Bøger i Synthesis Lectures on Information Concepts, Retrieval, and Services serien

Policy-based data management enables the creation of community-specific collections. Every collection is created for a purpose. The purpose defines the set of properties that will be associated with the collection. The properties are enforced by management policies that control the execution of procedures that are applied whenever data are ingested or accessed. The procedures generate state information that defines the outcome of enforcing the management policy. The state information can be queried to validate assessment criteria and verify that the required collection properties have been conserved. The integrated Rule-Oriented Data System implements the data management framework required to support policy-based data management. Policies are turned into computer actionable Rules. Procedures are composed from a microservice-oriented architecture. The result is a highly extensible and tunable system that can enforce management policies, automate administrative tasks, and periodically validate assessment criteria. iRODS 4.0+ represents a major effort to analyze, harden, and package iRODS for sustainability, modularization, security, and testability. This has led to a fairly significant refactorization of much of the underlying codebase. iRODS has been modularized whereby existing iRODS 3.x functionality has been replaced and provided by small, interoperable plugins. The core is designed to be as immutable as possible and serve as a bus for handling the internal logic of the business of iRODS. Seven major interfaces have been exposed by the core and allow extensibility and separation of functionality into plugins.

Information Architecture is about organizing and simplifying information, designing and integrating information spaces/systems, and creating ways for people to find and interact with information content. Its goal is to help people understand and manage information and make the right decisions accordingly. This updated and revised edition of the book looks at integrated information spaces in the web context and beyond, with a focus on putting theories and principles into practice.In the ever-changing social, organizational, and technological contexts, information architects not only design individual information spaces (e.g., websites, software applications, and mobile devices), but also tackle strategic aggregation and integration of multiple information spaces across websites, channels, modalities, and platforms. Not only do they create predetermined navigation pathways, but they also provide tools and rules for people to organize information on their own and get connected with others.Information architects work with multi-disciplinary teams to determine the user experience strategy based on user needs and business goals, and make sure the strategy gets carried out by following the user-centered design (UCD) process via close collaboration with others. Drawing on the authors' extensive experience as HCI researchers, User Experience Design practitioners, and Information Architecture instructors, this book provides a balanced view of the IA discipline by applying theories, design principles, and guidelines to IA and UX practices. It also covers advanced topics such as iterative design, UX decision support, and global and mobile IA considerations. Major revisions include moving away from a web-centric view toward multi-channel, multi-device experiences. Concepts such as responsive design, emerging design principles, and user-centered methods such as Agile, Lean UX, and Design Thinking are discussed and related to IA processes and practices.

Information retrieval used to mean looking through thousands of strings of texts to find words or symbols that matched a user's query. Today, there are many models that help index and search more effectively so retrieval takes a lot less time. Information retrieval (IR) is often seen as a subfield of computer science and shares some modeling, applications, storage applications and techniques, as do other disciplines like artificial intelligence, database management, and parallel computing. This book introduces the topic of IR and how it differs from other computer science disciplines. A discussion of the history of modern IR is briefly presented, and the notation of IR as used in this book is defined. The complex notation of relevance is discussed. Some applications of IR is noted as well since IR has many practical uses today. Using information retrieval with fuzzy logic to search for software terms can help find software components and ultimately help increase the reuse of software. This is just one practical application of IR that is covered in this book.Some of the classical models of IR is presented as a contrast to extending the Boolean model. This includes a brief mention of the source of weights for the various models. In a typical retrieval environment, answers are either yes or no, i.e., on or off. On the other hand, fuzzy logic can bring in a "e;degree of"e; match, vs. a crisp, i.e., strict match. This, too, is looked at and explored in much detail, showing how it can be applied to information retrieval. Fuzzy logic is often times considered a soft computing application and this book explores how IR with fuzzy logic and its membership functions as weights can help indexing, querying, and matching. Since fuzzy set theory and logic is explored in IR systems, the explanation of where the fuzz is ensues.The concept of relevance feedback, including pseudorelevance feedback is explored for the various models of IR. For the extended Boolean model, the use of genetic algorithms for relevance feedback is delved into.The concept of query expansion is explored using rough set theory. Various term relationships is modeled and presented, and the model extended for fuzzy retrieval. An example using the UMLS terms is also presented. The model is also extended for term relationships beyond synonyms.Finally, this book looks at clustering, both crisp and fuzzy, to see how that can improve retrieval performance. An example is presented to illustrate the concepts.

Institutions typically treat research integrity violations as black and white, right or wrong. The result is that the wide range of grayscale nuances that separate accident, carelessness, and bad practice from deliberate fraud and malpractice often get lost. This lecture looks at how to quantify the grayscale range in three kinds of research integrity violations: plagiarism, data falsification, and image manipulation.Quantification works best with plagiarism, because the essential one-to-one matching algorithms are well known and established tools for detecting when matches exist. Questions remain, however, of how many matching words of what kind in what location in which discipline constitute reasonable suspicion of fraudulent intent. Different disciplines take different perspectives on quantity and location. Quantification is harder with data falsification, because the original data are often not available, and because experimental replication remains surprisingly difficult. The same is true with image manipulation, where tools exist for detecting certain kinds of manipulations, but where the tools are also easily defeated.This lecture looks at how to prevent violations of research integrity from a pragmatic viewpoint, and at what steps can institutions and publishers take to discourage problems beyond the usual ethical admonitions. There are no simple answers, but two measures can help: the systematic use of detection tools and requiring original data and images. These alone do not suffice, but they represent a start.The scholarly community needs a better awareness of the complexity of research integrity decisions. Only an open and wide-spread international discussion can bring about a consensus on where the boundary lines are and when grayscale problems shade into black. One goal of this work is to move that discussion forward.

Rapid technological changes and availability of news anywhere and at any moment have changed how people seek out news. Increasingly, consumers no longer take deliberate actions to read the news, instead stumbling upon news online. While the emergence of serendipitous news discovery online has been recognized in the literature, there is a limited understanding about how people experience this behavior. Based on the mixed method study that investigated online news reading behavior of residents in a Midwestern U.S. town, we explore how people accidentally discover news when engaged in various online activities. Employing the grounded theory approach, we define Incidental Exposure to Online News (IEON) as individual's memorable experiences of chance encounters with interesting, useful, or surprising news while using the Internet for news browsing or for non-news-related online activities, such as checking email or visiting social networking sites. The book presents a conceptual framework of IEON that advances research and an understanding of serendipitous news discovery from people's holistic experiences of news consumption in their everyday lives. The proposed IEON Process Model identifies key steps in an IEON experience that could help news reporters and developers of online news platforms create innovative storytelling and design strategies to catch consumers' attention during their online activities. Finally, this book raises important methodological questions for further investigation: how should serendipitous news discovery be studied, measured, and observed, and what are the essential elements that differentiate this behavior from other types of online news consumption and information behaviors?

In recent years there has been an increasing demand for research evaluation within universities and other research-based organisations. In parallel, there has been an increasing recognition that traditional citation-based indicators are not able to reflect the societal impacts of research and are slow to appear. This has led to the creation of new indicators for different types of research impact as well as timelier indicators, mainly derived from the Web. These indicators have been called altmetrics, webometrics or just web metrics. This book describes and evaluates a range of web indicators for aspects of societal or scholarly impact, discusses the theory and practice of using and evaluating web indicators for research assessment and outlines practical strategies for obtaining many web indicators. In addition to describing impact indicators for traditional scholarly outputs, such as journal articles and monographs, it also covers indicators for videos, datasets, software and other non-standard scholarly outputs. The book describes strategies to analyse web indicators for individual publications as well as to compare the impacts of groups of publications. The practical part of the book includes descriptions of how to use the free software Webometric Analyst to gather and analyse web data. This book is written for information science undergraduate and Master's students that are learning about alternative indicators or scientometrics as well as Ph.D. students and other researchers and practitioners using indicators to help assess research impact or to study scholarly communication.

A trustworthy repository provides assurance in the form of management documents, event logs, and audit trails that digital objects are being managed correctly. The assurance includes plans for the sustainability of the repository, the accession of digital records, the management of technology evolution, and the mitigation of the risk of data loss. A detailed assessment is provided by the ISO-16363:2012 standard, "e;Space data and information transfer systems-Audit and certification of trustworthy digital repositories."e; This book examines whether the ISO specification for trustworthiness can be enforced by computer actionable policies. An implementation of the policies is provided and the policies are sorted into categories for procedures to manage externally generated documents, specify repository parameters, specify preservation metadata attributes, specify audit mechanisms for all preservation actions, specify control of preservation operations, and control preservation properties as technology evolves. An application of the resulting procedures is made to enforce trustworthiness within National Science Foundation data management plans.

Big data and human-computer information retrieval (HCIR) are changing IR. They capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these characteristics which are largely ignored by conventional techniques. What is missing is an ability for the model to change over time and be responsive to stimulus. Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in data sets typically collected over long time spans and models need to respond to these changes. Additionally, the size of modern datasets enforces limits on the amount of learning a system can achieve. Further to this, advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way. In this book we provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. We define dynamics, what it means within the context of IR and highlight examples of problems where dynamics play an important role. We cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics. The theoretical component is based around the Markov Decision Process (MDP), a mathematical framework taken from the field of Artificial Intelligence (AI) that enables us to construct models that change according to sequential inputs. We define the framework and the algorithms commonly used to optimize over it and generalize it to the case where the inputs aren't reliable. We explore the topic of reinforcement learning more broadly and introduce another tool known as a Multi-Armed Bandit which is useful for cases where exploring model parameters is beneficial. Following this we introduce theories and algorithms which can be used to incorporate dynamics into an IR model before presenting an array of state-of-the-art research that already does, such as in the areas of session search and online advertising. Change is at the heart of modern Information Retrieval systems and this book will help equip the reader with the tools and knowledge needed to understand Dynamic Information Retrieval Modeling.

With the proliferation of social network services, more and more social users, such as individuals and organizations, are simultaneously involved in multiple social networks for various purposes. In fact, multiple social networks characterize the same social users from different perspectives, and their contexts are usually consistent or complementary rather than independent. Hence, as compared to using information from a single social network, appropriate aggregation of multiple social networks offers us a better way to comprehensively understand the given social users. Learning across multiple social networks brings opportunities to new services and applications as well as new insights on user online behaviors, yet it raises tough challenges: (1) How can we map different social network accounts to the same social users? (2) How can we complete the item-wise and block-wise missing data? (3) How can we leverage the relatedness among sources to strengthen the learning performance? And (4) How can we jointly model the dual-heterogeneities: multiple tasks exist for the given application and each task has various features from multiple sources? These questions have been largely unexplored to date. We noticed this timely opportunity, and in this book we present some state-of-the-art theories and novel practical applications on aggregation of multiple social networks. In particular, we first introduce multi-source dataset construction. We then introduce how to effectively and efficiently complete the item-wise and block-wise missing data, which are caused by the inactive social users in some social networks. We next detail the proposed multi-source mono-task learning model and its application in volunteerism tendency prediction. As a counterpart, we also present a mono-source multi-task learning model and apply it to user interest inference. We seamlessly unify these models with the so-called multi-source multi-task learning, and demonstrate several application scenarios, such as occupation prediction. Finally, we conclude the book and figure out the future research directions in multiple social network learning, including the privacy issues and source complementarity modeling. This is preliminary research on learning from multiple social networks, and we hope it can inspire more active researchers to work on this exciting area. If we have seen further it is by standing on the shoulders of giants.

Collaboration among scholars has always been recognized as a fundamental feature of scientific discovery. The ever-increasing diversity among disciplines and complexity of research problems makes it even more compelling to collaborate in order to keep up with the fast pace of innovation and advance knowledge. Along with the rapidly developing Internet communication technologies and the increasing popularity of the social web, we have observed many important developments of scholarly collaboration on the academic social web. In this book, we review the rapid transformation of scholarly collaboration on various academic social web platforms and examine how these platforms have facilitated academics throughout their research lifecycle-from forming ideas, collecting data, and authoring articles to disseminating findings. We refer to the term "e;academic social web platforms"e; in this book as a category of Web 2.0 tools or online platforms (such as CiteULike, Mendeley, Academia.edu, and ResearchGate) that enable and facilitate scholarly information exchange and participation. We will also examine scholarly collaboration behaviors including sharing academic resources, exchanging opinions, following each other's research, keeping up with current research trends, and, most importantly, building up their professional networks. Inspired by the model developed Olson et al. [2000] on factors for successful scientific collaboration, our examination of the status of scholarly collaboration on the academic social web has four emphases: technology readiness, coupling work, building common ground, and collaboration readiness. Finally, we talk about the insights and challenges of all these online scholarly collaboration activities imposed on the research communities who are engaging in supporting online scholarly collaboration. This book aims to help researchers and practitioners understand the development of scholarly collaboration on the academic social web, and to build up an active community of scholars who are interested in this topic.

In this book, we aim to provide a fairly comprehensive overview of the scalability and efficiency challenges in large-scale web search engines. More specifically, we cover the issues involved in the design of three separate systems that are commonly available in every web-scale search engine: web crawling, indexing, and query processing systems. We present the performance challenges encountered in these systems and review a wide range of design alternatives employed as solution to these challenges, specifically focusing on algorithmic and architectural optimizations. We discuss the available optimizations at different computational granularities, ranging from a single computer node to a collection of data centers. We provide some hints to both the practitioners and theoreticians involved in the field about the way large-scale web search engines operate and the adopted design choices. Moreover, we survey the efficiency literature, providing pointers to a large number of relatively important research papers. Finally, we discuss some open research problems in the context of search engine efficiency.

The study of people, information, and communication technologies and the contexts in which these technologies are designed, implemented, and used has long interested scholars in a wide range of disciplines, including the social study of computing, science and technology studies, the sociology of technology, and management information systems. As ICT use has spread from organizations into the larger world, these devices have become routine information appliances in our social lives, researchers have begun to ask deeper and more profound questions about how our lives have become bound up with technologies. A common theme running through this research is that the relationships among people, technology, and context are dynamic, complex, and critically important to understand. This book explores social informatics (SI), one important and dynamic approach that researchers have used to study these complex relationships. SI is "e;the interdisciplinary study of the design, uses and consequences of information technology that takes into account their interaction with institutional and cultural contexts"e; (Kling 1998, p. 52; 1999). SI provides flexible frameworks to explore complex and dynamic socio-technical interactions. As a domain of study related largely by common vocabulary and conclusions, SI critically examines common conceptions of and expectations for technology, by providing contextual evidence. This book describes the evolution of SI research and identifies challenges and opportunities for future research. In what might be seen as an example of socio-technical "e;natural selection,"e; SI emerged in six different locations during the 1980s and 1990s: Norway, Slovenia, Japan, the former Soviet Union, the UK and, last, the U.S. As SI evolved, the version popularized in the US became globally dominant. The evolution of SI is presented in five stages: emergence, foundational, expansion, coherence, and transformation. Thus, we divide SI research into five major periods: an emergence stage, when various forms of SI emerged around the globe, an early period of foundational work which grounds SI (Pre-1990s), a period of expansion (1990s), a robust period of coherence and influence by Rob Kling (2000-2005), and a period of transformation (2006-present). Following the description of the five periods we discuss the evolution throughout the periods under five sections: principles, concepts, approaches, topics, and findings. Principles refer to the overarching motivations and labels employed to describe scholarly work. Approaches describe the theories, frameworks, and models employed in analysis, emphasizing the multi-disciplinary and interdisciplinary nature of SI. Concepts include specific processes, entities, themes, and elements of discourse within a given context, revealing a shared SI language surrounding change, complexity, consequences, and social elements of technology. Topics label the issues and general domains studied within social informatics, ranging from scholarly communication to online communities to information systems. Findings from seminal SI works illustrate growing insights over time and demonstrate how repeatable explanations unify SI. In the concluding remarks, we raise questions about the possible futures of SI research.

The time-worn aphorism "e;close only counts in horseshoes and hand grenades"e; is clearly inadequate. Close also counts in golf, shuffleboard, archery, darts, curling, and other games of accuracy in which hitting the precise center of the target isn't to be expected every time, or in which we can expect to be driven from the target by skilled opponents. This book is not devoted to sports discussions, but to efficient algorithms for determining pairs of closely related web pages-and a few other situations in which we have found that inexact matching is good enough - where proximity suffices. We will not, however, attempt to be comprehensive in the investigation of probabilistic algorithms, approximation algorithms, or even techniques for organizing the discovery of nearest neighbors. We are more concerned with finding nearby neighbors; if they are not particularly close by, we are not particularly interested. In thinking of when approximation is sufficient, remember the oft-told joke about two campers sitting around after dinner. They hear noises coming towards them. One of them reaches for a pair of running shoes, and starts to don them. The second then notes that even with running shoes, they cannot hope to outrun a bear, to which the first notes that most likely the bear will be satiated after catching the slower of them. We seek problems in which we don't need to be faster than the bear, just faster than the others fleeing the bear.

Part 1 in "e;The Future of"e; series covers the fundamentals of personal information management (PIM) and then explores the seismic shift, already well underway, toward a world where our information is always at hand (thanks to our devices) and "e;forever"e; on the web. Part 2, "e;Transforming Technologies to Manage Our Information,"e; provides a more focused look at technologies for managing information. The opening chapter discusses "e;natural interface"e; technologies of input/output to free us from keyboard, screen, and mouse. Successive chapters then explore technologies to save, search, and structure our information. A concluding chapter introduces the possibility that we may see dramatic reductions in the "e;clerical tax"e; we pay as we work with our information. Focus in this concluding Part 3 to the series shifts to the practical and to the near future. What can we do, now or soon, to manage our information better? And, as we do so, how might we build a better world? Part 3 is in three chapters:Chapter 10. Group Information Management and the Social Fabric in PIM. How do we preserve and promote our PIM practices as we interact with others at home, at school, at work, at play and in wider, even global, communities?Chapter 11. PIM by Design. What principles guide us? How can developers build better tools for PIM? How can the rest of us make better use of the tools we already have?Chapter 12. To Each of Us, Our Own concludes with an exploration of the ways each of us, individually, can develop better practices for the management of our information in service of the lives we wish to live and toward a better world we all must share.

This book introduces fundamentals of information communication. At first, concepts and characteristics of information and information communication are summarized. And then five classic models of information communication are introduced. The mechanisms and fundamental laws of the information transmission process are also discussed. In order to realize information communication, impediments in information communication process are identified and analyzed. For the purpose of investigating implications of Internet information communication, patterns and characteristics of information communication in the Internet and Web 2.0 environment are also analyzed. In the end, case studies are provided for readers to understand the theory.

Citation analysis-the exploration of reference patterns in the scholarly and scientific literature-has long been applied in a number of social sciences to study research impact, knowledge flows, and knowledge networks. It has important information science applications as well, particularly in knowledge representation and in information retrieval. Recent years have seen a burgeoning interest in citation analysis to help address research, management, or information service issues such as university rankings, research evaluation, or knowledge domain visualization. This renewed and growing interest stems from significant improvements in the availability and accessibility of digital bibliographic data (both citation and full text) and of relevant computer technologies. The former provides large amounts of data and the latter the necessary tools for researchers to conduct new types of large-scale citation analysis, even without special access to special data collections. Exciting new developments are emerging this way in many aspects of citation analysis. This book critically examines both theory and practical techniques of citation network analysis and visualization, one of the two main types of citation analysis (the other being evaluative citation analysis). To set the context for its main theme, the book begins with a discussion of the foundations of citation analysis in general, including an overview of what can and what cannot be done with citation analysis (Chapter 1). An in-depth examination of the generally accepted steps and procedures for citation network analysis follows, including the concepts and techniques that are associated with each step (Chapter 2). Individual issues that are particularly important in citation network analysis are then scrutinized, namely: field delineation and data sources for citation analysis (Chapter 3); disambiguation of names and references (Chapter 4); and visualization of citation networks (Chapter 5). Sufficient technical detail is provided in each chapter so the book can serve as a practical how-to guide to conducting citation network analysis and visualization studies. While the discussion of most of the topics in this book applies to all types of citation analysis, the structure of the text and the details of procedures, examples, and tools covered here are geared to citation network analysis rather than evaluative citation analysis. This conscious choice was based on the authors' observation that, compared to evaluative citation analysis, citation network analysis has not been covered nearly as well by dedicated books, despite the fact that it has not been subject to nearly as much severe criticism and has been substantially enriched in recent years with new theory and techniques from research areas such as network science, social network analysis, or information visualization. Table of Contents: Acknowledgment / Dedications /Foundations of Citation Analysis / Conducting Citation Network Analysis: Steps, Concepts, Techniques, and Tools / Field Delineation and Data Sources for Citation Analysis / Disambiguation in Citation Network Analysis / Visualization of Citation Networks / Appendix 3.3 / Appendix 5.4.2 / Bibliography / Author Biographies

User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus ``in the wild''), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users' motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings, and across different user groups and web domains. At the same time, this book advocates for the development of ``good'' measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct, which has become so vital in our wired world.

Digital libraries (DLs) have introduced new technologies, as well as leveraging, enhancing, and integrating related technologies, since the early 1990s. These efforts have been enriched through a formal approach, e.g., the 5S (Societies, Scenarios, Spaces, Structures, Streams) framework, which is discussed in two earlier volumes in this series. This volume should help advance work not only in DLs, but also in the WWW and other information systems. Drawing upon four (Kozievitch, Murthy, Park, Yang) completed and three (Elsherbiny, Farag, Srinivasan) in-process dissertations, as well as the efforts of collaborating researchers and scores of related publications, presentations, tutorials, and reports, this book should advance the DL field with regard to at least six key technologies. By integrating surveys of the state-of-the-art, new research, connections with formalization, case studies, and exercises/projects, this book can serve as a computing or information science textbook. It can support studies in cyber-security, document management, hypertext/hypermedia, IR, knowledge management, LIS, multimedia, and machine learning. Chapter 1, with a case study on fingerprint collections, focuses on complex (composite, compound) objects, connecting DL and related work on buckets, DCC, and OAI-ORE. Chapter 2, discussing annotations, as in hypertext/hypermedia, emphasizes parts of documents, including images as well as text, managing superimposed information. The SuperIDR system, and prototype efforts with Flickr, should motivate further development and standardization related to annotation, which would benefit all DL and WWW users. Chapter 3, on ontologies, explains how they help with browsing, query expansion, focused crawling, and classification. This chapter connects DLs with the Semantic Web, and uses CTRnet as an example. Chapter 4, on (hierarchical) classification, leverages LIS theory, as well as machine learning, and is important for DLs as well as the WWW. Chapter 5, on extraction from text, covers document segmentation, as well as how to construct a database from heterogeneous collections of references (from ETDs); i.e., converting strings to canonical forms. Chapter 6 surveys the security approaches used in information systems, and explains how those approaches can apply to digital libraries which are not fully open. Given this rich content, those interested in DLs will be able to find solutions to key problems, using the right technologies and methods. We hope this book will help show how formal approaches can enhance the development of suitable technologies and how they can be better integrated with DLs and other information systems.

Digital libraries (DLs) have evolved since their launch in 1991 into an important type of information system, with widespread application. This volume advances that trend further by describing new research and development in the DL field that builds upon the 5S (Societies, Scenarios, Spaces, Structures, Streams) framework, which is discussed in three other DL volumes in this series.While the 5S framework may be used to describe many types of information systems, and is likely to have even broader utility and appeal, we focus here on digital libraries. Drawing upon six (Akbar, Kozievitch, Leidig, Li, Murthy, Park) completed and two (Chen, Fouh) in-process dissertations, as well as the efforts of collaborating researchers, and scores of related publications, presentations, tutorials, and reports, this book demonstrates the applicability of 5S in five digital library application areas, that also have importance in the context of the WWW, Web 2.0, and innovative information systems. By integrating surveys of the state-of-the-art, newresearch, connections with formalization, case studies, and exercises/projects, this book can serve as a textbook for those interested in computing, information, and/or library science. Chapter 1 focuses on images, explaining how they connect with information retrieval, in the context of CBIR systems. Chapter 2 gives two case studies of DLs used in education, which is one of the most common applications of digital libraries. Chapter 3 covers social networks, which are at the heart of work onWeb 2.0, explaining the construction and use of deduced graphs, that can enhance retrieval and recommendation. Chapter 4 demonstrates the value of DLs in eScience, focusing, in particular, on cyber-infrastructure for simulation. Chapter 5 surveys geospatial information in DLs, with a case study on geocoding. Given this rich content, we trust that any interested in digital libraries, or in related systems, will find this volume to be motivating, intellectually satisfying, and useful. We hope it will help move digital libraries forward into a science as well as a practice. We hope it will help build community that will address the needs of the next generation of DLs.

This is the second book based on the 5S (Societies, Scenarios, Spaces, Structures, Streams) approach to digital libraries (DLs). Leveraging the first volume, on Theoretical Foundations, we focus on the key issues of evaluation and integration. These cross-cutting issues serve as a bridge for those interested in DLs, connecting the introduction and formal discussion in the first book, with the coverage of key technologies in the third book, and of illustrative applications in the fourth book. These two topics have central importance in the DL field, allowing it to be treated scientifically as well as practically. In the scholarly world, we only really understand something if we know how to measure and evaluate it. In the Internet era of distributed information systems, we only can be practical at scale if we integrate across both systems and their associated content. Evaluation of DLs must take place atmultiple levels,so we can address the different entities and their associated measures. Thus, for digital objects, we assess accessibility, pertinence, preservability, relevance, significance, similarity, and timeliness. Other measures are specific to higher-level constructs like metadata, collections, catalogs, repositories, and services.We tie these together through a case study of the 5SQual tool, which we designed and implemented to perform an automatic quantitative evaluation of DLs. Thus, across the Information Life Cycle, we describe metrics and software useful to assess the quality of DLs, and demonstrate utility with regard to representative application areas: archaeology and education. Though integration has been a challenge since the earliest work on DLs, we provide the first comprehensive 5S-based formal description of the DL integration problem, cast in the context of related work. Since archaeology is a fundamentally distributed enterprise, we describe ETANADL, for integrating Near Eastern Archeology sites and information. Thus, we show how 5S-based modeling can lead to integrated services and content. While the first book adopts a minimalist and formal approach to DLs, and provides a systematic and functional method to design and implement DL exploring services, here we broaden to practical DLs with richer metamodels, demonstrating the power of 5S for integration and evaluation.

In 1991, a group of researchers chose the term digital libraries to describe an emerging field of research, development, and practice. Since then, Virginia Tech has had funded research in this area, largely through its Digital Library Research Laboratory. This book is the first in a four book series that reports our key findings and current research investigations. Underlying this book series are six completed dissertations (Gonalves, Kozievitch, Leidig, Murthy, Shen, Torres), eight dissertations underway, and many masters theses. These reflect our experience with a long string of prototype or production systems developed in the lab, such as CITIDEL, CODER, CTRnet, Ensemble, ETANA, ETD-db, MARIAN, and Open Digital Libraries. There are hundreds of related publications, presentations, tutorials, and reports. We have built upon that work so this book, and the others in the series, will address digital library related needs in many computer science, information science, and library science (e.g., LIS) courses, as well as the requirements of researchers, developers, and practitioners. Much of the early work in the digital library field struck a balance between addressing real-world needs, integrating methods from related areas, and advancing an ever-expanding research agenda. Our work has fit in with these trends, but simultaneously has been driven by a desire to provide a firm conceptual and formal basis for the field.Our aim has been to move from engineering to science. We claim that our 5S (Societies, Scenarios, Spaces, Structures, Streams) framework, discussed in publications dating back to at least 1998, provides a suitable basis. This book introduces 5S, and the key theoretical and formal aspects of the 5S framework. While the 5S framework may be used to describe many types of information systems, and is likely to have even broader utility and appeal, we focus here on digital libraries. Our view of digital libraries is broad, so further generalization should be straightforward. We have connected with related fields, including hypertext/hypermedia, information storage and retrieval, knowledge management, machine learning, multimedia, personal information management, and Web 2.0. Applications have included managing not only publications, but also archaeological information, educational resources, fish images, scientific datasets, and scientific experiments/ simulations. Table of Contents: Introduction / Exploration / Mathematical Preliminaries / Minimal Digital Library / Archaeological Digital Libraries / 5S Results: Lemmas, Proofs, and 5SSuite / Glossary / Bibliography / Authors' Biographies / Index

Search User Interfaces (SUIs) represent the gateway between people who have a task to complete, and the repositories of information and data stored around the world. Not surprisingly, therefore, there are many communities who have a vested interest in the way SUIs are designed. There are people who study how humans search for information, and people who study how humans use computers. There are people who study good user interface design, and people who design aesthetically pleasing user interfaces. There are also people who curate and manage valuable information resources, and people who design effective algorithms to retrieve results from them. While it would be easy for one community to reject another for their limited ability to design a good SUI, the truth is that they all can, and they all have made valuable contributions. Fundamentally, therefore, we must accept that designing a great SUI means leveraging the knowledge and skills from all of these communities. The aim of this book is to at least acknowledge, if not integrate, all of these perspectives to bring the reader into a multidisciplinary mindset for how we should think about SUI design. Further, this book aims to provide the reader with a framework for thinking about how different innovations each contribute to the overall design of a SUI. With this framework and a multidisciplinary perspective in hand, the book then continues by reviewing: early, successful, established, and experimental concepts for SUI design. The book then concludes by discussing how we can analyse and evaluate the on-going developments in SUI design, as this multidisciplinary area of research moves forwards. Finally, in reviewing these many SUIs and SUI features, the book finishes by extracting a series of 20 SUI design recommendations that are listed in the conclusions. Table of Contents: Introduction / Searcher-Computer Interaction / Early Search User Interfaces / Modern Search User Interfaces / Experimental Search User Interfaces / Evaluating Search User Interfaces / Conclusions

Evaluation has always played a major role in information retrieval, with the early pioneers such as Cyril Cleverdon and Gerard Salton laying the foundations for most of the evaluation methodologies in use today. The retrieval community has been extremely fortunate to have such a well-grounded evaluation paradigm during a period when most of the human language technologies were just developing. This lecture has the goal of explaining where these evaluation methodologies came from and how they have continued to adapt to the vastly changed environment in the search engine world today. The lecture starts with a discussion of the early evaluation of information retrieval systems, starting with the Cranfield testing in the early 1960s, continuing with the Lancaster "e;user"e; study for MEDLARS, and presenting the various test collection investigations by the SMART project and by groups in Britain. The emphasis in this chapter is on the how and the why of the various methodologies developed. The second chapter covers the more recent "e;batch"e; evaluations, examining the methodologies used in the various open evaluation campaigns such as TREC, NTCIR (emphasis on Asian languages), CLEF (emphasis on European languages), INEX (emphasis on semi-structured data), etc. Here again the focus is on the how and why, and in particular on the evolving of the older evaluation methodologies to handle new information access techniques. This includes how the test collection techniques were modified and how the metrics were changed to better reflect operational environments. The final chapters look at evaluation issues in user studies -- the interactive part of information retrieval, including a look at the search log studies mainly done by the commercial search engines. Here the goal is to show, via case studies, how the high-level issues of experimental design affect the final evaluations. Table of Contents: Introduction and Early History / "e;Batch"e; Evaluation Since 1992 / Interactive Evaluation / Conclusion

Knowledge Management (KM) is an effort to increase useful knowledge in the organization. It is a natural outgrowth of late twentieth century movements to make organizational management and operations more effective, of higher quality, and more responsive to constituents in a rapidly changing global environment. This document traces the evolution of KM in organizations, summarizing the most influential research and literature in the field. It also presents an overview of selected common and current practices in knowledge management, including the relationship between knowledge management and decision making, with the intention of making a case for KM as a series of processes and not necessarily a manipulation of things. The final section highlights the use of social networking and commonly adopted Web applications to increase the value of social capital and to connect practitioners with clients and colleagues. Table of Contents: Introduction / Background Bibliographic Analysis / Theorizing Knowledge in Organizations / Conceptualizing Knowledge Emergence / Knowledge "e;Acts"e; / Knowledge Management in Practice / Knowledge Management Issues / Knowledge Management and Decision Making / Social Network Analysis and KM / Implications for the Future / Conclusion

We are poised at a major turning point in the history of information management via computers. Recent evolutions in computing, communications, and commerce are fundamentally reshaping the ways in which we humans interact with information, and generating enormous volumes of electronic data along the way. As a result of these forces, what will data management technologies, and their supporting software and system architectures, look like in ten years? It is difficult to say, but we can see the future taking shape now in a new generation of information access platforms that combine strategies and structures of two familiar -- and previously quite distinct -- technologies, search engines and databases, and in a new model for software applications, the Search-Based Application (SBA), which offers a pragmatic way to solve both well-known and emerging information management challenges as of now. Search engines are the world's most familiar and widely deployed information access tool, used by hundreds of millions of people every day to locate information on the Web, but few are aware they can now also be used to provide precise, multidimensional information access and analysis that is hard to distinguish from current database applications, yet endowed with the usability and massive scalability of Web search. In this book, we hope to introduce Search Based Applications to a wider audience, using real case studies to show how this flexible technology can be used to intelligently aggregate large volumes of unstructured data (like Web pages) and structured data (like database content), and to make that data available in a highly contextual, quasi real-time manner to a wide base of users for a varied range of purposes. We also hope to shed light on the general convergences underway in search and database disciplines, convergences that make SBAs possible, and which serve as harbingers of information management paradigms and technologies to come. Table of Contents: Search Based Applications / Evolving Business Information Access Needs / Origins and Histories / Data Models and Storage / Data Collection/Population / Data Processing / Data Retrieval / Data Security, Usability, Performance, Cost / Summary Evolutions and Convergences / SBA Platforms / SBA Uses and Preconditions / Anatomy of a Search Based Application / Case Study: GEFCO / Case Study: Urbanizer / Case Study: National Postal Agency / Future Directions

Information is essential to all human activity, and information in electronic form both amplifies and augments human information interactions. This lecture surveys some of the different classical meanings of information, focuses on the ways that electronic technologies are affecting how we think about these senses of information, and introduces an emerging sense of information that has implications for how we work, play, and interact with others. The evolutions of computers and electronic networks and people's uses and adaptations of these tools manifesting a dynamic space called cyberspace. Our traces of activity in cyberspace give rise to a new sense of information as instantaneous identity states that I term proflection of self. Proflections of self influence how others act toward us. Four classical senses of information are described as context for this new form of information. The four senses selected for inclusion here are the following: thought and memory, communication process, artifact, and energy. Human mental activity and state (thought and memory) have neurological, cognitive, and affective facets.The act of informing (communication process) is considered from the perspective of human intentionality and technical developments that have dramatically amplified human communication capabilities. Information artifacts comprise a common sense of information that gives rise to a variety of information industries. Energy is the most general sense of information and is considered from the point of view of physical, mental, and social state change. This sense includes information theory as a measurable reduction in uncertainty. This lecture emphasizes how electronic representations have blurred media boundaries and added computational behaviors that yield new forms of information interaction, which, in turn, are stored, aggregated, and mined to create profiles that represent our cyber identities. Table of Contents: The Many Meanings of Information / Information as Thought and Memory / Information as Communication Process / Information as Artifact / Information as Energy / Information as Identity in Cyberspace: The Fifth Voice / Conclusion and Directions

Many information retrieval (IR) systems suffer from a radical variance in performance when responding to users' queries. Even for systems that succeed very well on average, the quality of results returned for some of the queries is poor. Thus, it is desirable that IR systems will be able to identify "e;difficult"e; queries so they can be handled properly. Understanding why some queries are inherently more difficult than others is essential for IR, and a good answer to this important question will help search engines to reduce the variance in performance, hence better servicing their customer needs. Estimating the query difficulty is an attempt to quantify the quality of search results retrieved for a query from a given collection of documents. This book discusses the reasons that cause search engines to fail for some of the queries, and then reviews recent approaches for estimating query difficulty in the IR field. It then describes a common methodology for evaluating the prediction quality of those estimators, and experiments with some of the predictors applied by various IR methods over several TREC benchmarks. Finally, it discusses potential applications that can utilize query difficulty estimators by handling each query individually and selectively, based upon its estimated difficulty. Table of Contents: Introduction - The Robustness Problem of Information Retrieval / Basic Concepts / Query Performance Prediction Methods / Pre-Retrieval Prediction Methods / Post-Retrieval Prediction Methods / Combining Predictors / A General Model for Query Difficulty / Applications of Query Difficulty Estimation / Summary and Conclusions

Policy-based data management enables the creation of community-specific collections. Every collection is created for a purpose. The purpose defines the set of properties that will be associated with the collection. The properties are enforced by management policies that control the execution of procedures that are applied whenever data are ingested or accessed. The procedures generate state information that defines the outcome of enforcing the management policy. The state information can be queried to validate assessment criteria and verify that the required collection properties have been conserved. The integrated Rule-Oriented Data System implements the data management framework required to support policy-based data management. Policies are turned into computer actionable Rules. Procedures are composed from a Micro-service-oriented architecture. The result is a highly extensible and tunable system that can enforce management policies, automate administrative tasks, and periodically validate assessment criteria. Table of Contents: Introduction / Integrated Rule-Oriented Data System / iRODS Architecture / Rule-Oriented Programming / The iRODS Rule System / iRODS Micro-services / Example Rules / Extending iRODS / Appendix A: iRODS Shell Commands / Appendix B: Rulegen Grammar / Appendix C: Exercises / Author Biographies

Today, Web search is treated as a solitary experience. Web browsers and search engines are typically designed to support a single user, working alone. However, collaboration on information-seeking tasks is actually commonplace. Students work together to complete homework assignments, friends seek information about joint entertainment opportunities, family members jointly plan vacation travel, and colleagues jointly conduct research for their projects. As improved networking technologies and the rise of social media simplify the process of remote collaboration, and large, novel display form-factors simplify the process of co-located group work, researchers have begun to explore ways to facilitate collaboration on search tasks. This lecture investigates the who, what, where, when and why of collaborative search, and gives insight in how emerging solutions can address collaborators' needs. Table of Contents: Introduction / Who? / What? / Where? / When? / Why? / Conclusion: How?

At its very core multimedia information retrieval means the process of searching for and finding multimedia documents; the corresponding research field is concerned with building the best possible multimedia search engines. The intriguing bit here is that the query itself can be a multimedia excerpt: For example, when you walk around in an unknown place and stumble across an interesting landmark, would it not be great if you could just take a picture with your mobile phone and send it to a service that finds a similar picture in a database and tells you more about the building -- and about its significance, for that matter? This book goes further by examining the full matrix of a variety of query modes versus document types. How do you retrieve a music piece by humming? What if you want to find news video clips on forest fires using a still image? The text discusses underlying techniques and common approaches to facilitate multimedia search engines from metadata driven retrieval, via piggy-back text retrieval where automated processes create text surrogates for multimedia, automated image annotation and content-based retrieval. The latter is studied in great depth looking at features and distances, and how to effectively combine them for efficient retrieval, to a point where the readers have the ingredients and recipe in their hands for building their own multimedia search engines. Supporting users in their resource discovery mission when hunting for multimedia material is not a technological indexing problem alone. We look at interactive ways of engaging with repositories through browsing and relevance feedback, roping in geographical context, and providing visual summaries for videos. The book concludes with an overview of state-of-the-art research projects in the area of multimedia information retrieval, which gives an indication of the research and development trends and, thereby, a glimpse of the future world. Table of Contents: What is Multimedia Information Retrieval? / Basic Multimedia Search Technologies / Content-based Retrieval in Depth / Added Services / Multimedia Information Retrieval Research / Summary