This book provides a comprehensive presentation of a recently introduced framework, named "probabilistic indexing" (PrIx), for searching text in large collections of document images and other related applications. It fosters the development of new search engines for effective information retrieval from manuscripts which, however, lack the electronic text (transcripts) that would typically be required for such search and retrieval tasks.

The digital information revolution has brought about profound changes in our society and our life. New devices and powerful software have made it possible for consumers worldwide to create, manipulate, share, and enjoy the multimedia information. Internet and wireless networks offer ubiquitous channels to deliver and to exchange multimedia information for such pur­ poses as remote collaboration, distant learning, and entertainment. With all these advances in multimedia coding and communication technologies over the past decade, the major hurdle for allowing much broader access of multimedia assets and deployment of multimedia services no longer lies with bandwidth-related issues, but with how to make sure that content is used for its intended purpose by its intended recipients. The core issue then be­ comes the development of secure management of content usage and delivery across communication networks. Data hiding and digital watermarking are promising new technologies for multimedia information protection and rights management. Secondary data can be embedded imperceptibly in digital multimedia signals for a variety of applications, including ownership protection, authentication, access con­ trol, and annotation. Data hiding can also be used to send side information in multimedia communication for providing additional functionalities or for enhancing performance. The extraction of the embedded data mayor may not need knowledge of the original host media data. In addition to im­ perceptibility, robustness against moderate processing such as compression is also an important consideration.

Statistical computing provides the link between statistical theory and applied statistics. As at previous COMPSTAT volumes, the content of the book covers all aspects of this link, from the development and implementation of new statistical ideas to user experiences and software evaluation. The proceedings should appeal to anyone working in statistics and using computers, whether in universities, industrial companies, government agencies, research institutes or as software developers.

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

In recent years, a new class of applications has come to the forefront { p- marily due to the advancement in our ability to collect data from multitudes of devices, and process them e ciently. These include homeland security - plications, sensor/pervasive computing applications, various kinds of mo- toring applications, and even traditional applications belonging to nancial, computer network management, and telecommunication domains. These - plications need to process data continuously (and as long as data is available) from one or more sources. The sequence of data items continuously gen- ated by sources is termed a data stream. Because of the possible never-ending nature of a data stream, the amount of data to be processed is likely to be unbounded. In addition, timely detection of interesting changes or patterns or aggregations over incoming data is critical for many of these applications. Furthermore, the data arrival rates may uctuate over a period of time and may be bursty at times. For most of these applications, Quality of Service (or QoS) requirements, such as response time, memory usage, and throughput are extremely important. These application requirements make it infeasible to simply load the incoming data streams into a persistent store and process them e ectively using currently available database management techniques.

This book presents the latest advances and new visions in the field of ontology in information science.

With the unprecedented growth-rate at which data is being collected and stored electronically today in almost all fields of human endeavor, the efficient extraction of useful information from the data available is becoming an increasing scientific challenge and a massive economic need. This book presents thoroughly reviewed and revised full versions of papers presented at a workshop on the topic held during KDD'99 in San Diego, California, USA in August 1999 complemented by several invited chapters and a detailed introductory survey in order to provide complete coverage of the relevant issues. The contributions presented cover all major tasks in data mining including parallel and distributed mining frameworks, associations, sequences, clustering, and classification. All in all, the volume presents the state of the art in the young and dynamic field of parallel and distributed data mining methods. It will be a valuable source of reference for researchers and professionals.

Change Management for Semantic Web Services provides a thorough analysis of change management in the lifecycle of services for databases and workflows, including changes that occur at the individual service level or at the aggregate composed service level. This book describes taxonomy of changes that are expected in semantic service oriented environments. The process of change management consists of detecting, propagating, and reacting to changes.

Lernen Sie mit diesem Buch Methoden und Strategien des Enterprise Content Managements (ECM) kennen
Die digitale Transformation ist allgegenwärtig. Um weiterhin wettbewerbsfähig zu bleiben, führt für Unternehmen kein Weg mehr an der Digitalisierung ihrer Geschäftsprozesse vorbei. Diese Entwicklung macht auch nicht vor der Datenverwaltung Halt. In diesem Zusammenhang lautet das Stichwort: Enterprise Content Management (ECM). Was genau sich hinter diesen drei Buchstaben verbirgt, erklärt Ihnen Wolfgang Riggert in diesem ECM-Buch.

Many challenging problems in information systems engineering involve the manipulation of complex metadata artifacts or models, such as database schema, interface specifications, or object diagrams, and mappings between models. Applications solving metadata manipulation problems are complex and hard to build. The goal of generic model management is to reduce the amount of programming needed to solve such problems by providing a database infrastructure in which a set of high-level algebraic operators are applied to models and mappings as a whole rather than to their individual building blocks.

This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.

This volume constitutes the refereed proceedings of the 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021, held in Phuket, Thailand, in April 2021.
The total of 35 full papers accepted for publication in these proceedings were carefully reviewed and selected from 291 submissions. The papers are organized in the following topical sections: ​​data mining and machine learning methods; advanced data mining techniques and applications; intelligent and contextual systems; natural language processing; network systems and applications; computational imaging and vision; decision support and control systems; data modelling and processing for Industry 4.0.

This volume constitutes the refereed proceedings of the 19th International Conference on Business Process Management, BPM 2021, held in Rome, Italy, in September 2021.
The 23 full papers, one keynote paper, and 4 tutorial papers presented in this volume were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections named: foundations, engineering, and management.

This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.