Information Retrieval - Books Available for Review

Handbook of Research on Web Log Analysis
Edited by: Bernard J. Jansen, Penn State University, USA, Amanda Spink, Queensland University of Technology, Australia, and Isak Taksa, Baruch College, City University of New York, USA

The Handbook of Research on Web Log Analysis reflects on the multifaceted themes of Web use and presents various approaches to log analysis. This expansive collection reviews the history of Web log analysis and examines new trends including the issues of privacy, social interaction and community building. Over 20 research contributions from international experts comprehensively cover the latest user-behavior analytic and log analysis methodologies, and consider new research directions and novel applications. An essential holding for library reference collections, this Handbook of Research will benefit academics, researchers, and students in a variety of fields, as well as technology professionals interested in the opportunities and challenges presented by the massive collection of Web usage data.

Doing Things with Information: Beyond Indexing and Abstracting
Brian O'Connor, Jodi Kearns and Richard L. Anderson

The relationship between a person with a question and a source of information is complex. Indexing and abstracting often fail because too much emphasis is put on the mechanics of description and too little on what ought to be represented. Research literature suggests that inappropriate representation results in failed searches a significant number of times, perhaps even in a majority of cases. Doing Things with Information seeks to rectify this unfortunate situation by emphasizing methods of modeling and constructing appropriate representations of such questions and documents. Students in programs of information studies will find focal points for discussion about system design and refinement of existing systems. Librarians, scholars, and those who work within large document collections, whether paper or electronic, will find insights into the strengths and weaknesses of the access systems they use.

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

Survey of text mining II : clustering, classification, and retrieval
Berry, Michael; Castellanos, Malu

The proliferation of digital computing devices and their use in communication has resulted in an increased demand for systems and algorithms capable of mining textual data. Thus, the development of techniques for mining unstructured, semi-structured, and fully-structured textual data has become increasingly important in both academia and industry. This second volume continues to survey the evolving field of text mining - the application of techniques of machine learning, in conjunction with natural language processing, information extraction and algebraic/mathematical approaches, to computational information retrieval. Numerous diverse issues are addressed, ranging from the development of new learning approaches to novel document clustering algorithms, collectively spanning several major topic areas in text mining. Features: Х Acts as an important benchmark in the development of current and future approaches to mining textual information Х Serves as an excellent companion text for courses in text and data mining, information retrieval and computational statistics Х Experts from academia and industry share their experiences in solving large-scale retrieval and classification problems Х Presents an overview of current methods and software for text mining Х Highlights open research questions in document categorization and clustering, and trend detection Х Describes new application problems in areas such as email surveillance and anomaly detection Survey of Text Mining II offers a broad selection in state-of-the art algorithms and software for text mining from both academic and industrial perspectives, to generate interest and insight into the state of the field. This book will be an indispensable resource for researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining.

Computational Methods of Feature Selection
Li, Huan; Motoda, Hiroshi

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool. The book begins by exploring unsupervised, randomized, and causal feature selection. It then reports on some recent results of empowering feature selection, including active feature selection, decision-border estimate, the use of ensembles with independent probes, and incremental feature selection. This is followed by discussions of weighting and local methods, such as the ReliefF family, k-means clustering, local feature relevance, and a new interpretation of Relief. The book subsequently covers text classification, a new feature selection score, and both constraint-guided and aggressive feature selection. The final section examines applications of feature selection in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection. Through a clear, concise, and coherent presentation of topics, this volume systematically covers the key concepts, underlying principles, and inventive applications of feature selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable information.

Data Mining with Ontologies: Implementations, Findings, and Frameworks
Nigro, H.E.; Cisaro, S.E.G. and Xodo, D.H.

One of the most important and challenging problems in data mining is the definition of prior knowledge either from the process or the domain. Prior knowledge is helpful for selecting suitable data and mining techniques, pruning the space of hypothesis, representing the output in a comprehensible way, and improving the overall method. Data Mining with Ontologies: Implementations, Findings and Frameworks provides a comprehensive set of methodologies and tools for the development of ontological foundations for data mining in diverse domains ranging from biomedicine to marketing. Forming a benchmark reference for future efforts to enhance capabilities in ontology utilization and design, this Premier Reference Source will be an invaluable addition to libraries worldwide.

Successes and New Directions in Data Mining
Poncelet, P., Masseglia, F. and Teisseire, M.

The problem of mining patterns is becoming a very active research area and efficient techniques have been widely applied to problems in industry, government, and science. From the initial definition and motivated by real-applications, the problem of mining patterns not only addresses the finding of itemsets but also more and more complex patterns. Successes and New Directions in Data Mining addresses existing solutions for data mining, with particular emphasis on potential real-world applications. Capturing defining research on topics such as fuzzy set theory, clustering algorithms, semi-supervised clustering, modeling and managing data mining patterns, and sequence motif mining, this book is an indispensable resource for library collections.

Semantic-Based Visual Information Retrieval
Zhang, Yu-Zin

Semantic-Based Visual Information Retrieval is one of the most challenging research directions of content-based visual information retrieval. It provides efficient tools for access, interaction, searching, and retrieving from collected databases of visual media. Building on research from over 30 leading experts from around the world, Semantic-Based Visual Information Retrieval presents state-of-the-art advancements and developments in the field, and also brings a selection of techniques and algorithms about semantic-based visual information retrieval. It covers many critical issues, such as: multi-level representation and description, scene understanding, semantic modeling, image and video annotation, human-computer interaction, and more. Semantic-Based Visual Information Retrieval also explains detailed solutions to a wide range of practical applications. Researchers, students, and practitioners will find this comprehensive and detailed volume to be a roadmap for applying suitable methods in semantic-based visual information retrieval.

Emerging Technologies of Text Mining: Techniques and Applications
do Prado, H.A. and Ferneda, E.

Massive amounts of textual data make up most organizationsТ stored information. Therefore, there is increasingly high demand for a comprehensive resource providing practical hands-on knowledge for real-world applications. Emerging Technologies of Text Mining: Techniques and Applications provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, Emerging Technologies of Text Mining: Techniques and Applications will provide libraries with the defining reference on this topic.

Introduction to Clustering Large and High-Dimensional Data
Kogan, J.

There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.

Rather than providing comprehensive coverage of the area, the book focuses on a few important clustering algorithms. A detailed and elementary description of the algorithms is provided in the beginning chapters, to be easily absorbed by undergraduates. Recent research results involving sophisticated mathematics are of interest for graduate students and research experts

Computing Attitude and Affect in Text: Theory and Applications
Shanahan, J.G; Qu, Yan and Wiebe, Janyce (Eds.)

Human Language Technology (HLT) and Natural Language Processing (NLP) systems have typically focused on the "factual" aspect of content analysis. Other aspects, including pragmatics, opinion, and style, have received much less attention. However, to achieve an adequate understanding of a text, these aspects cannot be ignored. The chapters in this book address attitude, affect, and subjective opinion. Various conceptual models and computational methods are presented, including distinguishing attitudes from simple factual assertions; distinguishing between the authorТs reports from reports of other peopleТs opinions; and distinguishing between explicitly and implicitly stated attitudes. In addition, many applications are described that promise to benefit from the ability to understand attitudes and affect, such as indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups; analyzing client discourse in therapy and counseling; determining relations between scientific texts; generating more appropriate texts; and creating writersТ aids. In addition to English texts, the collection includes studies of French, Japanese, and Portuguese texts. The chapters in this book are extended and revised versions of papers presented at the American Association for Artificial Intelligence (AAAI) Spring Symposium on Exploring Attitude and Affect in Text, which took place in March 2004 at Stanford University. The symposium, and the book which grew out it, represents a first foray into this area and a balance among conceptual models, computational methods, and applications. Written for: Advanced undergraduate students, graduate students, professionals, and researchers in computer science, engineering, information science, and content analysis with an interest in the subjective aspects of text

Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
Markov, Z. and Larose, D.T.

This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).

Multimedia Data Mining and Knowledge Discovery
Petrushin, V.A. and Khan, L. (Eds.)

Multimedia Data Mining and Knowledge Discovery, assembling the work of leading academic and professional/industrial researchers worldwide, provides an overview of the current state-of-the-art in the field of multimedia data mining and knowledge discovery, and discusses the variety of hot topics in multimedia data mining research. Consisting of an introductory section and four topical parts, the book describes the objectives and current tendencies in multimedia data mining research and their applications. Each part contains an overview of its chapters and leads the reader with a structured approach through the diverse subjects in the field. Written with graduate students in mind, this much needed comprehensive survey of the current state of multimedia data mining and knowledge discovery will also serve as a valuable resource for researchers with interests in multimedia data mining, summarization, indexing, and retrieval.

Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization (2nd. edition)
Jackson, P. and Moulinier, I.

This text covers the technologies of document retrieval, information extraction, and text categorization in a way which highlights commonalities in terms of both general principles and practical concerns. It assumes some mathematical background on the part of the reader, but the chapters typically begin with a non-mathematical account of the key issues. Current research topics are covered only to the extent that they are informing current applications; detailed coverage of longer term research and more theoretical treatments should be sought elsewhere. There are many pointers at the ends of the chapters that the reader can follow to explore the literature. However, the book does maintain a strong emphasis on evaluation in every chapter both in terms of methodology and the results of controlled experimentation.

Multimedia Retrieval
Blanken, H.M.; de Vries, A.P.; Blok, H.E.; Feng, L. (Eds.)

There are few authors in this subject area who can lay claim to as much pedagogical experience as those responsible for this excellent text. Based on more than 10 years of teaching experience, Blanken and his coeditors have assembled all the topics that should be covered in advanced undergraduate or graduate courses on multimedia retrieval and multimedia databases. The single chapters of this textbook explain the general architecture of multimedia information retrieval systems and cover various metadata languages such as Dublin Core, RDF, or MPEG. The book covers, among other subjects, pattern recognition through Markov models, unsupervised learning, and pattern clustering. It also delineates in detail various indexing approaches to audio and video streams; interaction and control; the protection of content and user privacy; and search effectiveness and efficiency. The authors emphasize high-level features and show how these are used in mathematical models to support the retrieval process. For each chapter, thereТs detail on further reading, and additional exercises and teaching material is available online.

This book provides a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered. The book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Internet support with lecture slides and project problems is available online.

Lucene In Action
Hatcher, E.

Lucene is a gem in the open-source world--a highly scalable, fast search engine. It delivers performance and is disarmingly easy to use. Lucene in Action is the authoritative guide to Lucene. It describes how to index your data, including types you definitely need to know such as MS Word, PDF, HTML, and XML. It introduces you to searching, sorting, filtering, and highlighting search results. Lucene powers search in surprising places--in discussion groups at Fortune 100 companies, in commercial issue trackers, in email search from Microsoft, in the Nutch web search engine (that scales to billions of pages). It is used by diverse companies including Akamai, Overture, Technorati, HotJobs, Epiphany, FedEx, Mayo Clinic, MIT, New Scientist Magazine, and many others. Adding search to your application can be easy. With many reusable examples and good advice on best practices, Lucene in Action shows you how. And if you would like to search through Lucene in Action over the Web, you can do so using Lucene itself as the search engine--take a look at the authors' awesome Search Inside solution. Its results page resembles Google's and provides a novel yet familiar interface to the entire book and book blog.

Enabling Semantic Web Services: The Web Service Modeling Ontology
Fensel, D., Lausen, H., Polleres, A., Bruijn, J.d., Stollberg, M., Roman, D., Domingue, J.

Service-oriented computing has become one of the predominant factors in current IT research and development. Web services seem to be the middleware solution of the future for highly interoperable distributed software solutions. In parallel, research on the Semantic Web provides the results required to exploit distributed machine-processable data. To combine these two research lines into industrial-strength applications, a number of research projects have been set up by organizations like W3C and the EU. Dieter Fensel and his coauthors deliver a profound introduction into one of the most promising approaches Ц the Web Service Modeling Ontology (WSMO). After a brief presentation of the underlying basic technologies and standards of the World Wide Web, the Semantic Web, and Web Services, they detail all the elements of WSMO from basic concepts to possible applications in e-commerce, e-government and e-banking, and they also describe its relation to other approaches like OWL-S or WSDL-S. While many of the related technologies and standards are still under development, this book already offers both a broad conceptual introduction and lots of pointers to future application scenarios for researchers in academia and industry as well as for developers of distributed Web applications.

Web Data Management Practices: Emerging Techniques and Technologies
Athena Vakali and George Pallis

New state-of-the-art techniques for analyzing and managing Web data have emerged due to the need for dealing with huge amounts of data which are circulated on the Web. Web Data Management Practices: Emerging Techniques and Technologies provides a thorough understanding of major issues, current practices, and the main ideas in the field of Web data management, helping readers to identify current and emerging issues, as well as future trends in this area. Web Data Management Practices: Emerging Techniques and Technologies presents a complete overview of important aspects related to Web data management practices, such as: Web mining, Web data clustering, and others. This book also covers an extensive range of topics, including related issues about Web mining, Web caching and replication, Web services, and the XML standard.

Google's Page Rank and Beyond: The Science of Search Engine Rankings
Langville, A. & Meyer, C.

Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, Google's PageRank and Beyond supplies the answers to these and other questions and more.
The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other chapters are much more mathematical in nature, each one contains something for both audiences. For example, the authors include entertaining asides such as how search engines make money and how the Great Firewall of China influences research. The book includes an extensive background chapter designed to help readers learn more about the mathematics of search engines, and it contains several MATLAB codes and links to sample Web data sets. The philosophy throughout is to encourage readers to experiment with the ideas and algorithms in the text. Any business seriously interested in improving its rankings in the major search engines can benefit from the clear examples, sample code, and list of resources provided. * Many illustrative examples and entertaining asides * MATLAB code * Accessible and informal style * Complete and self-contained section for mathematics review

Distributed Multimedia Retrieval Strategies for Large Scale Networked Systems
Veeravalli, B. & Barlas, G.

Several works on multimedia storage appear in literature today, but very little if any, have been devoted to handling long duration video retrieval, over large scale networks. Distributed retrieval of multimedia documents, especially the long duration documents, is an imperative step in rendering high-quality, high-fidelity, and cost-effective services for network service providers. Distributed Multimedia Retrieval Strategies for Large Scale Networked Systems presents an up-to-date research status in the domain of distributed video retrieval. This professional book will include several different techniques that are in place for long duration video retrieval. An experimentally tested technology under the JINI platform, demonstrates a practical working system which serves as a feasibility study, as well as the first step in realizing such a technology.

Computational Linguistics and Intelligent Text Processing
Alexander Gelbukh

This book constitutes the refereed proceedings of the 7th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2006, held in Mexico City, Mexico, in February 2006. The 43 revised full papers and 16 revised short papers presented together with 3 invited papers were carefully reviewed and selected from 176 submissions. The papers are structured into two parts and organized beyond in topical sections on computational linguistics research: lexical resources, corpus-based knowledge acquisition, morphology and part-of-speech tagging, syntax and parsing, word sense disambiguation and anaphora resolution, semantics, text generation, natural language interfaces and speech processing; and intelligent text processing applications: information retrieval, question answering, text summarization, information extraction and text mining, text classification, as well as authoring tools and spelling correction.

Soft Computing in Web Information Retrieval: Models and Applications
Herrera-Viedma, Enrique; Pasi, Gabriella; Crestani, Fabio (Eds.)

This book presents some recent works on the application of Soft Computing techniques in information access on the World Wide Web. The book comprises 15 chapters from internationally known researchers and is divided in four parts reflecting the areas of research of the presented works such as Document Classification, Semantic Web, Web Information Retrieval and Web Applications. This book demonstrates that Web Information Retrieval is a stimulating area of research where Soft Computing technologies can be applied satisfactorily. Written for: Researchers, Engineers, Graduate Students in Soft computing, Computational Intelligence, Computer Science, Internet.

A Unified Framework for Video Summarization, Browisng & Retrieval
Xiong Ziyou

Large volumes of video content can only be easily accessed by the use of rapid browsing and retrieval techniques. Constructing a video table of contents (ToC) and video highlights to enable end users to sift through all this data and find what they want, when they want are essential. This reference puts forth a unified framework to integrate these functions supporting efficient browsing and retrieval of video content. The authors have developed a cohesive way to create a video table of contents, video highlights, and video indices that serve to streamline the use of applications in consumer and surveillance video applications. The authors discuss the generation of table of contents, extraction of highlights, different techniques for audio and video marker recognition, and indexing with low-level features such as color, texture, and shape. Current applications including this summarization and browsing technology are also reviewed. Applications such as event detection in elevator surveillance, highlight extraction from sports video, and image and video database management are considered within the proposed framework. This book presents the latest in research and readers will find their search for knowledge completely satisfied by the breadth of the information covered in this volume. Audience Electrical & computer engineers, computer scientists (especially computer graphics; SIGGRAPH), internet engineers, web developers, digital cinematographers. R&D engineers at companies addressing multimedia (consumer) market, e.g., IBM, Mitsubishi, Hewlett-Packard, Intel, Microsoft, Apple, etc. Contents Contents Foreword Chapter 1: Introduction Chapter 2: Literature Survey Chapter 3: Video Table-of-content Generation Chapter 4: Video Highlights Extraction Chapter 5: Video Structure Discovery using Unsupervised Learning Chapter 6: Video Indexing Chapter 7: A Unified Framework for Video Summarization, Browsing, and Retrieval Chapter 8: Applications Chapter 9: Conclusions.

TREC Experiment and Evaluation in Information Retrieval
Ellen VoorheesBuckley

The Text REtrieval Conference (TREC), a yearly workshop hosted by the US government's National Institute of Standards and Technology, provides the infrastructure necessary for large-scale evaluation of text retrieval methodologies. With the goal of accelerating research in this area, TREC created the first large test collections of full-text documents and standardized retrieval evaluation. The impact has been significant; since TREC's beginning in 1992, retrieval effectiveness has approximately doubled. TREC has built a variety of large test collections, including collections for such specialized retrieval tasks as cross-language retrieval and retrieval of speech. Moreover, TREC has accelerated the transfer of research ideas into commercial systems, as demonstrated in the number of retrieval techniques developed in TREC that are now used in Web search engines. This book provides a comprehensive review of TREC research, summarizing the variety of TREC results, documenting the best practices in experimental information retrieval, and suggesting areas for further research. The first part of the book describes TREC's history, test collections, and retrieval methodology. Next, the book provides "track" reports -- describing the evaluations of specific tasks, including routing and filtering, interactive retrieval, and retrieving noisy text. The final part of the book offers perspectives on TREC from such participants as Microsoft Research, University of Massachusetts, Cornell University, University of Waterloo, City University of New York, and IBM. The book will be of interest to researchers in information retrieval and related technologies, including natural language processing. Ellen M. Voorhees is Computer Scientist at the National Institute of Standards and Technology (NIST). Donna K. Harman is Group Leader at the National Institute of Standards and Technology (NIST).

Video Data Management and Information Retrieval
Sagarmay Deb

Video Data Management and Information Retrieval combines the two important areas of research within computer technology and presents them in comprehensive, easy to understand manner. Video Data Management and Information Retrieval is ideal for graduates and under-graduates, as well as researchers working in either video data management or information retrieval. It takes an in depth look at many relevant topics within both video data management and information retrieval. In addition to dissecting those issues, the book also provides a Уbig pictureФ view of each topic. This shows the relevance of each issue and how those areas affect every one today.

Web Systems Design and Online Consumer Behavior
Yuan Gao

Web Systems Design and Online Consumer Behavior takes and interdisciplinary approach toward systems design in the online environment by providing an understanding of how consumers behave while shopping online and how certain system design elements may impact consumers' perceptions, attitude, intentions, and actual behavior. This book contains theoretical and empirical research from expert scholars in a number of areas including communications, psychology, marketing and advertising, and information systems. This book provides an integrated look at the subject area as described above to further our understanding of the linkage among various disciplines inherently connected with one another in electronic commerce.

Web Mining: Applications and Techniques
Anthony Scime

Web Mining is moving the World Wide Web toward a more useful environment in which users can quickly and easily find the information they need. Web Mining uses document content, hyperlink structure, and usage statistics to assist users in meeting their needed information. This book provides a record of current research and practical applications in Web searching. It includes techniques that will improve the utilization of the Web by the design of Web sites, as well as the design and application of search agents. This book presents research and related applications in a manner that encourages additional work toward improving the reduction of information overflow, which is so common today in Web search results.

Knowledge-Based Clustering: From Data to Information Granules
Witold Pedrycz

A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics * Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible * Includes illustrative material andwell-known experimentsto offer hands-on experience

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