beautiful site near Sofia, the capital of Bulgaria, and at the foot of the mountain Vitosha. 2. Participants in the NATO ARW. Scientists from 23 countries in Europe, North America and Asia attended the meeting and participated actively in the discussions. The total number of participants was 57. The main topic of the discussions was the role of the large mathematical models in resolving difficult problems connected with the protection of our environment. 3. Major topics discussed at the workshop. The protection of our environment is one of the most important problems facing modern society. The importance of this problem has steadily increased during the last two-three decades, and environment protection will become even more important in the next century. Reliable and robust control strategies for keeping the pollution caused by harmful chemical compounds under certain safe levels have to be developed and used in a routine way. Large mathematical models, in which all important physical and chemical processes are adequately described, can successfully be used to solve this task.
Large-Scale Data Analytics
by Aris Gkoulalas-Divanis Abderrahim LabbiThis edited book collects state-of-the-art research related to large-scale data analytics that has been accomplished over the last few years. This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy.There is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis.Large-Scale Data Analytics is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. Students, researchers, professionals and practitioners will find this book an authoritative and comprehensive resource.
Large-Scale Disk Failure Prediction: PAKDD 2020 Competition and Workshop, AI Ops 2020, February 7 – May 15, 2020, Revised Selected Papers (Communications in Computer and Information Science #1261)
by Cheng He Mengling Feng Patrick P. C. Lee Pinghui Wang Shujie Han Yi LiuThis book constitutes the thoroughly refereed post-competition proceedings of the AI Ops Competition on Large-Scale Disk Failure Prediction, conducted between February 7th and May 15, 2020 on the Alibaba Cloud Tianchi Platform. A dedicated workshop, featuring the best performing teams of the competition, was held at the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, in Singapore, in April 2019. Due to the COVID-19 pandemic, the workshop was hosted online. This book includes 13 selected contributions: an introduction to dataset, selected approaches of the competing teams and the competition summary, describing the competition task, practical challenges, evaluation metrics, etc.
Large-scale Distributed Systems and Energy Efficiency: A Holistic View (Wiley Series on Parallel and Distributed Computing #94)
by Jean-Marc PiersonAddresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks With concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. Large-Scale Distributed Systems and Energy Efficiency: A Holistic View addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks. After an introductory overview of the energy demands of current Information and Communications Technology (ICT), individual chapters offer in-depth analyses of such topics as cloud computing, green networking (both wired and wireless), mobile computing, power modeling, the rise of green data centers and high-performance computing, resource allocation, and energy efficiency in peer-to-peer (P2P) computing networks. Discusses measurement and modeling of the energy consumption method Includes methods for energy consumption reduction in diverse computing environments Features a variety of case studies and examples of energy reduction and assessment Timely and important, Large-Scale Distributed Systems and Energy Efficiency is an invaluable resource for ways of increasing the energy efficiency of computing systems and networks while simultaneously reducing the carbon footprint.
Large-scale Distributed Systems and Energy Efficiency: A Holistic View (Wiley Series on Parallel and Distributed Computing #94)
by Jean-Marc PiersonAddresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks With concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. Large-Scale Distributed Systems and Energy Efficiency: A Holistic View addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks. After an introductory overview of the energy demands of current Information and Communications Technology (ICT), individual chapters offer in-depth analyses of such topics as cloud computing, green networking (both wired and wireless), mobile computing, power modeling, the rise of green data centers and high-performance computing, resource allocation, and energy efficiency in peer-to-peer (P2P) computing networks. Discusses measurement and modeling of the energy consumption method Includes methods for energy consumption reduction in diverse computing environments Features a variety of case studies and examples of energy reduction and assessment Timely and important, Large-Scale Distributed Systems and Energy Efficiency is an invaluable resource for ways of increasing the energy efficiency of computing systems and networks while simultaneously reducing the carbon footprint.
Large-scale Graph Analysis: System, Algorithm and Optimization (Big Data Management)
by Yingxia Shao Bin Cui Lei ChenThis book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.
Large-Scale Graph Processing Using Apache Giraph
by Sherif Sakr Faisal Moeen Orakzai Ibrahim Abdelaziz Zuhair KhayyatThis book takes its reader on a journey through Apache Giraph, a popular distributed graph processing platform designed to bring the power of big data processing to graph data. Designed as a step-by-step self-study guide for everyone interested in large-scale graph processing, it describes the fundamental abstractions of the system, its programming models and various techniques for using the system to process graph data at scale, including the implementation of several popular and advanced graph analytics algorithms.The book is organized as follows: Chapter 1 starts by providing a general background of the big data phenomenon and a general introduction to the Apache Giraph system, its abstraction, programming model and design architecture. Next, chapter 2 focuses on Giraph as a platform and how to use it. Based on a sample job, even more advanced topics like monitoring the Giraph application lifecycle and different methods for monitoring Giraph jobs are explained. Chapter 3 then provides an introduction to Giraph programming, introduces the basic Giraph graph model and explains how to write Giraph programs. In turn, Chapter 4 discusses in detail the implementation of some popular graph algorithms including PageRank, connected components, shortest paths and triangle closing. Chapter 5 focuses on advanced Giraph programming, discussing common Giraph algorithmic optimizations, tunable Giraph configurations that determine the system’s utilization of the underlying resources, and how to write a custom graph input and output format. Lastly, chapter 6 highlights two systems that have been introduced to tackle the challenge of large scale graph processing, GraphX and GraphLab, and explains the main commonalities and differences between these systems and Apache Giraph.This book serves as an essential reference guide for students, researchers and practitioners in the domain of large scale graph processing. It offers step-by-step guidance, with several code examples and the complete source code available in the related github repository. Students will find a comprehensive introduction to and hands-on practice with tackling large scale graph processing problems using the Apache Giraph system, while researchers will discover thorough coverage of the emerging and ongoing advancements in big graph processing systems.
Large-Scale Group Decision-Making: State-to-the-Art Clustering and Consensus Paths
by Su-Min Yu Zhi-Jiao DuThis book explores clustering operations in the context of social networks and consensus-reaching paths that take into account non-cooperative behaviors. This book focuses on the two key issues in large-scale group decision-making: clustering and consensus building. Clustering aims to reduce the dimension of a large group. Consensus reaching requires that the divergent individual opinions of the decision makers converge to the group opinion. This book emphasizes the similarity of opinions and social relationships as important measurement attributes of clustering, which makes it different from traditional clustering methods with single attribute to divide the original large group without requiring a combination of the above two attributes. The proposed consensus models focus on the treatment of non-cooperative behaviors in the consensus-reaching process and explores the influence of trust loss on the consensus-reaching process.The logic behind is as follows: firstly, a clustering algorithm is adopted to reduce the dimension of decision-makers, and then, based on the clusters’ opinions obtained, a consensus-reaching process is carried out to obtain a decision result acceptable to the majority of decision-makers. Graduates and researchers in the fields of management science, computer science, information management, engineering technology, etc., who are interested in large-scale group decision-making and consensus building are potential audience of this book. It helps readers to have a deeper and more comprehensive understanding of clustering analysis and consensus building in large-scale group decision-making.
Large Scale Hierarchical Classification: State of the Art (SpringerBriefs in Computer Science)
by Azad Naik Huzefa RangwalaThis SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past few years. In this book, the authors provide a comprehensive overview of various state-of-the-art existing methods and algorithms that were developed to solve the HC problem in large scale domains. Several challenges faced by LSHC is discussed in detail such as: 1. High imbalance between classes at different levels of the hierarchy 2. Incorporating relationships during model learning leads to optimization issues 3. Feature selection 4. Scalability due to large number of examples, features and classes 5. Hierarchical inconsistencies 6. Error propagation due to multiple decisions involved in making predictions for top-down methods The brief also demonstrates how multiple hierarchies can be leveraged for improving the HC performance using different Multi-Task Learning (MTL) frameworks. The purpose of this book is two-fold: 1. Help novice researchers/beginners to get up to speed by providing a comprehensive overview of several existing techniques. 2. Provide several research directions that have not yet been explored extensively to advance the research boundaries in HC. New approaches discussed in this book include detailed information corresponding to the hierarchical inconsistencies, multi-task learning and feature selection for HC. Its results are highly competitive with the state-of-the-art approaches in the literature.
Large Scale Interactive Fuzzy Multiobjective Programming: Decomposition Approaches (Studies in Fuzziness and Soft Computing #48)
by Masatoshi SakawaSimultaneous considerations of multiobjectiveness, fuzziness and block angular structures involved in the real-world decision making problems lead us to the new field of interactive multiobjective optimization for large scale programming problems under fuzziness. The aim of this book is to introduce the latest advances in the new field of interactive multiobjective optimization for large scale programming problems under fuzziness on the basis of the author's continuing research. Special stress is placed on interactive decision making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness. The book is intended for graduate students, researchers and practitioners in the fields of operations research, industrial engineering, management science and computer science.
Large-Scale Knowledge Resources. Construction and Application: Construction and Application - Third International Conference on Large-Scale Knowledge Resources, LKR 2008, Tokyo, Japan, March 3-5, 2008, Proceedings (Lecture Notes in Computer Science #4938)
by Takenobu Tokunaga Antonio OrtegaAtthestartofthe21stcentury,wearenowwellonthewaytowardsaknowled- intensive society, in which knowledge plays ever more important roles. Thus, research interest should inevitably shift from information to knowledge, with the problems of building, organizing, maintaining and utilizing knowledge - coming centralissues in a wide varietyof ?elds. The 21stCentury COE program “Framework for Systematization and Application of Large-scale Knowledge - sources (COE-LKR)” conducted by the Tokyo Institute of Technology is one of several early attempts worldwide to address these important issues. Inspired by this project, LKR2008 aimed at bringing together diverse contributions in cognitive science, computer science, education and linguistics to explore design, construction, extension, maintenance, validation and application of knowledge. Respondingtoourcallforpapers,wereceived38submissionfromavarietyof researchareas.EachpaperwasreviewedbythreeProgramCommitteemembers. Since we were aiming at an interdisciplinary conference covering a wide range of topics concerning large-scale knowledge resources (LKR), each paper was assigned a reviewer from a topic area outside the main thrust of the paper. This reviewer was asked to assess whether the authors described the moti- tion and importance of their work in a comprehensible manner even for readers in other research areas. Following a rigorous reviewing process, we accepted 14 regular papers and 12 poster papers.