This book constitutes the thoroughly refereed proceedings of the 16th Italian Research Conference on Digital Libraries, IRCDL 2020, held in Bari, Italy, in January 2020. The 12 full papers and 6 short papers presented were carefully selected from 26 submissions. The papers are organized in topical sections on information retrieval, bid data and data science in DL; cultural heritage; open science.
Apply predictive analytics throughout all stages of workforce management People Analytics in the Era of Big Data provides a blueprint for leveraging your talent pool through the use of data analytics. Written by the Global Vice President of Business Intelligence and Predictive Analytics at Monster Worldwide, this book is packed full of actionable insights to help you source, recruit, acquire, engage, retain, promote, and manage the exceptional talent your organization needs. With a unique approach that applies analytics to every stage of the hiring process and the entire workforce planning and management cycle, this informative guide provides the key perspective that brings analytics into HR in a truly useful way. You're already inundated with disparate employee data, so why not mine that data for insights that add value to your organization and strengthen your workforce? This book presents a practical framework for real-world talent analytics, backed by groundbreaking examples of workforce analytics in action across the U.S., Canada, Europe, Asia, and Australia. Leverage predictive analytics throughout the hiring process Utilize analytics techniques for more effective workforce management Learn how people analytics benefits organizations of all sizes in various industries Integrate analytics into HR practices seamlessly and thoroughly Corporate executives need fact-based insights into what will happen with their talent. Who should you hire? Who should you promote? Who are the top or bottom performers, and why? Who is at risk to quit, and why? Analytics can provide these answers, and give you insights based on quantifiable data instead of gut feeling and subjective assessment. People Analytics in the Era of Big Data is the essential guide to optimizing your workforce with the tools already at your disposal.
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Quality and the Academic Library: Reviewing, Assessing and Enhancing Service Provision provides an in-depth review and analysis of quality management and service quality in academic libraries. All aspects of quality are considered in the book, including quality assessment, quality review, and quality enhancement. An overview of quality management and service quality concepts, principles, and methods leads to a detailed consideration of how they have been applied in universities and their libraries. A case study approach is used with different perspectives provided from the different stakeholders involved in the quality processes. All contributors adopt a critical reflection approach, reflecting on the implications, impact, and significance of the activities undertaken and the conclusions that can be drawn for future developments. The book concludes with an overall reflection on quality management and service quality in academic libraries with a final analysis of priorities for the future. Presents a holistic view of the subject, looking at reviews of academic library services, quality assurance and assessment, quality enhancement, and service quality Provides perspectives from authors with different experiences and responsibilities, including those responsible for initiating and managing quality processes in higher education Includes case studies where the authors not only describe the quality processes used, but also seek to review and reflect on their success, limitations, and the impact of their work some time after the event Seeks to be current, comprehensive, and reflective by including the results of surveys/interviews from senior librarians on quality in academic libraries
The main purpose of this book is to investigate, explore and describe approaches and methods to facilitate data understanding through analytics solutions based on its principles, concepts and applications. But analyzing data is also about involving the use of software. For this, and in order to cover some aspect of data analytics, this book uses software (Excel, SPSS, Python, etc) which can help readers to better understand the analytics process in simple terms and supporting useful methods in its application.
Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
The information age; Technological capabilities and prospects; Some applications of technology; Computers and publishing; A paperless communication system; Libraries and technology; The migration from print on paper; The future of the library: some forecasts; The disembodiment of the library; Does the library have a future?
“Big data” has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. Effective Big Data Management and Opportunities for Implementation explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data.
Information and Knowledge Organisation explores the role of knowledge organisation in the digital humanities. By focusing on how information is described, represented and organised in both research and practice, this work furthers the transdisciplinary nature of digital humanities. Including contributions from Asia, Australia, Europe, North America and the Middle East, the volume explores the potential uses of, and challenges involved in, applying the organisation of information and knowledge in the various areas of Digital Humanities. With a particular focus on the digital worlds of cultural heritage collections, the book also includes chapters that focus on machine learning, knowledge graphs, text analysis, text annotations and network analysis. Other topics covered include: semantic technologies, conceptual schemas and data augmentation, digital scholarly editing, metadata creation, browsing, visualisation and relevance ranking. Most importantly, perhaps, the book provides a starting point for discussions about the impact of information and knowledge organisation and related tools on the methodologies used in the Digital Humanities field. Information and Knowledge Organisation is intended for use by researchers, students and professionals interested in the role information and knowledge organisation plays in the Digital Humanities. It will be essential reading for those working in library and information science, computer science and across the humanities. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
