Resource-Oriented Architecture Patterns for Webs of Data, Brian Sletten

Автор: Kristin J. Dana
Название: Computational Texture and Patterns: From Textons to Deep Learning
ISBN: 1681730111 ISBN-13(EAN): 9781681730110
Издательство: Mare Nostrum (Eurospan)
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Цена: 51750.00 T
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Описание: Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance—to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.

Автор: Lawler, James P. , Howell-Barber, H.
Название: Service-Oriented Architecture
ISBN: 0367388235 ISBN-13(EAN): 9780367388232
Издательство: Taylor&Francis
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Цена: 60220.00 T
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Описание: Aggressively being adopted by organizations in all markets, service-oriented architecture (SOA) is a framework enabling business process improvement for gaining competitive advantage. Service-Oriented Architecture: SOA Strategy, Methodology, and Technology guides you through the challenges of deploying SOA. It demonstrates conclusively that strategy and methodology are the keys to implementing SOA and provides the methodology needed for SOA success.

The book examines the role of both non-agile and agile project management techniques for deploying SOA. Its methodology applies frameworks of governance, communications, product realization, project management, architecture, data management, service management, human resource management and post implementation processes. Filled with case studies, the book shows the methodology in action.

This reference benefits business managers, business analysts, and technology project managers who are serious about adopting SOA as a long-term strategy. It is also benefits those new to business process management, enterprise architecture, and information systems and need to understand SOA, its business drivers, and its methodology.

Автор: Paolo Arena; Luca Patan?
Название: Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II
ISBN: 3319346954 ISBN-13(EAN): 9783319346953
Издательство: Springer
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Цена: 102480.00 T
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Описание: This book presents the result of a joint effort from different European Institutions within the framework of the EU funded project called SPARK II, devoted to device an insect brain computational model, useful to be embedded into autonomous robotic agents. Part I reports the biological background on Drosophila melanogaster with particular attention to the main centers which are used as building blocks for the implementation of the insect brain computational model. Part II reports the mathematical approach to model the Central Pattern Generator used for the gait generation in a six-legged robot. Also the Reaction-diffusion principles in non-linear lattices are exploited to develop a compact internal representation of a dynamically changing environment for behavioral planning. In Part III a software/hardware framework, developed to integrate the insect brain computational model in a simulated/real robotic platform, is illustrated. The different robots used for the experiments are also described. Moreover the problems related to the vision system were addressed proposing robust solutions for object identification and feature extraction. Part IV includes the relevant scenarios used in the experiments to test the capabilities of the insect brain-inspired architecture taking as comparison the biological case. Experimental results are finally reported, whose multimedia can be found in the SPARK II web page: www.spark2.diees.unict.it

Автор: Paolo Arena; Luca Patan?
Название: Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots
ISBN: 3642100147 ISBN-13(EAN): 9783642100147
Издательство: Springer
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Цена: 156720.00 T
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Описание: This volume collects significant results of the research Project called "SPARK". Its aim was to develop new cognitive architectures and sensing-perceiving-moving artefacts, inspired by the basic principles of living systems and based on "self-organization".

Автор: Paolo Arena; Luca Patan?
Название: Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II
ISBN: 3319023616 ISBN-13(EAN): 9783319023618
Издательство: Springer
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Цена: 139310.00 T
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Описание: Describing a cutting-edge project that used a computational model of an insect brain to enable spatial awareness in mobile robots, this volume shows how today`s scientists are blending biologically inspired networks and complex, nonlinear dynamical systems.

Автор: Osmar R. Zaiane; Jaideep Srivastava; Myra Spiliopo
Название: WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles
ISBN: 3540203044 ISBN-13(EAN): 9783540203049
Издательство: Springer
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Цена: 55890.00 T
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Описание: This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Mining Web Data, WEBKDD 2002, held in Edmonton, Canada, in July 2002.The 10 revised full papers presented together with a detailed preface went through two rounds of reviewing and improvement and were selected from 23 submissions.

Автор: Carol Jean Godby, Shenghui Wang, Jeffrey K. Mixter
Название: Library Linked Data in the Cloud: OCLC`s Experiments with New Models of Resource Description
ISBN: 1627052194 ISBN-13(EAN): 9781627052191
Издательство: Mare Nostrum (Eurospan)
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Цена: 51750.00 T
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Описание: This book focuses on the conceptual and technical challenges involved in publishing linked data derived from traditional library metadata. This transformation is a high priority because most searches for information start not in the library, nor even in a Web-accessible library catalogue, but elsewhere on the Internet.

Автор: Dong Guozhu
Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems
ISBN: 1681735024 ISBN-13(EAN): 9781681735023
Издательство: Mare Nostrum (Eurospan)
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Цена: 57290.00 T
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Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.

Автор: Dong Guozhu
Название: Exploiting the Power of Group Differences: Using Patterns to Solve Data Analysis Problems
ISBN: 1681735040 ISBN-13(EAN): 9781681735047
Издательство: Mare Nostrum (Eurospan)
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Цена: 77610.00 T
Наличие на складе: Невозможна поставка.
Описание: This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.