Big-Data Analytics and Cloud Computing : Theory, Algorithms and Applications / / edited by Marcello Trovati, Richard Hill, Ashiq Anjum, Shao Ying Zhu, Lu Liu

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
• Edizione: 1st ed. 2015.
• Descrizione fisica: 1 online resource (178 p.)
• Disciplina: 004
• Soggetto topico: Mathematical statistics; Computer communication systems; Computer simulation; Computer science—Mathematics; Probability and Statistics in Computer Science; Computer Communication Networks; Simulation and Modeling; Math Applications in Computer Science
• Persona (resp. second.): TrovatiMarcello; HillRichard; AnjumAshiq; ZhuShao Ying; LiuLu
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Foreword; Preface; Overview and Goals; Organisation and Features; Target Audiences; Suggested Uses; Acknowledgements; Contents; Contributors; Part I Theory; 1 Data Quality Monitoring of Cloud Databases Based on Data Quality SLAs; 1.1 Introduction and Summary; 1.2 Background; 1.2.1 Data Quality in the Context of Big Data; 1.2.2 Cloud Computing; 1.2.3 Data Quality Monitoring in the Cloud; 1.2.4 The Challenge of Specifying a DQSLA; 1.2.5 The Infrastructure Estimation Problem; 1.3 Proposed Solutions; 1.3.1 Data Quality SLA Formalization; 1.3.2 Examples of Data Quality SLAs ; 1.3.3 Data Quality-Aware Service Architecture1.4 Future Research Directions; 1.5 Conclusions; References; 2 Role and Importance of Semantic Search in Big Data Governance; 2.1 Introduction; 2.2 Big Data: Promises and Challenges; 2.3 Participatory Design for Big Data; 2.4 Self-Service Discovery; 2.5 Conclusion; References; 3 Multimedia Big Data: Content Analysis and Retrieval; 3.1 Introduction; 3.2 The MapReduce Framework and Multimedia Big Data; 3.2.1 Indexing; 3.2.2 Caveats on Indexing; 3.2.3 Multiple Multimedia Processing; 3.2.4 Additional Work Required? ; 5 Integrating Twitter Traffic Information with Kalman Filter Models for Public Transportation Vehicle Arrival Time Prediction5.1 Introduction; 5.2 Communication Platform on Twitter; 5.3 Communication for Data Collection on Twitter; 5.4 Event Detection and Analysis: Tweets Relating to Road Incidents; 5.4.1 Twitter Data: Incident Data Set; 5.5 Methodology; 5.5.1 Time Series and Temporal Analysis of Textual Twitter; 5.6 Proposed Refined Kalman Filter (KF) Model-Based System; 5.7 Conclusion; References; 6 Data Science and Big Data Analytics at Career Builder ; 6.1 Carotene: A Job Title Classification System6.1.1 Occupation Taxonomies; 6.1.2 The Architecture of Carotene; 6.1.2.1 Taxonomy Discovery Using Clustering; 6.1.2.2 Coarse-Level Classification: SOC Major Classifier; 6.1.2.3 Fine-Level Classification: Proximity-Based Classifier; 6.1.3 Experimental Results and Discussion; 6.2 CARBi: A Data Science Ecosystem; 6.2.1 Accessing CB Data and Services Using WebScalding; 6.2.2 ScriptDB: Managing Hadoop Jobs; References; 7 Extraction of Bayesian Networks from Large Unstructured Datasets; 7.1 Introduction; 7.2 Text Mining; 7.2.1 Text Mining Techniques ; 7.2.2 General Architecture and Various Components of Text Mining
• Sommario/riassunto: This important and timely text/reference reviews the theoretical concepts, leading-edge techniques and practical tools involved in the latest multi-disciplinary approaches addressing the challenges of big data. Illuminating perspectives from both academia and industry are presented by an international selection of experts in big data science. Topics and features: Describes the innovative advances in theoretical aspects of big data, predictive analytics and cloud-based architectures Examines the applications and implementations that utilize big data in cloud architectures Surveys the state of the art in architectural approaches to the provision of cloud-based big data analytics functions Identifies potential research directions and technologies to facilitate the realization of emerging business models through big data approaches Provides relevant theoretical frameworks, empirical research findings, and numerous case studies Discusses real-world applications of algorithms and techniques to address the challenges of big datasets This authoritative volume will be of great interest to researchers, enterprise architects, business analysts, IT infrastructure managers and application developers, who will benefit from the valuable insights offered into the adoption of architectures for big data and cloud computing. The work is also suitable as a textbook for university instructors, with the outline for a possible course structure suggested in the preface. The editors are all members of the Computing and Mathematics Department at the University of Derby, UK, where Dr. Marcello Trovati serves as a Senior Lecturer in Mathematics, Dr. Richard Hillas a Professor and Head of the Computing and Mathematics Department, Dr. Ashiq Anjum as a Professor of Distributed Computing, Dr. Shao Ying Zhu as a Senior Lecturer in Computing, and Dr. Lu Liu as a Professor of Distributed Computing. The other publications of the editors include the Springer titles Guide to Security Assurance for Cloud Computing, Guide to Cloud Computing and Cloud Computing for Enterprise Architectures.
• Titolo autorizzato: Big-Data Analytics and Cloud Computing
• ISBN: 3-319-25313-1
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910298964403321
• Lo trovi qui: Univ. Federico II