Описание: 1. Data Center for Smart Issues: Energy and Sustainability Issue.- 2 Apache Spark for Digitalization, Analysis and Optimization of Discrete Manufacturing Process.- 3. An Empirica Study on Teleworking among Slovakia's Office-Based Academics.- 4. DSS for Pro-Active Flood Management of Water Reservoir Systems.- 5. exhiSTORY: Small Self-Organizing Exhibits.- 6. IoT Cloud Design Patterns.- 7. Cloud-based mHealth Streaming for IoT Processing.- 8. A System for Monitoring Water Quality Parameters in Rivers: Challenges and Solutions. 9. A Survey on Privacy Enhancements for Massively Scalable Storage Systems in Public Cloud Environments.- 10. Online Real-time Monitoring and Processing of Medical Parameters: A Cloud-based Solution.- 11. Cloud-Enabled Modeling of Sensor Networks in Educational Settings.- 12. Methods and Techniques for Automatic Identification System Data Reduction.- 13. Energy Efficiency of Arduino Sensors Platform based on Mobile-Cloud: A Bicycle Lights Use-Case.- 14. Process Control Using Cloud Computing Architectures.
Описание: The LNCS journal Transactions on Large-scale Data and Knowledge-centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science.
Описание: This book constitutes refereed proceedings of the First International First International Conference on Big Data, Machine Learning, and Applications, BigDML 2019, held in Silchar, India, in December. The 6 full papers and 3 short papers were carefully reviewed and selected from 152 submissions.
Автор: Kaya Название: Social Network Based Big Data Analysis and Applications ISBN: 3319781952 ISBN-13(EAN): 9783319781952 Издательство: Springer Рейтинг: Цена: 10976.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a timely collection of chapters that present the state of the art within the analysis and application of big data.
Автор: Abu-Salih Bilal, Wongthongtham Pornpit, Zhu Dengya Название: Social Big Data Analytics: Practices, Techniques, and Applications ISBN: 9813366516 ISBN-13(EAN): 9789813366510 Издательство: Springer Цена: 17074.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Chapter 1: Big data technologies
Big data is no more "all just hype" but widely applied in nearly all aspects of our business, governments, and organizations with the technology stack of AI. Its influences are far beyond a simple technique innovation but involves all rears in the world. This chapter will first have historical review of big data; followed by discussion of characteristics of big data, i.e. the 3V's to up 10V's of big data. The chapter then introduces technology stacks for an organization to build a big data application, from infrastructure/platform/ecosystem to constructional units/components; following by several successful examples. Finally, we provide some big data online resources for reference.
Chapter 2: Credibility and influence in social big data
Online Social Networks (OSNs) are a fertile medium through which users can express their sentiments and share their opinions, experiences and knowledge of several topics. There is a deficiency of assessment mechanisms that incorporate domain-based trustworthiness. In OSNs, determining users' influence in a particular domain has been driven by its significance in a broad range of applications such as personalized recommendation systems, opinion analysis, expertise retrieval, to name a few. This chapter presents a comprehensive framework that aims to infer value from BSD by measuring the domain-based trustworthiness of OSN users, addressing the main features of big data, and incorporating semantic analysis and the temporal factor.
Chapter 3: Semantic data discovery from social big data
The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academia and industry. Social big data is an important big data island; thus, social data analytics are intended to make sense of data and to obtain value from data. Social big data provides a wealth of information that businesses, political governments, organisations, etc. can mine and analyse to exploit value in a variety of areas. This chapter discusses the development of an approach that aims to semantically analyse social content, thus enriching social data with semantic conceptual representation for domain-based discovery.
Chapter 4: Predictive analytics using social big data and machine learning
Previous works in the area of topic distillation and discovery lack an appropriate and applicable technical solution that can handle the complex task of obtaining an accurate interpretation of the contextual social content. This is evident through the inadequacy of these endeavours in addressing the topics of microblogging short messages like tweets, and their inability to classify and predict the messages' actual and precise domains of interest at the user level. Hence, this chapter intends to address this problem by presenting solutions to domain-based classification and prediction of social big data at the user and tweet levels incorporating comprehensive knowledge discovery tools and well-known machine learning algorithms.
Chapter 5: Affective design in the era of big social data
In today's competitive market, product designers not only need to optimize functional qualities when developing a new product, but also they need to optimize the affective qualities of the product. The reason is that products with high affective qualities is more likely to attract more potential consumers to buy. In the past, affective design is generally conducted based on the limited amount of customer survey data which is collected from marketing questionnaires and consumer interviews. Since the data amount is limited, the affective design cannot fully reflect the current or even the recent situation of the marketplaces. Thanks to the advanced computing and web technologies, big data from social media or product reviews in w
Автор: Diana Trandab??; Daniela G?fu Название: Linguistic Linked Open Data ISBN: 3319329413 ISBN-13(EAN): 9783319329413 Издательство: Springer Рейтинг: Цена: 6098.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Ontological modeling of social media data.- Application of social media and linked data methodologies in real-life scenarios.- User proling and assessing the suitability of content from social media.- Extracting and linking content.- Sentiment analysis in social media and linked data.-Social data mining to create structured social media resources.
Автор: Philipp Cimiano; Christian Chiarcos; John P. McCra Название: Linguistic Linked Data ISBN: 3030302245 ISBN-13(EAN): 9783030302245 Издательство: Springer Рейтинг: Цена: 18294.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This is the first monograph on the emerging area of linguistic linked data. Presenting a combination of background information on linguistic linked data and concrete implementation advice, it introduces and discusses the main benefits of applying linked data (LD) principles to the representation and publication of linguistic resources, arguing that LD does not look at a single resource in isolation but seeks to create a large network of resources that can be used together and uniformly, and so making more of the single resource.
The book describes how the LD principles can be applied to modelling language resources. The first part provides the foundation for understanding the remainder of the book, introducing the data models, ontology and query languages used as the basis of the Semantic Web and LD and offering a more detailed overview of the Linguistic Linked Data Cloud. The second part of the book focuses on modelling language resources using LD principles, describing how to model lexical resources using Ontolex-lemon, the lexicon model for ontologies, and how to annotate and address elements of text represented in RDF. It also demonstrates how to model annotations, and how to capture the metadata of language resources. Further, it includes a chapter on representing linguistic categories. In the third part of the book, the authors describe how language resources can be transformed into LD and how links can be inferred and added to the data to increase connectivity and linking between different datasets. They also discuss using LD resources for natural language processing. The last part describes concrete applications of the technologies: representing and linking multilingual wordnets, applications in digital humanities and the discovery of language resources.
Given its scope, the book is relevant for researchers and graduate students interested in topics at the crossroads of natural language processing / computational linguistics and the Semantic Web / linked data. It appeals to Semantic Web experts who are not proficient in applying the Semantic Web and LD principles to linguistic data, as well as to computational linguists who are used to working with lexical and linguistic resources wanting to learn about a new paradigm for modelling, publishing and exploiting linguistic resources.
Описание: This open access book explores the dataspace paradigm as a best-effort approach to data management within data ecosystems.
Автор: Christian Chiarcos; Sebastian Nordhoff; Sebastian Название: Linked Data in Linguistics ISBN: 3642434967 ISBN-13(EAN): 9783642434969 Издательство: Springer Рейтинг: Цена: 11586.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The contributions assembled in this volume illustrate the band-width of applications of the Linked Data paradigm for representative types of language resources. They cover lexical-semantic resources, annotated corpora, typological databases as well as terminology and metadata repositories.
Описание: This book provides a corpus-led analysis of multi-word units (MWUs) in English, specifically fixed pairs of nouns which are linked by a conjunction, such as `mum and dad`, `bride and groom` and `law and order`.
Автор: Edward Curry Название: Real-time Linked Dataspaces ISBN: 3030296644 ISBN-13(EAN): 9783030296643 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This open access book explores the dataspace paradigm as a best-effort approach to data management within data ecosystems.
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