Scalable Uncertainty Management, Davide Ciucci; Gabriella Pasi; Barbara Vantaggi
Автор: Christoph Beierle; Alex Dekhtyar Название: Scalable Uncertainty Management ISBN: 3319235397 ISBN-13(EAN): 9783319235394 Издательство: Springer Рейтинг: Цена: 8944.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The mysterious world of normal numbers.- Bayesian Networks.- Probabilistic Query Answering in the Bayesian Description Logic BEL.- The Complexity of Plate Probabilistic Models.- DL-Lite Bayesian Networks: A Tractable Probabilistic Graphical Model.- Probabilistic Models.- State Space Search with Stochastic Costs and Risk Aversion.- On the Impact of Junction-Tree Topology on Weighted Model Counting.- A System for Probabilistic Inductive Answer Set Programming.- Towards Large-Scale Probabilistic OBDA.- Reasoning over Linear Probabilistic Knowledge Bases with Priorities.- Intelligent Data Analytics.- Evenness-based reasoning with logical proportions applied to classification.- Multivariate Cluster-Based Discretization for Bayesian Network Structure Learning.- Modeling and Forecasting Time Series of Compositional Data: A Generalized Dirichlet Power Steady Model.- Linguistic and Graphical Explanation of a Cluster-based Data Structure.- Possibility Theory, Belief Functions and Transformations.- Probability-possibility transformations: Application to credal networks.- Planning in Partially Observable Domains with Fuzzy Epistemic States and Probabilistic Dynamics.- Propagation of Belief Functions in Singly-Connected Hybrid Directed Evidential Networks.- Uncertain logical gates in possibilistic networks. An application to human geography.- Argumentation.- Undercutting in argumentation systems.- Formalizing Explanatory Dialogues.- Towards a dual process cognitive model for argument evaluation.- Change in abstract bipolar argumentation systems.- On argumentation with purely defeasible rules.- Dealing with Inconsistency.- A possibilistic analysis of inconsistency.- First-Order Under-Approximations of Consistent Query Answers.- Using Rules of Thumb for Repairing Inconsistent Answer Set Program.- Applications.- Fuzzy XPath for the Automatic Search of Fuzzy Formulae Models.- ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution.- Matching uncertain identities against sparse knowledge.
Автор: Lluis Godo; Andrea Pugliese Название: Scalable Uncertainty Management ISBN: 3642043879 ISBN-13(EAN): 9783642043871 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume contains the papers presented at the Third International Conference on Scalable Uncertainty Management, SUM 2009, in Washington, DC, September 28-30, 2009.
Автор: Jonathan Bartlett Название: Building Scalable PHP Web Applications Using the Cloud ISBN: 148425211X ISBN-13(EAN): 9781484252116 Издательство: Springer Рейтинг: Цена: 3912.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Eliminate the guesswork involved in writing and deploying a cloud application. This step-by-step guide uses PHP to minimize the complexity of the code and setup, but the tools and techniques can be applied on any platform using any language. Everything that you need to jumpstart your application on the cloud is right here.
Clear diagrams, step-by-step configuration information, and complete code listings tell you everything you need to get off the ground and start developing your cloud application today. This book introduces several cloud architectures and technologies that will help you accelerate your application in the cloud. Chapters cover load-balanced clusters, database replication, caching configuration, content delivery networks, infinite-scale file storage, and cloud system administration.
Cloud computing has dramatically changed the landscape of web hosting. Instead of spending weeks negotiating contracts for servers, new servers can be deployed with the push of a button, and your application can be resized almost instantly to meet today's needs. No matter what size of web application you are developing, you can benefit from modern cloud servers, and this is the guide to tell you how.
What You'll Learn
Use the cloud and its various platforms with Docker management toolsBuild a simple PHP-based scalable web applicationCreate a basic cloud clusterWork with Amazon and Google Cloud Platform in your PHP web application development
Who This Book Is For
Developers who have some prior programming experience, including PHP, and who are new to building applications
Описание: PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2016 in the special sessions: Agents Behaviours and Artificial Markets (ABAM); Advances on Demand Response and Renewable Energy Sources in Agent Based Smart Grids (ADRESS); Agents and Mobile Devices (AM); Agent Methodologies for Intelligent Robotics Applications (AMIRA); Learning, Agents and Formal Languages (LAFLang); Multi-Agent Systems and Ambient Intelligence (MASMAI); Web Mining and Recommender systems (WebMiRes). The volume also includes the paper accepted for the Doctoral Consortium in PAAMS 2016 and Collocated Events.
Автор: Peter Mueller; Jiannong Cao; Cho-Li Wang Название: Scalable Information Systems ISBN: 3642104843 ISBN-13(EAN): 9783642104848 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Includes the proceedings of the 4th International ICST Conference, INFOSCALE 2009, held in Hong Kong in June 2009. This title features papers that focus on various scalability issues and the approaches to tackle problems arising from the ever growing size and complexity of information.
Описание: The general problem addressed in this book is a large and important one: how to usefully deal with huge storehouses of complex information about real-world situations.
Автор: Nahla Ben Amor; Benjamin Quost; Martin Theobald Название: Scalable Uncertainty Management ISBN: 3030355136 ISBN-13(EAN): 9783030355135 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 13th International Conference on Scalable Uncertainty Management, SUM 2019, which was held in Compiegne, France, in December 2019. The 25 full, 4 short, 4 tutorial, 2 invited keynote papers presented in this volume were carefully reviewed and selected from 44 submissions.
Автор: Schockaert Название: Scalable Uncertainty Management ISBN: 3319458558 ISBN-13(EAN): 9783319458557 Издательство: Springer Рейтинг: Цена: 8106.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Combinatorial Games: From Theoretical Solving to AI Algorithms.- A Gentle Introduction to Reinforcement Learning.- Possibilistic Graphical Models for Uncertainty Modeling.- On the Explanation of SameAs Statements using Argumentation.- Reasoning with Multiple-Agent Possibilistic Logic.- Incremental Preference Elicitation in Multi-Attribute Domains for Choice and Ranking with the Borda Count.- Graphical Models for Preference Representation: An Overview.- Diffusion of Opinion and Influence.- Fuzzy Labeling for Abstract Argumentation: An Empirical Evaluation.- A Belief-Based Approach to Measuring Message Acceptability.- Intertranslatability of Labeling-based Argumentation Semantics.- Preference Inference Based on Pareto Models.- Persuasion Dialogues via Restricted Interfaces using Probabilistic Argumentation.- Metric Logic Program Explanations for Complex Separator Functions.- A Two-Stage Online Approach for Collaborative Multi-Agent Planning under Uncertainty.- 9-ASP for Computing Repairs with Existential Ontologies.- Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy.- Fuzzy Quantified Structural Queries to Fuzzy Graph Databases.- Reasoning with Data - A New Challenge For AI.- Probabilistic Spatial Reasoning in Constraint Logic Programming.- ChoiceGAPs: Competitive Diffusion as a Massive Multi-Player Game in Social Networks.- Challenges for Efficient Query Evaluation on Structured Probabilistic Data.- Forgetting-Based Inconsistency Measure.- A Possibilistic Multivariate Fuzzy c-Means Clustering Algorithm.- A Measure of Referential Success Based on Alpha-Cuts.- Graded Justification of Arguments via Internal and External Endogenous Features.
Автор: Sergio Greco Название: Scalable Uncertainty Management ISBN: 3540879927 ISBN-13(EAN): 9783540879923 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Constitutes the refereed proceedings of the Second International Conference on Scalable Uncertainty Management, SUM 2008, held in Naples, Italy, in October 2008.
Автор: Seraf?n Moral; Olivier Pivert; Daniel S?nchez; Nic Название: Scalable Uncertainty Management ISBN: 3319675818 ISBN-13(EAN): 9783319675817 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Granada, Spain, in October 2017.
Автор: Pradipta Maji; Sushmita Paul Название: Scalable Pattern Recognition Algorithms ISBN: 3319056298 ISBN-13(EAN): 9783319056296 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models.
Автор: Bahaaldine Azarmi Название: Scalable Big Data Architecture ISBN: 1484213270 ISBN-13(EAN): 9781484213278 Издательство: Springer Рейтинг: Цена: 4186.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This book highlights the different types of data architecture and illustrates the many possibilities hidden behind the term "Big Data", from the usage of No-SQL databases to the deployment of stream analytics architecture, machine learning, and governance.
Scalable Big Data Architecture covers real-world, concrete industry use cases that leverage complex distributed applications, which involve web applications, RESTful API, and high throughput of large amount of data stored in highly scalable No-SQL data stores such as Couchbase and Elasticsearch. This book demonstrates how data processing can be done at scale from the usage of NoSQL datastores to the combination of Big Data distribution.
When the data processing is too complex and involves different processing topology like long running jobs, stream processing, multiple data sources correlation, and machine learning, it's often necessary to delegate the load to Hadoop or Spark and use the No-SQL to serve processed data in real time.
This book shows you how to choose a relevant combination of big data technologies available within the Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every pattern is illustrated with practical examples, which use the different open sourceprojects such as Logstash, Spark, Kafka, and so on.
Traditional data infrastructures are built for digesting and rendering data synthesis and analytics from large amount of data. This book helps you to understand why you should consider using machine learning algorithms early on in the project, before being overwhelmed by constraints imposed by dealing with the high throughput of Big data.
Scalable Big Data Architecture is for developers, data architects, and data scientists looking for a better understanding of how to choose the most relevant pattern for a Big Data project and which tools to integrate into that pattern.
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