Data Mining for Design and Manufacturing: Methods and Applications is the first book that brings together research and applications for data mining within design and manufacturing. The aim of the book is 1) to clarify the integration of data mining in engineering design and manufacturing, 2) to present a wide range of domains to which data mining can be applied, 3) to demonstrate the essential need for symbiotic collaboration of expertise in design and manufacturing, data mining, and information technology, and 4) to illustrate how to overcome central problems in design and manufacturing environments. The book also presents formal tools required to extract valuable information from design and manufacturing data, and facilitates interdisciplinary problem solving for enhanced decision making. Audience: The book is aimed at both academic and practising audiences. It can serve as a reference or textbook for senior or graduate level students in Engineering, Computer, and Management Sciences who are interested in data mining technologies. The book will be useful for practitioners interested in utilizing data mining techniques in design and manufacturing as well as for computer software developers engaged in developing data mining tools.

Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Video segmentation is the most fundamental process for appropriate index ing and retrieval of video intervals. In general, video streams are composed 1 of shots delimited by physical shot boundaries. Substantial work has been done on how to detect such shot boundaries automatically (Arman et aI. , 1993) (Zhang et aI. , 1993) (Zhang et aI. , 1995) (Kobla et aI. , 1997). Through the inte gration of technologies such as image processing, speech/character recognition and natural language understanding, keywords can be extracted and associated with these shots for indexing (Wactlar et aI. , 1996). A single shot, however, rarely carries enough amount of information to be meaningful by itself. Usu ally, it is a semantically meaningful interval that most users are interested in re trieving. Generally, such meaningful intervals span several consecutive shots. There hardly exists any efficient and reliable technique, either automatic or manual, to identify all semantically meaningful intervals within a video stream. Works by (Smith and Davenport, 1992) (Oomoto and Tanaka, 1993) (Weiss et aI. , 1995) (Hjelsvold et aI. , 1996) suggest manually defining all such inter vals in the database in advance. However, even an hour long video may have an indefinite number of meaningful intervals. Moreover, video data is multi interpretative. Therefore, given a query, what is a meaningful interval to an annotator may not be meaningful to the user who issues the query. In practice, manual indexing of meaningful intervals is labour intensive and inadequate.

This practically-focused text presents a hands-on guide to making biometric technology work in real-life scenarios. Extensively revised and updated, this new edition takes a fresh look at what it takes to integrate biometrics into wider applications. An emphasis is placed on the importance of a complete understanding of the broader scenario, covering technical, human and implementation factors. This understanding may then be exercised through interactive chapters dealing with educational software utilities and the BANTAM Program Manager. Features: provides a concise introduction to biometrics; examines both technical issues and human factors; highlights the importance of a broad understanding of biometric technology implementation from both a technical and operational perspective; reviews a selection of freely available utilities including the BANTAM Program Manager; considers the logical next steps on the path from aspiration to implementation, and looks towards the future use of biometrics in context.

In a resolutely practical and data-driven project universe, the digital age changed the way data is collected, stored, analyzed, visualized and protected, transforming business opportunities and strategies. It is important for today's organizations and entrepreneurs to implement a robust data strategy and industrialize a set of "data-driven" solutions to utilize big data analytics to its fullest potential. Big Data Analytics for Entrepreneurial Success provides emerging perspectives on the theoretical and practical aspects of data analysis tools and techniques within business applications. Featuring coverage on a broad range of topics such as algorithms, data collection, and machine learning, this publication provides concrete examples and case studies of successful uses of data-driven projects as well as the challenges and opportunities of generating value from data using analytics. It is ideally designed for entrepreneurs, researchers, business owners, managers, graduate students, academicians, software developers, and IT professionals seeking current research on the essential tools and technologies for organizing, analyzing, and benefiting from big data.

This book represents the combined peer-reviewed proceedings of the ninth International Symposium on Intelligent Distributed Computing - IDC'2015, of the Workshop on Cyber Security and Resilience of Large-Scale Systems - WSRL'2015, and of the International Workshop on Future Internet and Smart Networks - FI&SN'2015. All the events were held in Guimaraes, Portugal during October 7th-9th, 2015. The 46 contributions published in this book address many topics related to theory and applications of intelligent distributed computing, including: Intelligent Distributed Agent-Based Systems, Ambient Intelligence and Social Networks, Computational Sustainability, Intelligent Distributed Knowledge Representation and Processing, Smart Networks, Networked Intelligence and Intelligent Distributed Applications, amongst others.

Distributed and Parallel Database Object Management brings together in one place important contributions and state-of-the-art research results in this rapidly advancing area of computer science. Distributed and Parallel Database Object Management serves as an excellent reference, providing insights into some of the most important issues in the field.

This book is an outcome of the second national conference on Communication, Cloud and Big Data (CCB) held during November 10-11, 2016 at Sikkim Manipal Institute of Technology. The nineteen chapters of the book are some of the accepted papers of CCB 2016. These chapters have undergone review process and then subsequent series of improvements. The book contains chapters on various aspects of communication, computation, cloud and big data. Routing in wireless sensor networks, modulation techniques, spectrum hole sensing in cognitive radio networks, antenna design, network security, Quality of Service issues in routing, medium access control protocol for Internet of Things, and TCP performance over different routing protocols used in mobile ad-hoc networks are some of the topics discussed in different chapters of this book which fall under the domain of communication. Moreover, there are chapters in this book discussing topics like applications of geographic information systems, use of radar for road safety, image segmentation and digital media processing, web content management system, human computer interaction, and natural language processing in the context of Bodo language. These chapters may fall under broader domain of computation. Issues like robot navigation exploring cloud technology, and application of big data analytics in higher education are also discussed in two different chapters. These chapters fall under the domains of cloud and big data, respectively.

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research.In general, data mining consists of extracting knowledge from data. In this book we particularly emphasize the importance of discovering comprehensible, interesting knowledge, which is potentially useful for the reader for intelligent decision making.In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions. In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.

Data warehousing and mining technologies are key assets today in many areas of human knowledge, from scientific to commercial and industrial settings, and the last decades have seen tremendous advances in those fields. ""Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions"" provides insight into the latest findings concerning data warehousing, data mining, and their applications in everyday human activities. Comprising a valuable resource for researchers, practitioners, and academicians, this advanced publication offers insight into recent field trends, techniques on how these technologies operate, and analysis of their effects.

The complexity and sensitivity of modern industrial processes and systems increasingly require adaptable advanced control protocols. These controllers have to be able to deal with circumstances demanding "judgement" rather than simple "yes/no," "on/off" responses, circumstances where an imprecise linguistic description is often more relevant than a cut-and-dried numerical one. The ability of fuzzy systems to handle numeric and linguistic information within a single framework renders them efficacious in this form of expert control system.

Divided into two parts, Fuzzy Logic, Identification and Predictive Control first shows you how to construct static and dynamic fuzzy models using the numerical data from a variety of real-world industrial systems and simulations. The second part demonstrates the exploitation of such models to design control systems employing techniques like data mining.

Fuzzy Logic, Identification and Predictive Control is a comprehensive introduction to the use of fuzzy methods in many different control paradigms encompassing robust, model-based, PID-like and predictive control. This combination of fuzzy control theory and industrial serviceability will make a telling contribution to your research whether in the academic or industrial sphere and also serves as a fine roundup of the fuzzy control area for the graduate student.

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrialcontrol.

For Database Systems and Database Design and Application courses offered at the junior, senior, and graduate levels in Computer Science departments. Written by well-known computer scientists, this accessible and succinct introduction to database systems focuses on database design and use. The authors provide in-depth coverage of databases from the point of view of the database designer, user, and application programmer, leaving implementation for later courses. It is the first database systems text to cover such topics as UML, algorithms for manipulating dependencies in relations, extended relational algebra, PHP, 3-tier architectures, data cubes, XML, XPATH, XQuery, XSLT.

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today. The Semantic Web treats data as a distributed resource on the scale of the World Wide Web, and incorporates features to address the challenges of massive data distribution as part of its basic design. The aim of the first two editions was to motivate the Semantic Web technology stack from end-to-end; to describe not only what the Semantic Web standards are and how they work, but also what their goals are and why they were designed as they are. It tells a coherent story from beginning to end of how the standards work to manage a world-wide distributed web of knowledge in a meaningful way. The third edition builds on this foundation to bring Semantic Web practice to enterprise. Fabien Gandon joins Dean Allemang and Jim Hendler, bringing with him years of experience in global linked data, to open up the story to a modern view of global linked data. While the overall story is the same, the examples have been brought up to date and applied in a modern setting, where enterprise and global data come together as a living, linked network of data. Also included with the third edition, all of the data sets and queries are available online for study and experimentation at data.world/swwo.

Database Solutions: A step-by-step guide to building databases 2/eAre you responsible for designing and creating the databases that keep your business running? Or are you studying for a module in database design? If so, Database Solutions is for you This fully revised and updated edition will make the database design and build process smoother, quicker and more reliable.Recipe for database success Take one RDMS - any of the major commercial products will do: Oracle, Informix, SQL Server, Access, Paradox Add one thorough reading of Database Solutions if you are an inexperienced database designer, or one recap of the methodology if you are an old hand Use the design and implementation frameworks to plan your timetable, use a common data model that fits your requirements and adapt as necessaryFeatures Includes hints and tips for success with comprehensive guidance on avoiding pitfalls and traps Shows how to create data models using the UML design notation Includes two full-length coded example databases written on Microsoft Access 2002 and Oracle 9i, plus 15 sample data models to adapt to your needs, chosen from seven common business areasNew for this edition New chapters on SQL (St

This book provides an insight into IoT intelligence in terms of applications and algorithmic challenges. The book is dedicated to addressing the major challenges in realizing the artificial intelligence in IoT-based applications including challenges that vary from cost and energy efficiency to availability to service quality in multidisciplinary fashion. The aim of this book is hence to focus on both the algorithmic and practical parts of the artificial intelligence approaches in IoT applications that are enabled and supported by wireless sensor networks and cellular networks. Targeted readers are from varying disciplines who are interested in implementing the smart planet/environments vision via intelligent wireless/wired enabling technologies. Includes the most up-to-date research and applications related to IoT artificial intelligence (AI); Provides new and innovative operational ideas regarding the IoT artificial intelligence that help advance the telecommunications industry; Presents AI challenges facing the IoT scientists and provides potential ways to solve them in critical daily life issues.

This book presents the main theoretical foundations behind smart services as well as specific guidelines and practically proven methods on how to design them. Furthermore, it gives an overview of the possible implementation architectures and shows how the designed smart services can be realized with specific technologies. Finally, it provides four specific use cases that show how smart services have been realized in practice and what impact they have within the businesses. The first part of the book defines the basic concepts and aims to establish a shared understanding of terms, such as smart services, service systems, smart service systems or cyber-physical systems. On this basis, it provides an analysis of existing work and includes insights on how an organization incorporating smart services could enhance and adjust their management and business processes. The second part on the design of smart services elaborates on what constitutes a successful smart service and describes experiences in the area of interdisciplinary teams, strategic partnerships, the overall service systems and the common data basis. In the third part, technical reference architectures are presented in detail, encompassing topics on the design of digital twins in cyber physical systems, the communication between entities and sensors in the age of Industry 4.0 as well as data management and integration. The fourth part then highlights a number of analytical possibilities that can be realized and that can constitute or be part of smart services, including machine learning and artificial intelligence methods. Finally, the applicability of the introduced design and development method is demonstrated by considering specific real-world use cases. These include services in the industrial and mobility sector, which were developed in direct cooperation with industry partners. The main target audience of this book is industry-focused readers, especially practitioners from industry, who are involved in supporting and managing digital business. These include professionals working in business development, product management, strategy, and development, ranging from middle management to Chief Digital Officers. It conveys all the basics needed for developing smart services and successfully placing them on the market by explaining technical aspects as well as showcasing practical use cases.

Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.

Artificial Intelligence for Capital Market throws light on application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to "black box" syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book: Features: Showcases artificial intelligence in finance service industry Explains Credit and Risk Analysis Elaborates on cryptocurrencies and blockchain technology Focuses on optimal choice of asset pricing model Introduces Testing of market efficiency and Forecasting in Indian Stock Market This book serves as a reference book for Academicians, Industry Professional, Traders, Finance Mangers and Stock Brokers. It may also be used as textbook for graduate level courses in financial services and financial Analytics.

The advent of the World Wide Web has changed the perspectives of groupware systems. The interest and deployment of Internet and intranet groupware solutions is growing rapidly, not just in academic circles but also in the commercial arena. The first generation of Web-based groupware tools has already started to emerge, and leading groupware vendors are urgently adapting their products for compatibility and integration with Web technologies. The focus of Groupware and the World Wide Web is to explore the potential for Web-based groupware. This book includes an analysis of the key characteristics of the Web, presenting reasons for its success, and describes developments of a diverse range of Web-based groupware systems. An emphasis on the technical obstacles and challenges is implemented by more analytical discussions and perspectives, including that of Information Technology managers looking to deploy groupware solutions within their organizations. Written by experts from different backgrounds - academic and commercial, technical and organizational - this book provides a unique overview of and insight into current issues and future possibilities concerning extension of the World Wide Web for group working.

Nontraditional Database Systems is the fifth volume in the Advanced Information Processing Technology series. It brings together the results of research carried out by the Japanese database research community in the field of nontraditional database systems.

The book examines nontraditional types of applications, data types, systems and environments together with high-performance architecture to support nontraditional applications, such as web mining, data engineering and object processing.

eBook available with sample pages: 0203301943

The use of information and communication technologies to support public administrations, governments and decision makers has been recorded for more than 20 years and dubbed e-Government. Moving towards open governance roadmaps worldwide, electronic participation and citizen engagement stand out as a new domain, important both for decision makers and citizens; and over the last decade, there have been a variety of related pilot projects and innovative approaches.

With contributions from leading researchers, Charalabidis and Koussouris provide the latest research findings such as theoretical foundations, principles, methodologies, architectures, technical frameworks, cases and lessons learnt within the domain of open, collaborative governance and online citizen engagement. The book is divided into three sections: Section one, "Public Policy Debate Foundations," lays the foundations regarding processes and methods for scoping, planning, evaluating and transforming citizen engagement. The second section, "Information and Communication Technologies for Citizen Participation," details practical approaches to designing and creating collaborative governance infrastructures and citizen participation for businesses and administrations. Lastly, the third section on "Future Research Directions of Open, Collaborative ICT-enabled Governance" provides a constructive critique of the developments in the past and presents prospects regarding future challenges and research directions.

The book is mainly written for academic researchers and graduate students working in the computer, social, political and management sciences. Its audience includes researchers and practitioners in e-Governance, public administration officials, policy and decision makers at the local, national and international level engaged in the design and creation of policies and services, and ICT professionals engaged in e-Governance and policy modelling projects and solutions.

The key competing texts are practitioner-focused 'how to' guides, whilst our book combines rigorous theory with practical insight and examples, with authors from both the academic and business world, making it more adoptable as a student text; Unlike other books on the subject, this has a customer focus and an exploration of how big data can add value to customers as well as organisations; Enables readers to move from "big data" to "big solutions" by demonstrating how to integrate data analytics into specific goals and processes for implementation; Highly successful and well regarded both for students and practitioners

The emerging field of Data Science has had a large impact on science and society. This book explores how one distinguishing feature of Data Science - its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded - suggests serious implications for learning and education. Drawing from theories of learning and identity development in the learning sciences, this volume investigates the impacts of these complex relationships on how learners think about, use, and share data, including their understandings of data in light of history, race, geography, and politics. More than just using 'real world examples' to motivate students to work with data, this book demonstrates how learners' relationships to data shape how they approach those data with agency, as part of their social and cultural lives. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, socially aware, and transformative Data Science Education. The chapters in this book were originally published as a special issue of the Journal of the Learning Sciences.

Constraint databases provide extra expressive power over relational databases in a largely hidden way at the data-storage or physical level. Constraints, such as linear or polynomial equations, are used to represent large sets in a compact manner. They keep the view of the database for a user or application programmer almost as simple as in relational databases. "Introduction to Constraint Databases" comprehensively covers both constraint-database theory and several sample systems. The book reveals how constraint databases bring together techniques from a variety of fields, such as logic and model theory, algebraic and computational geometry, and symbolic computation, to the design and analysis of data models and query languages. Constraint databases are shown to be powerful and simple tools for data modeling and querying in application areas¿such as environmental modeling, bioinformatics, and computer vision--that are not suitable for relational databases. Specific applications are examined in geographic information systems, spatiotemporal data management, linear programming, genome databases, model checking of automata, and other areas. Topics and features: *Offers a database perspective and a focus on simplicity at the user level *Utilizes simple tools for determining whether queries are safe or not *Incorporates scientist-supplied descriptions of applications *Explains constraint databases from a developer's viewpoint *Provides extensive exercise sets, and sample software systems, that facilitate rapid learning of the topic within a real-world software context This volume presents a comprehensive introduction to the theory and applications of constraint database systems, which provide new methods for the design of data models and query languages. It is an essential resource for advanced students, practitioners, and professionals in computer science, database systems, and information systems.

Database System Concepts by Silberschatz, Korth and Sudarshan is now in its 7th edition and is one of the cornerstone texts of database education. It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible. The text is designed for a first course in databases at the junior/senior undergraduate level or the first year graduate level. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true.