Database management is attracting wide interest in both academic and industrial contexts. New application areas such as CAD/CAM, geographic information systems, and multimedia are emerging. The needs of these application areas are far more complex than those of conventional business applications. The purpose of this book is to bring together a set of current research issues that addresses a broad spectrum of topics related to database systems and applications. The book is divided into four parts: - object-oriented databases, - temporal/historical database systems, - query processing in database systems, - heterogeneity, interoperability, open system architectures, multimedia database systems.

Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

With about 200,000 entries, StarBriefs Plus represents the most comprehensive and accurately validated collection of abbreviations, acronyms, contractions and symbols within astronomy, related space sciences and other related fields. As such, this invaluable reference source (and its companion volume, StarGuides Plus) should be on the reference shelf of every library, organization or individual with any interest in these areas. Besides astronomy and associated space sciences, related fields such as aeronautics, aeronomy, astronautics, atmospheric sciences, chemistry, communications, computer sciences, data processing, education, electronics, engineering, energetics, environment, geodesy, geophysics, information handling, management, mathematics, meteorology, optics, physics, remote sensing, and so on, are also covered when justified. Terms in common use and/or of general interest have also been included where appropriate.

"This book concentrates on one particular and fast-growing application of mobile technologies: data acquisition for the tourism industry, providing travel agents, visitors, and hosts with the most advanced data mining methods, empirical research findings, and computational analysis techniques necessary to compete effectively in the global tourism industry"--Provided by publisher.

Due to the increasing availability of affordable internet services, the number of users, and the need for a wider range of multimedia-based applications, internet usage is on the rise. With so many users and such a large amount of data, the requirements of analyzing large data sets leads to the need for further advancements to information processing. Big Data Processing With Hadoop is an essential reference source that discusses possible solutions for millions of users working with a variety of data applications, who expect fast turnaround responses, but encounter issues with processing data at the rate it comes in. Featuring research on topics such as market basket analytics, scheduler load simulator, and writing YARN applications, this book is ideally designed for IoT professionals, students, and engineers seeking coverage on many of the real-world challenges regarding big data.

A smart city utilizes ICT technologies to improve the working effectiveness, share various data with the citizens, and enhance political assistance and societal wellbeing. The fundamental needs of a smart and sustainable city are utilizing smart technology for enhancing municipal activities, expanding monetary development, and improving citizens’ standards of living. The Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities discusses new mathematical models in smart and sustainable cities using big data, visualization tools in mathematical modeling, machine learning-based mathematical modeling, and more. It further delves into privacy and ethics in data analysis. Covering topics such as deep learning, optimization-based data science, and smart city automation, this premier reference source is an excellent resource for mathematicians, statisticians, computer scientists, civil engineers, government officials, students and educators of higher education, librarians, researchers, and academicians.

This compilation probably looks like one of the craziest things a human being could spend his or her time on. Yet nobody would wonder at someone taking a short walk every day - after twenty five years that person would have covered a surprisingly long distance. This is exactly the story behind this list, which appeared first as a few pages within the directory StarGuides (or whatever name it had at that time) and as a distinct sister publication since 1990. The idea behind this dictionary is to offer astronomers and related space scientists practical assistance in decoding the numerous abbreviations, acronyms, contractions and symbols which they might encounter in all aspects of the vast range of their professional activities, including traveling. Perhaps it is a bit paradoxical, but if scientists quickly grasp the meaning of an acronym solely in their own specific discipline, they will probably encounter more difficulties when dealing with adjacent fields. It is for this purpose that this dictionary might be most often used. Scientists might also refer to this compilation in order to avoid identifying a project by an acronym which already has too many meanings or confused definitions.

The field of data mining is receiving significant attention in today's information-rich society, where data is available from different sources and formats, in large volumes, and no longer constitutes a bottleneck for knowledge acquisition. This rich information has paved the way for novel areas of research, particularly in the crime data analysis realm. Data Mining Trends and Applications in Criminal Science and Investigations 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 modeling. Exploring the diverse ways that data is revolutionizing the field of criminal science, this publication meets the research needs of law enforcement professionals, data analysts, investigators, researchers, and graduate-level students.

Entity resolution is an essential tool in processing and analyzing data in order to draw precise conclusions from the information being presented. Further research in entity resolution is necessary to help promote information quality and improved data reporting in multidisciplinary fields requiring accurate data representation. Innovative Techniques and Applications of Entity Resolution draws upon interdisciplinary research on tools, techniques, and applications of entity resolution. This research work provides a detailed analysis of entity resolution applied to various types of data as well as appropriate techniques and applications and is appropriately designed for students, researchers, information professionals, and system developers.

The need for tailored data for machine learning models is often unsatisfied, as it is considered too much of a risk in the real-world context. Synthetic data, an algorithmically birthed counterpart to operational data, is the linchpin for overcoming constraints associated with sensitive or regulated information. In high-dimensional data, where the dimensions of features and variables often surpass the number of available observations, the emergence of synthetic data heralds a transformation. Applications of Synthetic High Dimensional Data delves into the algorithms and applications underpinning the creation of synthetic data, which surpass the capabilities of authentic datasets in many cases. Beyond mere mimicry, synthetic data takes center stage in prioritizing the mathematical domain, becoming the crucible for training robust machine learning models. It serves not only as a simulation but also as a theoretical entity, permitting the consideration of unforeseen variables and facilitating fundamental problem-solving. This book navigates the multifaceted advantages of synthetic data, illuminating its role in protecting the privacy and confidentiality of authentic data. It also underscores the controlled generation of synthetic data as a mechanism to safeguard private information while maintaining a controlled resemblance to real-world datasets. This controlled generation ensures the preservation of privacy and facilitates learning across datasets, which is crucial when dealing with incomplete, scarce, or biased data. Ideal for researchers, professors, practitioners, faculty members, students, and online readers, this book transcends theoretical discourse.