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Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs, Sikos Leslie F., Seneviratne Oshani W., McGuinness Deborah L.


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Автор: Sikos Leslie F., Seneviratne Oshani W., McGuinness Deborah L.
Название:  Provenance in Data Science: From Data Models to Context-Aware Knowledge Graphs
ISBN: 9783030676803
Издательство: Springer
Классификация:




ISBN-10: 3030676803
Обложка/Формат: Hardcover
Страницы: 110
Вес: 0.34 кг.
Дата издания: 06.06.2021
Серия: Advanced information and knowledge processing
Язык: English
Издание: 1st ed. 2021
Иллюстрации: 24 illustrations, black and white; viii, 176 p. 24 illus.
Размер: 23.39 x 15.60 x 0.79 cm
Читательская аудитория: Professional & vocational
Подзаголовок: From data models to context-aware knowledge graphs
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: The Evolution of Context-Aware RDF Knowledge Graphs.- Data Provenance and Accountability on the Web.- The Right (Provenance) Hammer for the Job: a Comparison of Data Provenance Instrumentation.- Contextualized Knowledge Graphs in Communication Network and Cyber-Physical System Modeling.- ProvCaRe: A Large-Scale Semantic Provenance Resource for Scientific Reproducibility.- Graph-Based Natural Language Processing for the Pharmaceutical Industry.


Context-Aware Systems and Applications

Автор: Phan Cong Vinh; Le Tuan Anh; Nguyen Thi Thuy Loan;
Название: Context-Aware Systems and Applications
ISBN: 3319563564 ISBN-13(EAN): 9783319563565
Издательство: Springer
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Цена: 6988.00 р.
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Описание: This book constitutes the proceedings of the 5th International Conference on Context-Aware Systems and Applications, ICCASA 2016, held in Thu Dau Mot, Vietnam, in November 2016. The 22 revised full papers presented were carefully selected from 35 submissions and cover a wide spectrum in the area of Context-Aware-Systems (CAS).

Privacy-Aware Knowledge Discovery

Название: Privacy-Aware Knowledge Discovery
ISBN: 1138374105 ISBN-13(EAN): 9781138374102
Издательство: Taylor&Francis
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Цена: 9492.00 р.
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Описание:

Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results--they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development.

Divided into seven parts, the book provides in-depth coverage of the most novel reference scenarios for privacy-preserving techniques. The first part gives general techniques that can be applied to various applications discussed in the rest of the book. The second section focuses on the sanitization of network traces and privacy in data stream mining. After the third part on privacy in spatio-temporal data mining and mobility data analysis, the book examines time series analysis in the fourth section, explaining how a perturbation method and a segment-based method can tackle privacy issues of time series data. The fifth section on biomedical data addresses genomic data as well as the problem of privacy-aware information sharing of health data. In the sixth section on web applications, the book deals with query log mining and web recommender systems. The final part on social networks analyzes privacy issues related to the management of social network data under different perspectives.

While several new results have recently occurred in the privacy, database, and data mining research communities, a uniform presentation of up-to-date techniques and applications is lacking. Filling this void, Privacy-Aware Knowledge Discovery presents novel algorithms, patterns, and models, along with a significant collection of open problems for future investigation.

Context-Aware Collaborative Prediction

Автор: Shu Wu; Qiang Liu; Liang Wang; Tieniu Tan
Название: Context-Aware Collaborative Prediction
ISBN: 9811053723 ISBN-13(EAN): 9789811053726
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book presents two collaborative prediction approaches based on contextual representation and hierarchical representation, and their applications including context-aware recommendation, latent collaborative retrieval and click-through rate prediction.

Context-Aware Systems and Applications, and Nature of Computation and Communication

Автор: Cong Vinh
Название: Context-Aware Systems and Applications, and Nature of Computation and Communication
ISBN: 331977817X ISBN-13(EAN): 9783319778174
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This book constitutes the refereed post-conference proceedings of the International Conferences ICCASA and ICTCC 2017, held in November 2017 in Tam Ky City, Vietnam. The 23 revised full papers presented were carefully selected from 31 submissions.

Network-Aware Security for Group Communications

Автор: Yan Sun; Wade Trappe; K. J. Ray Liu
Название: Network-Aware Security for Group Communications
ISBN: 1441943358 ISBN-13(EAN): 9781441943354
Издательство: Springer
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Цена: 16977.00 р.
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Описание: It focuses on tailoring the security solution to the underlying network architecture (such as the wireless cellular network or the ad hoc/sensor network), or to the application using the security methods (such as multimedia multicasts).

Self-Aware Computing Systems

Автор: Samuel Kounev; Jeffrey O. Kephart; Aleksandar Mile
Название: Self-Aware Computing Systems
ISBN: 3319474723 ISBN-13(EAN): 9783319474724
Издательство: Springer
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Цена: 23757.00 р.
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Описание: This book provides formal and informal definitions and taxonomies for self-aware computing systems, and explains how self-aware computing relates to many existing subfields of computer science, especially software engineering.

Process-Aware Systems

Автор: Jian Cao; Xiao Liu; Kaijun Ren
Название: Process-Aware Systems
ISBN: 9811010188 ISBN-13(EAN): 9789811010187
Издательство: Springer
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Цена: 6988.00 р.
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Описание: This book constitutes the refereed proceedings of the Second International Workshop on Process-Aware Systems, PAS 2015, held in Hangzhou, China, in October 2015. The four revised full papers and two short papers, presented together with five demo papers were carefully reviewed and selected from 16 submissions.

The Ethical Algorithm: The Science of Socially Aware Algorithm Design

Автор: Kearns Michael, Roth Aaron
Название: The Ethical Algorithm: The Science of Socially Aware Algorithm Design
ISBN: 0190948205 ISBN-13(EAN): 9780190948207
Издательство: Oxford Academ
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Цена: 3721.00 р.
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Описание: Algorithms have made our lives more efficient and entertaining-but not without a significant cost. Can we design a better future, one in which societial gains brought about by technology are balanced with the rights of citizens? The Ethical Algorithm offers a set of principled solutions based on the emerging and exciting science of socially aware algorithm design.

A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources

Автор: Tuan Tran Nguyen
Название: A Reliability-Aware Fusion Concept Toward Robust Ego-Lane Estimation Incorporating Multiple Sources
ISBN: 3658269480 ISBN-13(EAN): 9783658269487
Издательство: Springer
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Цена: 5589.00 р.
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Описание: To tackle the challenges of the road estimation task, many works employ a fusion of multiple sources. By that, a commonly made assumption is that the sources always are equally reliable. However, this assumption is inappropriate since each source has certain advantages and drawbacks depending on the operational scenarios. Therefore, Tuan Tran Nguyen proposes a novel concept by incorporating reliabilities into the multi-source fusion so that the road estimation task can alternately select only the most reliable sources. Thereby, the author estimates the reliability for each source online using classifiers trained with the sensor measurements, the past performance and the context. Using real data recordings, he shows via experimental results that the presented reliability-aware fusion increases the availability of automated driving up to 7 percentage points compared to the average fusion.

About the Author:Tuan Tran Nguyen received the Master's degree in computer science and the Ph.D. degree from Otto-von-Guericke University Magdeburg, Germany, in 2013 and 2019, respectively. His research focuses on methods and architectures for reliability-based sensor fusion in intelligent vehicles.
Exploiting Linked Data and Knowledge Graphs for Large Organisations

Автор: Jeff Z. Pan; Guido Vetere; Jose Manuel Gomez-Perez
Название: Exploiting Linked Data and Knowledge Graphs for Large Organisations
ISBN: 3319456520 ISBN-13(EAN): 9783319456522
Издательство: Springer
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Цена: 18167.00 р.
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Описание: This book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and "standard" data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps.

It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.

Exploiting Linked Data and Knowledge Graphs in Large Organisations

Автор: Pan Jeff Z., Vetere Guido, Gomez-Perez Jose Manuel
Название: Exploiting Linked Data and Knowledge Graphs in Large Organisations
ISBN: 3319833391 ISBN-13(EAN): 9783319833392
Издательство: Springer
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Цена: 23757.00 р.
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Описание:

Part I Knowledge Graph Foundations & Architecture.- Part II Constructing, Understanding and Consuming Knowledge Graphs.- Part III Industrial Applications and Successful Stories.


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