To navigate the complex ecosystem of societal challenges, the International Conference on Knowledge Management Conference (ICKM 2017) focused on big data and data analytics as part of the relationship to the wider concept of knowledge management processes and practices.This book includes top papers presenting the major, and diverse, topics discussed at the conference. The papers covered various aspects of big data ranging from enhancing access to the big data to facilitating its wide applications in healthcare, social media, library and information centers, governments, and corporations.
"To navigate the complex ecosystem of societal challenges, the International Conference on Knowledge Management Conference (ICKM 2017) focused on big data and data analytics as part of the relationship to the wider concept of knowledge management processes and practices. This book includes top papers presenting the major, and diverse, topics discussed at the conference. The papers covered various aspects of big data ranging from enhancing access to the big data to facilitating its wide applications in healthcare, social media, library and information centers, governments, and corporations."--Publisher's website.
With the dawn of electronic databases, information technologies, and the Internet, organizations, now more than ever, have easy access to all the knowledge they need to conduct their business. However, utilizing and detecting the beneficial information can pose as a challenge. Enhancing Knowledge Discovery and Innovation in the Digital Era is a vibrant reference source on the latest research on student education, open information, technology enhanced learning (TEL), and student outcomes. Featuring widespread coverage across a range of applicable perspectives and topics, such as engineering education, data mining, and 3D printing, this book is ideally designed for professionals, upper-level students, and academics seeking current research on knowledge management and innovation networks.
Recently, researchers have focused on challenging problems facing the development of data warehousing, knowledge discovery, and data mining applications.
The fast-paced world created by the accessibility of consumer information through internet-generated data requires improved information-management platforms. The continuous evaluation and evolution of these systems facilitate enhanced data reference and output. Optimizing Data and New Methods for Efficient Knowledge Discovery and Information Resources Management is a critical research publication that provides insight into the varied and rapidly changing fields of knowledge discovery and information resource management. Highlighting a range of topics such as datamining, artificial intelligence, and risk assessment, this book is essential for librarians, academicians, policymakers, information managers, professionals, and researchers in fields that include artificial intelligence, knowledge discovery, data visualization, big data, and information resources management.
Data Warehousing and Knowledge Discovery have been widely accepted as key te- nologies for enterprises and organizations as a means of improving their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. With the exponentially growing amount of information to be included in the decision making process, the data to be processed is becoming more and more complex in both structure and semantics. Consequently, the process of retrieval and knowledge disc- ery from this huge amount of heterogeneous complex data constitutes the reality check for research in the area. During the past few years, the International Conference on Data Warehousing and Knowledge Discovery (DaWaK) has become one of the most important international scientific events to bring together researchers, developers and practitioners. The DaWaK conferences serve as a prominent forum for discussing the latest research issues and experiences in developing and deploying data warehousing and knowledge discovery systems, applications, and solutions. This year’s conference, the 10th Int- national Conference on Data Warehousing and Knowledge Discovery (DaWaK 2008), continued the tradition of facilitating the cross-disciplinary exchange of ideas, expe- ence and potential research directions. DaWaK 2008 sought to disseminate innovative principles, methods, algorithms and solutions to challenging problems faced in the development of data warehousing, knowledge discovery and data mining applications.
This book addresses the usefulness of knowledge discovery through data mining. With this aim, contributors from different fields propose concrete problems and applications showing how data mining and discovering embedded knowledge from raw data can be beneficial to social organizations, domestic spheres, and ICT markets. Data mining or knowledge discovery in databases (KDD) has received increasing interest due to its focus on transforming large amounts of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships. The concept of knowledge is broad and speculative and has promoted epistemological debates in western philosophies. The intensified interest in knowledge management and data mining stems from the difficulty in identifying computational models able to approximate human behaviors and abilities in resolving organizational, social, and physical problems. Current ICT interfaces are not yet adequately advanced to support and simulate the abilities of physicians, teachers, assistants or housekeepers in domestic spheres. And unlike in industrial contexts where abilities are routinely applied, the domestic world is continuously changing and unpredictable. There are challenging questions in this field: Can knowledge locked in conventions, rules of conduct, common sense, ethics, emotions, laws, cultures, and experiences be mined from data? Is it acceptable for automatic systems displaying emotional behaviors to govern complex interactions based solely on the mining of large volumes of data? Discussing multidisciplinary themes, the book proposes computational models able to approximate, to a certain degree, human behaviors and abilities in resolving organizational, social, and physical problems. The innovations presented are of primary importance for: a. The academic research community b. The ICT market c. Ph.D. students and early stage researchers d. Schools, hospitals, rehabilitation and assisted-living centers e. Representatives from multimedia industries and standardization bodies
New techniques and tools for database and database technologies are continuously being introduced. These technologies are the heart of many business information systems and can benefit from theories, models, and research results from other disciplines. Innovations in Database Design, Web Applications, and Information Systems Management presents ideal research in the areas of database theory, systems design, ontologies, and many more. Including examples of the convergence of ideas from various disciplines aimed at improving and developing the theory of information technology and management of information resources, this book is useful for researchers and practitioners in the IT field.
With the dawn of electronic databases, information technologies, and the Internet, organizations, now more than ever, have easy access to all the knowledge they need to conduct their business. However, utilizing and detecting the beneficial information can pose as a challenge. Enhancing Knowledge Discovery and Innovation in the Digital Era is a vibrant reference source on the latest research on student education, open information, technology enhanced learning (TEL), and student outcomes. Featuring widespread coverage across a range of applicable perspectives and topics, such as engineering education, data mining, and 3D printing, this book is ideally designed for professionals, upper-level students, and academics seeking current research on knowledge management and innovation networks.
This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.
