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.

In the fall of 1999, I was asked to teach a course on computer intrusion detection for the Department of Mathematical Sciences of The Johns Hopkins University. That course was the genesis of this book. I had been working in the field for several years at the Naval Surface Warfare Center, in Dahlgren, Virginia, under the auspices of the SHADOW program, with some funding by the Office of Naval Research. In designing the class, I was concerned both with giving an overview of the basic problems in computer security, and with providing information that was of interest to a department of mathematicians. Thus, the focus of the course was to be more on methods for modeling and detecting intrusions rather than one on how to secure one's computer against intrusions. The first task was to find a book from which to teach. I was familiar with several books on the subject, but they were all at either a high level, focusing more on the political and policy aspects of the problem, or were written for security analysts, with little to interest a mathematician. I wanted to cover material that would appeal to the faculty members of the department, some of whom ended up sitting in on the course, as well as providing some interesting problems for students. None of the books on the market at the time had an adequate discussion of mathematical issues related to intrusion detection.

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.

Visualization has proven to be an effective strategy for supporting users in coping with complexity in knowledge- and information-rich scenarios. Up to now, however, information visualization and knowledge visualization have been distinct research areas, which have been developed independently of each other. This book aims toward bringing both approaches together and look- ing for synergies, which may be used for fostering learning, instruction, and problem solving. This introductory article seeks to provide a conceptual frame- work and a preview of the contributions of this volume. The most important concepts referred to in this book are defined and a conceptual rationale is pro- vided as to why visualization may be effective in fostering, processing and managing knowledge and information. The basic ideas underlying knowledge visualization and information visualization are outlined. The preview of each approach addresses its basic concept, as well as how it fits into the conceptual rationale of the book. The contributions are structured according to whether they belong to one of the following basic categories: "Background", "Knowl- edge Visualization", "Information Visualization", and "Synergies".

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.

Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing.

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.

Conceptually, a database consists of objects and relationships. Object Relationship Notation (ORN) is a simple notation that more precisely defines relationships by combining UML multiplicities with uniquely defined referential actions.

The area of similarity searching is a very hot topic for both research and c- mercial applications. Current data processing applications use data with c- siderably less structure and much less precise queries than traditional database systems. Examples are multimedia data like images or videos that offer query by example search, product catalogs that provide users with preference based search, scientific data records from observations or experimental analyses such as biochemical and medical data, or XML documents that come from hetero- neous data sources on the Web or in intranets and thus does not exhibit a global schema. Such data can neither be ordered in a canonical manner nor meani- fully searched by precise database queries that would return exact matches. This novel situation is what has given rise to similarity searching, also - ferred to as content based or similarity retrieval. The most general approach to similarity search, still allowing construction of index structures, is modeled in metric space. In this book. Prof. Zezula and his co authors provide the first monograph on this topic, describing its theoretical background as well as the practical search tools of this innovative technology.

Data Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.

Among the many changes brought by the Internet is the emergence of electronic commerce over the Web. E-commerce activities, such as the online exchange of information, services, and products, are opening up completely new opportunities for business, at new levels of productivity and profitability. In parallel with the emergence of e-commerce, intelligent software agents as entities capable of independent action in open, unpredictable environments have matured into a promising new technology. Quite naturally, e-commerce agents hold great promise for exploiting the Internet's full potential as an electronic marketplace. The 20 coherently written chapters in this book by leading researchers and professionals present the state of the art in agent-mediated e-commerce. Researchers, professionals, and advanced students interested in e-commerce or agent technology will find this book an indispensable source of information and reference.

In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers.

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.

World Wide Web (WWW) provides interlinked hypertext documents (Web pages) that may contain text, images, videos, and other multimedia. WWW is growing at a remarkable rate and finding appropriate information can therefore be challenging. Web information retrieval deals with the search for documents, for information within documents and for metadata about documents, as well as that of searching databases in the WWW. Web information retrieval is an interdisciplinary science and deals with this challenge by exploiting various information technologies and computational intelligence approaches to design the next generation of web-information retrieval technologies.