Bøger i Advances in Data Mining and Database Management serien

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 fuelled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students.

As mobile technologies become ever more pervasive in modern society, users find increasingly innovative methods to take advantage of the newest developments and mobile devices. This title concentrates on one particular and fast-growing application of mobile technologies: data acquisition for the tourism industry.

Decision making tools are essential for the successful outcome of any organisation. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made. This provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics and information technology for effective decision making.

Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems.

Focuses on database query processing methods, technologies, and applications. Aimed at providing an all-inclusive reference source of technologies and practices in advanced database query systems, this book investigates various techniques, including database and XML queries, spatiotemporal data queries, big data queries, metadata queries, and applications of database query systems.

Provides a comprehensive overview of the most innovative recent approaches borne from intelligent techniques such as neural networks, rough sets, fuzzy sets, and metaheuristics. Combining new data analysis technologies, applications, emerging trends, and case studies, this publication reviews the intelligent, technological, and organisational aspects of the field.

Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. This volume evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices.

Presents the latest scholarly research on the benefits and challenges presented by building composite indicators, and how these techniques promote optimized critical thinking. Highlighting various indicator types and quantitative methods, this book is designed for developers, researchers, public officials, and upper-level students.

Identifies methods for extraction and analysis of useful information from web documents. Featuring extensive coverage across a range of relevant perspectives and topics, such as semantic web, machine learning, and expert systems, this book is designed for web developers, internet users, online application developers, researchers, and faculty.

Presents scientific concepts and frameworks of data mining and analytics implementation and uses across various domains, such as public safety, criminal investigations, intrusion detection, crime scene analysis, and suspect modelling. It explores the diverse ways that data is revolutionizing the field of criminal science.

Explores various social networking platforms and the technologies being utilized to gather and analyse information being posted to these venues. Highlighting emergent research, analytical techniques, and best practices in data extraction in global electronic culture, this publication is an essential reference source for researchers, academics, and professionals.

Highlights methodological developments in the way that data analytics is both learned and taught. Featuring extensive coverage on emerging relevant topics such as data complexity, statistics education, and curriculum development, this publication is geared toward teachers, academics, students, engineers, professionals, and researchers.

Brings together research on the latest trends and patterns of data mining tools and techniques in dynamic social networks and fuzzy systems. With these improved modern techniques of data mining, this publication aims to provide insight and support to researchers and professionals concerned with the management of expertise, knowledge, information, and organisational development.

Explores the manifestation of data mining and how it can be enhanced at various levels of management. This publication provides theoretical frameworks and the empirical research findings.

Useful to healthcare providers, severity indices conclude which patients are most at risk for infection as well as the intensity of illness while in the hospital. This book discusses the general practice of defining a patient severity index for risk adjustments and comparison of patient outcomes to assess quality factors.

Examines a number of emerging technologies that significantly contribute to modern Information Retrieval (IR), as well as fundamental IR theories and concepts that have been adopted into new tools or systems. This reference is essential to researchers, educators, professionals, and students interested in the future of IR.

Features the latest empirical research on the business use of computing devices to send and receive data in conjunction with analytic applications to reduce maintenance costs, avoid equipment failures, and improve business operations. This volume includes research on a broad range of topics such as supply chain, aquaculture, and speech recognition systems.

Graphs are a powerful tool for representing and understanding objects and their relationships in various application domains. The growing popularity of graph databases has generated data management problems that include finding efficient techniques for compressing large graph databases and suitable techniques for visualizing, browsing, and navigating large graph databases. Graph Data Management: Techniques and Applications is a central reference source for different data management techniques for graph data structures and their application. This book discusses graphs for modeling complex structured and schemaless data from the Semantic Web, social networks, protein networks, chemical compounds, and multimedia databases and offers essential research for academics working in the interdisciplinary domains of databases, data mining, and multimedia technology.

The widespread use of XML in business and scientific databases has prompted the development of methodologies, techniques, and systems for effectively managing and analyzing XML data. This has increasingly attracted the attention of different research communities, including database, information retrieval, pattern recognition, and machine learning, from which several proposals have been offered to address problems in XML data management and knowledge discovery. XML Data Mining: Models, Methods, and Applications aims to collect knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods, and systems for XML data mining. This book addresses key issues and challenges in XML data mining, offering insights into the various existing solutions and best practices for modeling, processing, analyzing XML data, and for evaluating performance of XML data mining algorithms and systems.

Introduces recent research activities in the field of data mining. It covers association mining, classification, mobile marketing, opinion mining, microarray data mining, internet mining and applications of data mining on biological data, telecommunication and distributed databases, among others, while promoting understanding and implementation of data mining techniques in emerging domains.

Presents the latest academic research on embedding and associating semantics with multimedia information to improve data retrieval techniques. Highlighting a range of pertinent topics such as automation, knowledge discovery, and social networking, this book is designed for researchers, practitioners, students, and professionals interested in emerging trends in information retrieval.