Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios. Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches.

Constant improvements in technological applications have allowed for more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but also ensures that technological progression will continue. Ubiquitous Machine Learning and its Applications is a pivotal reference source for the latest research on the issues and challenges machines face in the new millennium. Featuring extensive coverage on relevant areas such as computational advertising, software engineering, and bioinformatics, this publication is an ideal resource for academicians, graduate students, engineering professionals, and researchers interested in discovering how they can apply these advancements to various disciplines.

The increase in connected devices in the internet of things (IoT) is leading to an exponential increase in the data that an organization is required to manage. To successfully utilize IoT in businesses, big data analytics are necessary in order to efficiently sort through the increased data. The combination of big data and IoT can thus enable new monitoring services and powerful processing of sensory data streams. The Handbook of Research on Big Data and the IoT is a pivotal reference source that provides vital research on emerging trends and recent innovative applications of big data and IoT, challenges facing organizations and the implications of these technologies on society, and best practices for their implementation. While highlighting topics such as bootstrapping, data fusion, and graph mining, this publication is ideally designed for IT specialists, managers, policymakers, analysts, software engineers, academicians, and researchers.

The current trend of learner centeredness in education has been challenging many of the current ways of working, especially in higher education institutions. This rapid change in educational institutions demands educators acquire new sets of skills via continuous reflective practices. Hence, educators in higher education institutions are actively involved in research-driven teaching and learning practices. This change of role from mere content delivery to learning facilitators could be better achieved through a strong research-driven community of practice. Preparing 21st Century Teachers for Teach Less, Learn More (TLLM) Pedagogies is a pivotal reference source that provides vital research on the application of practice-based learning techniques in higher education institutions. This publication establishes a platform for academics to share their best practices to promote teach less, learn more pedagogies and learn reciprocally from the community of practice. While highlighting topics such as interactive learning, experiential technology, and logical thinking skills, this book is ideally designed for teachers, instructional designers, higher education faculty, deans, researchers, professionals, universities, academicians, and students seeking current research on transformative learning and future teaching practices.

As technology continues to saturate modern society, agriculture has started to adopt digital computing and data-driven innovations. This emergence of "e;smart"e; farming has led to various advancements in the field, including autonomous equipment and the collection of climate, livestock, and plant data. As connectivity and data management continue to revolutionize the farming industry, empirical research is a necessity for understanding these technological developments. Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture provides emerging research exploring the theoretical and practical aspects of critical technological solutions within the farming industry. Featuring coverage on a broad range of topics such as crop monitoring, precision livestock farming, and agronomic data processing, this book is ideally designed for farmers, agriculturalists, product managers, farm holders, manufacturers, equipment suppliers, industrialists, governmental professionals, researchers, academicians, and students seeking current research on technological applications within agriculture and farming.

Empowering learners for life requires a fundamental shift in higher education curriculum design. New priorities, pedagogies, technologies, spaces, and assessment strategies are required to enable learners to take ownership of their learning. "e;Student-centeredness"e; concepts are still prescriptive in nature as most decisions on curriculum, assessment, teaching, and learning approaches are still teacher-centric. Teachers are developing student-centered learning environments without the involvement of the learners in the planning, decision making, and/or design process. In addition, some lecturers are still practicing the traditional approaches of content delivery and conventional assessment methods rather than experimenting with innovative practices suited for student-centered approaches. Therefore, there is an ongoing need for research focused on the importance and effectiveness of a paradigm shift in education that involves student-teacher partnerships, fueled by innovative teaching and learning designs, where students take an active role and contribute as partners in learning. Transforming Curriculum Through Teacher-Learner Partnerships captures experiences and evidence among teachers in exploring the possibility of active student participation in curriculum design, delivery, and assessment through teacher-learner partnership. The chapters address issues of teacher-learner partnerships in designing the learning environment and how student-centered methods create resilient, adaptable, and future-capable learners. While highlighting topics within this scope such as learner autonomy, learning performance, self-efficacy, and teaching pedagogy, this book is ideally intended for teachers, administrators, teacher educators, practitioners, stakeholders, researchers, academicians, and students interested in issues related to the teacher-learner partnership.

Sequential data from Web server logs, online transaction logs, and performance measurements is collected each day. This sequential data is a valuable source of information, as it allows individuals to search for a particular value or event and also facilitates analysis of the frequency of certain events or sets of related events. Finding patterns in sequences is of utmost importance in many areas of science, engineering, and business scenarios. Pattern Discovery Using Sequence Data Mining: Applications and Studies provides a comprehensive view of sequence mining techniques and presents current research and case studies in pattern discovery in sequential data by researchers and practitioners. This research identifies industry applications introduced by various sequence mining approaches.

Constant improvements in technological applications have allowed for more opportunities to develop automated systems. This not only leads to higher success in smart data analysis, but also ensures that technological progression will continue. Ubiquitous Machine Learning and its Applications is a pivotal reference source for the latest research on the issues and challenges machines face in the new millennium. Featuring extensive coverage on relevant areas such as computational advertising, software engineering, and bioinformatics, this publication is an ideal resource for academicians, graduate students, engineering professionals, and researchers interested in discovering how they can apply these advancements to various disciplines.

The increase in connected devices in the internet of things (IoT) is leading to an exponential increase in the data that an organization is required to manage. To successfully utilize IoT in businesses, big data analytics are necessary in order to efficiently sort through the increased data. The combination of big data and IoT can thus enable new monitoring services and powerful processing of sensory data streams. The Handbook of Research on Big Data and the IoT is a pivotal reference source that provides vital research on emerging trends and recent innovative applications of big data and IoT, challenges facing organizations and the implications of these technologies on society, and best practices for their implementation. While highlighting topics such as bootstrapping, data fusion, and graph mining, this publication is ideally designed for IT specialists, managers, policymakers, analysts, software engineers, academicians, and researchers.

The current trend of learner centeredness in education has been challenging many of the current ways of working, especially in higher education institutions. This rapid change in educational institutions demands educators acquire new sets of skills via continuous reflective practices. Hence, educators in higher education institutions are actively involved in research-driven teaching and learning practices. This change of role from mere content delivery to learning facilitators could be better achieved through a strong research-driven community of practice. Preparing 21st Century Teachers for Teach Less, Learn More (TLLM) Pedagogies is a pivotal reference source that provides vital research on the application of practice-based learning techniques in higher education institutions. This publication establishes a platform for academics to share their best practices to promote teach less, learn more pedagogies and learn reciprocally from the community of practice. While highlighting topics such as interactive learning, experiential technology, and logical thinking skills, this book is ideally designed for teachers, instructional designers, higher education faculty, deans, researchers, professionals, universities, academicians, and students seeking current research on transformative learning and future teaching practices.

As technology continues to saturate modern society, agriculture has started to adopt digital computing and data-driven innovations. This emergence of "e;smart"e; farming has led to various advancements in the field, including autonomous equipment and the collection of climate, livestock, and plant data. As connectivity and data management continue to revolutionize the farming industry, empirical research is a necessity for understanding these technological developments. Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture provides emerging research exploring the theoretical and practical aspects of critical technological solutions within the farming industry. Featuring coverage on a broad range of topics such as crop monitoring, precision livestock farming, and agronomic data processing, this book is ideally designed for farmers, agriculturalists, product managers, farm holders, manufacturers, equipment suppliers, industrialists, governmental professionals, researchers, academicians, and students seeking current research on technological applications within agriculture and farming.

Empowering learners for life requires a fundamental shift in higher education curriculum design. New priorities, pedagogies, technologies, spaces, and assessment strategies are required to enable learners to take ownership of their learning. "e;Student-centeredness"e; concepts are still prescriptive in nature as most decisions on curriculum, assessment, teaching, and learning approaches are still teacher-centric. Teachers are developing student-centered learning environments without the involvement of the learners in the planning, decision making, and/or design process. In addition, some lecturers are still practicing the traditional approaches of content delivery and conventional assessment methods rather than experimenting with innovative practices suited for student-centered approaches. Therefore, there is an ongoing need for research focused on the importance and effectiveness of a paradigm shift in education that involves student-teacher partnerships, fueled by innovative teaching and learning designs, where students take an active role and contribute as partners in learning. Transforming Curriculum Through Teacher-Learner Partnerships captures experiences and evidence among teachers in exploring the possibility of active student participation in curriculum design, delivery, and assessment through teacher-learner partnership. The chapters address issues of teacher-learner partnerships in designing the learning environment and how student-centered methods create resilient, adaptable, and future-capable learners. While highlighting topics within this scope such as learner autonomy, learning performance, self-efficacy, and teaching pedagogy, this book is ideally intended for teachers, administrators, teacher educators, practitioners, stakeholders, researchers, academicians, and students interested in issues related to the teacher-learner partnership.