When Don Swanson hypothesized a connection between Raynaud's phenomenon anddietary?shoil, the?eldofliterature-baseddiscovery(LBD)wasborn. During thesubsequenttwodecadesasteadystreamofresearchershavepublishedarticles aboutLBDandthe?eldhasmadesteadyprogressinlayingfoundationsandc- ating an identity. It is curiously signi?cant that LBD is not "owned" by any p- ticulardiscipline, forexample, knowledge discoveryortextmining. Rather, LBD researchersoriginatefromarangeof?eldsincludinginformationscience, infor- tionretrieval, logic, andthebiomedicalsciences. Thisre?ectsthefactLBDisan inherentlymulti-disciplinaryenterprisewherecollaborationsbetweentheinfor- tionandbiomedicalsciencesarereadilyencountered. Thismulti-disciplinaryaspect ofLBDhasmadeitharderforthe?eldtoplanta?ag, sotospeak. Thepresentv- umecanbeseenasanattempttoredressthis. Itpresentschaptersprovidingabroad brushstrokeofLBDbyleadingresearchersprovidinganoverviewofthestateofthe art, themodelsandtheoriesused, experimentalstudies, lessonslearnt, application areas, andfuturechallenges. Inshort, itattemptstoconveyalearnedimpressionof whereandhowLBDisbeingdeployed. DonSwansonhaskindlyagreedtoprovide theintroductorychapter. Itisthehopeandintentionthatthisvolumewillplanta ?aginthegroundandinspirenewresearcherstotheLBDchallenge. PeterBruza July2007 MarcWeeber v Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Part I General Outlook and Possibilities Literature-Based Discovery? The Very Idea . . . . . . . . . . . . . . . . . . . . . . . . . 3 D. R. Swanson The Place of Literature-Based Discovery in Contemporary Scienti?c Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 NeilR. SmalheiserandVetleI. Torvik The Tip of the Iceberg: The Quest for Innovation at the Base of the Pyramid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 M. D. GordonandN. F. Awad The 'Open Discovery' Challenge. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 JonathanD. Wren Where is the Discovery in Literature-Based Discovery?. . . . . . . . . . . . . . . . 57 R. N. Kostoff Part II Methodology and Applications Analyzing LBD Methods using a General Framework. . . . . . . . . . . . . . . . . 75 A. K. Sehgal, X. Y. Qiu, andP. Srinivasan Evaluation of Literature-Based Discovery Systems. . . . . . . . . . . . . . . . . . . . 101 M. Yetisgen-YildizandW. Pratt vii viii Contents Factor Analytic Approach to Transitive Text Mining using Medline Descriptors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 J. StegmannandG. Grohmann Literature-Based Knowledge Discovery using Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 D. Hristovski, C. Friedman, T. C. Rind?esch, andB. Peterlin Information Retrieval in Literature-Based Discovery. . . . . . . . . . . . . . . . . . 153 W. Hersh Biomedical Application of Knowledge Discovery . . . . . . . . . . . . . . . . . . . . . 173 A. Koike Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Contributors NeveenFaragAwad SchoolofBusiness, WayneStateUniversity, USA CarolFriedman

This book presents the Recommender System for Improving Customer Loyalty. New and innovative products have begun appearing from a wide variety of countries, which has increased the need to improve the customer experience. When a customer spends hundreds of thousands of dollars on a piece of equipment, keeping it running efficiently is critical to achieving the desired return on investment. Moreover, managers have discovered that delivering a better customer experience pays off in a number of ways. A study of publicly traded companies conducted by Watermark Consulting found that from 2007 to 2013, companies with a better customer service generated a total return to shareholders that was 26 points higher than the S&P 500. This is only one of many studies that illustrate the measurable value of providing a better service experience. The Recommender System presented here addresses several important issues. (1) It provides a decision framework to help managers determine which actions are likely to have the greatest impact on the Net Promoter Score. (2) The results are based on multiple clients. The data mining techniques employed in the Recommender System allow users to "learn" from the experiences of others, without sharing proprietary information. This dramatically enhances the power of the system. (3) It supplements traditional text mining options. Text mining can be used to identify the frequency with which topics are mentioned, and the sentiment associated with a given topic. The Recommender System allows users to view specific, anonymous comments associated with actual customers. Studying these comments can provide highly accurate insights into the steps that can be taken to improve the customer experience. (4) Lastly, the system provides a sensitivity analysis feature. In some cases, certain actions can be more easily implemented than others. The Recommender System allows managers to "weigh" these actions and determine which ones would have a greater impact.

Readers will progress from an understanding of what the Internet is now towards an understanding of the motivations and techniques that will drive its future.

Over the years, advances in the business world as well as the changing of diverse application contexts, have caused Data Warehousing and Data Mining to become more paramount in our society. The two share many common issues and are commonly interrelated. Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches provides a comprehensive compilation of knowledge covering state-of-the-art developments and research, as well as current innovative activities in data warehousing and mining. This book focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real world problems and provides a broad perspective on the future of these two cohesive topic areas.

th It is fitting that there was a World Computer Congress in the 50 anniversary year of IFIP. Within the Learn IT Stream of WCC2010, the conference, Key Competencies in the Knowledge Society (KCKS), brought together some 43 papers from around the world covering many areas of ICT and its role in education. Of the papers presented here, three were selected as key theme papers for the KCKS conference. These papers' by Adams and Tatnall, Tarrago and Wilson, Diethelm and Dorge, are included in these proceedings. We congratulate these authors for the quality of their work that led to selection. The range of issues covered within this volume is too broad to set out here but c- ers, amongst other things, e-examination, Twitter, teacher education, school-based learning, methodological frameworks and human development theories. It has been an exciting and rewarding task to put these papers together. They rep- sent a coming together of great minds and cutting-edge research. We thank our contributors and our reviewers for producing such an impressive body of work."

The World Wide Web can be considered a huge library that in consequence needs a capable librarian responsible for the classification and retrieval of documents as well as the mediation between library resources and users. Based on this idea, the concept of the "Librarian of the Web" is introduced which comprises novel, librarian-inspired methods and technical solutions to decentrally search for text documents in the web using peer-to-peer technology. The concept's implementation in the form of an interactive peer-to-peer client, called "WebEngine", is elaborated on in detail. This software extends and interconnects common web servers creating a fully integrated, decentralised and self-organising web search system on top of the existing web structure. Thus, the web is turned into its own powerful search engine without the need for any central authority. This book is intended for researchers and practitioners having a solid background in the fields of Information Retrieval and Web Mining.

Anyone with a computer has heard of viruses, had to deal with several, and has been struggling with spam, spyware, and disk crashes. This book is intended as a starting point for those familiar with basic concepts of computers and computations and who would like to extend their knowledge into the realm of computer and network security. Its comprehensive treatment of all the major areas of computer security aims to give readers a complete foundation in the field of Computer Security. Exercises are given throughout the book and are intended to strengthening the readera (TM)s knowledge - answers are also provided.

Written in a clear, easy to understand style, aimed towards advanced undergraduates and non-experts who want to know about the security problems confronting them everyday. The technical level of the book is low and requires no mathematics, and only a basic concept of computers and computations. Foundations of Computer Security will be an invaluable tool for students and professionals alike.

"Computational Analysis of Terrorist Groups: Lashkar-e-Taiba "provides an in-depth look at Web intelligence, and how advanced mathematics and modern computing technology can influence the insights we have on terrorist groups. This book primarily focuses on one famous terrorist group known as Lashkar-e-Taiba (or LeT), and how it operates.After 10 years of counter Al Qaeda operations, LeT is considered by many in the counter-terrorism community to be an even greater threat to the US and world peace than Al Qaeda.

"Computational Analysis of Terrorist Groups: Lashkar-e-Taiba "is the first book that demonstrates how to use modern computational analysis techniques including methods for "big data" analysis. This book presents how to quantify both the environment in which LeT operate, and the actions it took over a 20-year period, and represent it as a relational database table. This table is then mined using sophisticated data mining algorithms in order to gain detailed, mathematical, computational and statistical insights into LeT and its operations.This book also provides a detailed history of Lashkar-e-Taiba based on extensive analysis conducted by using open source information and public statements. Each chapter includes a case study, as well as a slide describing the key results which are available on the authors' web sites.

"Computational Analysis of Terrorist Groups: Lashkar-e-Taiba "is designed for a professional market composed of government or military workers, researchers and computer scientists working in the web intelligence field. Advanced-level students in computer science will also find this valuable as a reference book."

Business process reengineering (BPR) focuses on redesigning the strategic and value-added processes which transcend the organizational boundaries. It is a cross-functional approach that requires support from almost all the departments of the organization. Business Process Reengineering: Automation Decision Points in Process Reengineering offers a new framework based process reengineering and links it to organization life cycle, process life cycle, and process management. This volume describes the fundamental concepts behind business process reengineering and examines them through case studies, and should appeal to researchers and academics interested in business process reengineering, operations strategy, and organizational restructuring and design.

The development of net-centric approaches for intelligence and national security applications has become a major concern in many areas such as defense, intelligence and national and international law enforcement agencies. In this volume we consider the web architectures and recent developments that make n- centric approaches for intelligence and national security possible. These include developments in information integration and recent advances in web services including the concept of the semantic web. Discovery, analysis and management of web-available data pose a number of interesting challenges for research in w- based management systems. Intelligent agents and data mining are some of the techniques that can be employed. A number of specific systems that are net-centric based in various areas of military applications, intelligence and law enforcement are presented that utilize one or more of such techniques The opening chapter overviews the concepts related to ontologies which now form much of the basis of the possibility of sharing of information in the Semantic Web. In the next chapter an overview of Web Services and examples of the use of Web Services for net-centric operations as applied to meteorological and oceanographic (MetOc) data is presented and issues related to the Navy's use of MetOc Web Services are discussed. The third chapter focuses on metadata as conceived to support the concepts of a service-oriented architecture and, in particular, as it relates to the DoD Net-Centric Data Strategy and the NCES core services.

The recent advances in display technologies and mobile devices is having an important effect on the way users interact with all kinds of devices (computers, mobile devices, laptops, tablets, and so on). These are opening up new possibilities for interaction, including the distribution of the UI (User Interface) amongst different devices, and implies that the UI can be split and composed, moved, copied or cloned among devices running the same or different operating systems. These new ways of manipulating the UI are considered under the emerging topic of Distributed User Interfaces (DUIs). DUIs are concerned with the repartition of one of many elements from one or many user interfaces in order to support one or many users to carry out one or many tasks on one or many domains in one or many contexts of use - each context of use consisting of users, platforms, and environments. The 20 chapters in the book cover between them the state-of-the-art, the foundations, and original applications of DUIs. Case studies are also included, and the book culminates with a review of interesting and novel applications that implement DUIs in different scenarios.

Chaos Synchronization and Cryptography for Secure Communications: Applications for Encryption explores the combination of ordinary and time delayed systems and their applications in cryptographic encoding. This innovative publication presents a critical mass of the most sought after research, providing relevant theoretical frameworks and the latest empirical research findings in this area of study.

During the 21st century business environments have become more complex and dynamic than ever before. Companies operate in a world of change influenced by globalisation, volatile markets, legal changes and technical progress. As a result, they have to handle growing volumes of data and therefore require fast storage, reliable data access, intelligent retrieval of information and automated decision-making mechanisms, all provided at the highest level of service quality. Successful enterprises are aware of these challenges and efficiently respond to the dynamic environment in which their business operates. Business Intelligence (BI) and Performance Management (PM) offer solutions to these challenges and provide techniques to enable effective business change. The important aspects of both topics are discussed within this state-of-the-art volume. It covers the strategic support, business applications, methodologies and technologies from the field, and explores the benefits, issues and challenges of each. Issues are analysed from many different perspectives, ranging from strategic management to data technologies, and the different subjects are complimented and illustrated by numerous examples of industrial applications. Contributions are authored by leading academics and practitioners representing various universities, research centres and companies worldwide. Their experience covers multiple disciplines and industries, including finance, construction, logistics, and public services, amongst others. Business Intelligence and Performance Management is a valuable source of reference for graduates approaching MSc or PhD programs and for professionals in industry researching in the fields of BI and PM for industrial application.

Advances in social science research methodologies and data analytic methods are changing the way research in information systems is conducted. New developments in statistical software technologies for data mining (DM) such as regression splines or decision tree induction can be used to assist researchers in systematic post-positivist theory testing and development. Established management science techniques like data envelopment analysis (DEA), and value focused thinking (VFT) can be used in combination with traditional statistical analysis and data mining techniques to more effectively explore behavioral questions in information systems research. As adoption and use of these research methods expand, there is growing need for a resource book to assist doctoral students and advanced researchers in understanding their potential to contribute to a broad range of research problems.

"Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking" focuses on bridging and unifying these three different methodologies in order to bring them together in a unified volume for the information systems community. This book serves as a resource that provides overviews on each method, as well as applications on how they can be employed to address IS research problems. Its goal is to help researchers in their continuous efforts to set the pace for having an appropriate interplay between behavioral research and design science.

Web mining has become a popular area of research, integrating the different research areas of data mining and the World Wide Web. According to the taxonomy of Web mining, there are three sub-fields of Web-mining research: Web usage mining, Web content mining and Web structure mining. These three research fields cover most content and activities on the Web. With the rapid growth of the World Wide Web, Web mining has become a hot topic and is now part of the mainstream of Web - search, such as Web information systems and Web intelligence. Among all of the possible applications in Web research, e-commerce and e-services have been iden- fied as important domains for Web-mining techniques. Web-mining techniques also play an important role in e-commerce and e-services, proving to be useful tools for understanding how e-commerce and e-service Web sites and services are used, e- bling the provision of better services for customers and users. Thus, this book will focus upon Web-mining applications in e-commerce and e-services. Some chapters in this book are extended from the papers that presented in WMEE 2008 (the 2nd International Workshop for E-commerce and E-services). In addition, we also sent invitations to researchers that are famous in this research area to contr- ute for this book. The chapters of this book are introduced as follows: In chapter 1, Peter I.

This book provides a general and comprehensible overview of supervised descriptive pattern mining, considering classic algorithms and those based on heuristics. It provides some formal definitions and a general idea about patterns, pattern mining, the usefulness of patterns in the knowledge discovery process, as well as a brief summary on the tasks related to supervised descriptive pattern mining. It also includes a detailed description on the tasks usually grouped under the term supervised descriptive pattern mining: subgroups discovery, contrast sets and emerging patterns. Additionally, this book includes two tasks, class association rules and exceptional models, that are also considered within this field. A major feature of this book is that it provides a general overview (formal definitions and algorithms) of all the tasks included under the term supervised descriptive pattern mining. It considers the analysis of different algorithms either based on heuristics or based on exhaustive search methodologies for any of these tasks. This book also illustrates how important these techniques are in different fields, a set of real-world applications are described. Last but not least, some related tasks are also considered and analyzed. The final aim of this book is to provide a general review of the supervised descriptive pattern mining field, describing its tasks, its algorithms, its applications, and related tasks (those that share some common features). This book targets developers, engineers and computer scientists aiming to apply classic and heuristic-based algorithms to solve different kinds of pattern mining problems and apply them to real issues. Students and researchers working in this field, can use this comprehensive book (which includes its methods and tools) as a secondary textbook.

Researchers in data management have recently recognized the importance of a new class of data-intensive applications that requires managing data streams, i.e., data composed of continuous, real-time sequence of items. Streaming applications pose new and interesting challenges for data management systems. Such application domains require queries to be evaluated continuously as opposed to the one time evaluation of a query for traditional applications. Streaming data sets grow continuously and queries must be evaluated on such unbounded data sets. These, as well as other challenges, require a major rethink of almost all aspects of traditional database management systems to support streaming applications.

Stream Data Management comprises eight invited chapters by researchers active in stream data management. The collected chapters provide exposition of algorithms, languages, as well as systems proposed and implemented for managing streaming data.

Stream Data Management is designed to appeal to researchers or practitioners already involved in stream data management, as well as to those starting out in this area. This book is also suitable for graduate students in computer science interested in learning about stream data management.

The aim of this book is to illustrate that advanced fuzzy clustering algorithms can be used not only for partitioning of the data. It can also be used for visualization, regression, classification and time-series analysis, hence fuzzy cluster analysis is a good approach to solve complex data mining and system identification problems. This book is oriented to undergraduate and postgraduate and is well suited for teaching purposes.

The objective of this book is to introduce the basic concepts of big data computing and then to describe the total solution of big data problems using HPCC, an open-source computing platform. The book comprises 15 chapters broken into three parts. The first part, Big Data Technologies, includes introductions to big data concepts and techniques; big data analytics; and visualization and learning techniques. The second part, LexisNexis Risk Solution to Big Data, focuses on specific technologies and techniques developed at LexisNexis to solve critical problems that use big data analytics. It covers the open source High Performance Computing Cluster (HPCC Systems (R)) platform and its architecture, as well as parallel data languages ECL and KEL, developed to effectively solve big data problems. The third part, Big Data Applications, describes various data intensive applications solved on HPCC Systems. It includes applications such as cyber security, social network analytics including fraud, Ebola spread modeling using big data analytics, unsupervised learning, and image classification. The book is intended for a wide variety of people including researchers, scientists, programmers, engineers, designers, developers, educators, and students. This book can also be beneficial for business managers, entrepreneurs, and investors.

Dynamic programming is an efficient technique for solving optimization problems. It is based on breaking the initial problem down into simpler ones and solving these sub-problems, beginning with the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. This book develops extensions of dynamic programming, enabling us to (i) describe the set of objects under consideration; (ii) perform a multi-stage optimization of objects relative to different criteria; (iii) count the number of optimal objects; (iv) find the set of Pareto optimal points for bi-criteria optimization problems; and (v) to study relationships between two criteria. It considers various applications, including optimization of decision trees and decision rule systems as algorithms for problem solving, as ways for knowledge representation, and as classifiers; optimization of element partition trees for rectangular meshes, which are used in finite element methods for solving PDEs; and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths. The results presented are useful for researchers in combinatorial optimization, data mining, knowledge discovery, machine learning, and finite element methods, especially those working in rough set theory, test theory, logical analysis of data, and PDE solvers. This book can be used as the basis for graduate courses.

Details recent research in areas such as ontology design for information integration, metadata generation and management, and representation and management of distributed ontologies.

Provides decision support on the use of novel technologies, information about potential problems, and guidelines for the successful application of existing technologies.

This book presents a review of traditional context-aware computing research, identifies its limitations in developing social context-aware pervasive systems, and introduces a new technology framework to address these limitations. Thus, this book provides a good reference for developments in context-aware computing and pervasive social computing. It examines the emerging area of pervasive social computing, which is a novel collective paradigm derived from pervasive computing, social media, social networking, social signal processing and multimodal human-computer interaction. This book offers a novel approach to model, represent, reason about and manage different types of social context. It shows how users' social context information can be acquired from different online social networks such as Facebook, LinkedIn, Twitter and Google Calendar. It further presents the use of social context information in developing innovative smart mobile applications to assist users in their daily life. The mix of both theoretical and applied research results makes this book attractive to a variety of readers from both academia and industry. This book provides a new platform for implementing different types of socially-aware mobile applications. The platform hides the complexity of managing social context, and thus provides essential support to application developers for the development of socially-aware applications. The book contains detailed descriptions of how the underlying platform has been implemented using available technologies such as ontology and rule engines, and how this platform can be used to develop socially-aware mobile applications using two exemplar applications. The book also presents evaluations of the proposed platform and applications using real-world data from Facebook, LinkedIn and Twitter. Therefore, this book is a syndication of scientific research with practical industrial applications, making it useful to researchers as well as to software engineers.