The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitiveintelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an "application scenario," namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations.

To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the "gestalt" of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book.

Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph. "

Biomarker discovery is an important area of biomedical research that may lead to significant breakthroughs in disease analysis and targeted therapy. Biomarkers are biological entities whose alterations are measurable and are characteristic of a particular biological condition. Discovering, managing, and interpreting knowledge of new biomarkers are challenging and attractive problems in the emerging field of biomedical informatics.

This volumeis a collection of state-of-the-artresearch into the application of data mining to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, the included contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques.

This volume is intended for students, and researchers in bioinformatics, proteomics, and genomics, as wellengineers and applied scientistsinterested in the interdisciplinary application of data mining techniques."

This book constitutes the refereed proceedings of the 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, held in Montreal, QC, Canada, in May 2014. The 22 regular papers and 18 short papers presented together with 3 invited talks were carefully reviewed and selected from 94 submissions. The papers cover a variety of topics within AI, such as: agent systems; AI applications; automated reasoning; bioinformatics and BioNLP; case-based reasoning; cognitive models; constraint satisfaction; data mining; E-commerce; evolutionary computation; games; information retrieval; knowledge representation; machine learning; multi-media processing; natural language processing; neural nets; planning; privacy-preserving data mining; robotics; search; smart graphics; uncertainty; user modeling; web applications.

This two-volume set, consisting of LNCS 8403 and LNCS 8404, constitutes the thoroughly refereed proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The 85 revised papers presented together with 4 invited papers were carefully reviewed and selected from 300 submissions. The papers are organized in the following topical sections: lexical resources; document representation; morphology, POS-tagging, and named entity recognition; syntax and parsing; anaphora resolution; recognizing textual entailment; semantics and discourse; natural language generation; sentiment analysis and emotion recognition; opinion mining and social networks; machine translation and multilingualism; information retrieval; text classification and clustering; text summarization; plagiarism detection; style and spelling checking; speech processing; and applications.

This book constitutes the refereed proceedings of the Third International Conference on Health Information Science, HIS 2014, held in Shenzhen, China, in April 2014. The 29 full papers presented were carefully reviewed and selected from 61 submissions. They cover a wide range of topics in health information sciences and systems that support the health information management and health service delivery. They deal with medical/health/biomedicine information resources, such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyse, and optimize the use of information in the health domain; data management, data mining, and knowledge discovery, all of which play a key role in the decision making, management of public health, examination of standards, privacy and security issues; computer visualization and artificial intelligence for computer-aided diagnosis; and development of new architectures and applications for health information systems.

The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others:

What are the key factors that affect the performance of data fusion algorithms significantly?

What conditions are favorable to data fusion algorithms?

CombSum and CombMNZ, which one is better? and why?

What is the rationale of using the linear combination method?

How can the best fusion option be found under any given circumstances?"

This, the 13th issue of Transactions on Large-Scale Data and Knowledge-Centered Systems, contains six revised selected regular papers. Topics covered include federated data sources, information filtering, web data clouding, query reformulation, package skyline queries and SPARQL query processing over a LaV (Local-as-View) integration system.

This volume of Advances in Intelligent and Soft Computing contains accepted papers presented at the 8th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2015) and the 6th International Conference on European Transnational Education (ICEUTE 2015). These conferences were held in the beautiful and historic city of Burgos (Spain), in June 2015. The aim of the 8th CISIS conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of Computational Intelligence, Information Security, and Data Mining. The need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall event. After a through peer-review process, the CISIS 2015 International Program Committee selected 43 papers, written by authors from 16 different countries. In the case of 6th ICEUTE conference, the International Program Committee selected 12 papers (from 7 countries). These papers are published in present conference proceedings, achieving an acceptance rate of about 39%. The selection of papers was extremely rigorous in order to maintain the high quality of the conference and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference and the CISIS and ICEUTE conferences would not exist without their help.

This book presents recent research work and results in the area of communication and information technologies. The book includes the main results of the 11th International Conference on Computing and Information Technology (IC2IT) held during July 2nd-3rd, 2015 in Bangkok, Thailand. The book is divided into the two main parts Data Mining and Machine Learning as well as Data Network and Communications. New algorithms and methods of data mining asr discussed as well as innovative applications and state-of-the-art technologies on data mining, machine learning and data networking.

"Reliable Knowledge Discovery" focuses on theory, methods, and techniques for RKDD, a new sub-field of KDD. It studies the theory and methods to assure the reliability and trustworthiness of discovered knowledge and to maintain the stability and consistency of knowledge discovery processes. RKDD has a broad spectrum of applications, especially in critical domains like medicine, finance, and military.

"Reliable Knowledge Discovery" also presents methods and techniques for designing robust knowledge-discovery processes. Approaches to assessing the reliability of the discovered knowledge are introduced. Particular attention is paid to methods for reliable feature selection, reliable graph discovery, reliable classification, and stream mining. Estimating the data trustworthiness is covered in this volume as well. Case studies are provided in many chapters.

"Reliable Knowledge Discovery" is designed for researchers and advanced-level students focused on computer science and electrical engineering as a secondary text or reference. Professionals working in this related field and KDD application developers will also find this book useful.

The explosion of information technology has led to substantial growth of web-accessible linguistic data in terms of quantity, diversity and complexity. These resources become even more useful when interlinked with each other to generate network effects.

The general trend of providing data online is thus accompanied by newly developing methodologies to interconnect linguistic data and metadata. This includes linguistic data collections, general-purpose knowledge bases (e.g., the DBpedia, a machine-readable edition of the Wikipedia), and repositories with specific information about languages, linguistic categories and phenomena. The Linked Data paradigm provides a framework for interoperability and access management, and thereby allows to integrate information from such a diverse set of resources.

The contributions assembled in this volume illustrate the band-width of applications of the Linked Data paradigm for representative types of language resources. They cover lexical-semantic resources, annotated corpora, typological databases as well as terminology and metadata repositories. The book includes representative applications from diverse fields, ranging from academic linguistics (e.g., typology and corpus linguistics) over applied linguistics (e.g., lexicography and translation studies) to technical applications (in computational linguistics, Natural Language Processing and information technology).

This volume accompanies the Workshop on Linked Data in Linguistics 2012 (LDL-2012) in Frankfurt/M., Germany, organized by the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN). It assembles contributions of the workshop participants and, beyond this, it summarizes initial steps in the formation of a Linked Open Data cloud of linguistic resources, the Linguistic Linked Open Data cloud (LLOD).

This book constitutes the thoroughly refereed joint post-workshop proceedings of the 4th International Workshop on Mining Ubiquitous and Social Environments, MUSE 2013, held in Prague, Czech Republic, in September 2013, and the 4th International Workshop on Modeling Social Media, MSM 2013, held in Paris, France, in May 2013. The 8 full papers included in the book are revised and significantly extended versions of papers submitted to the workshops. The focus is on collective intelligence in ubiquitous and social environments. Issues tackled include personalization in social streams, recommendations exploiting social and ubiquitous data, and efficient information processing in social systems. Furthermore, this book presents work dealing with the problem of mining patterns from ubiquitous social data, including mobility mining and exploratory methods for ubiquitous data analysis.

This proceedings set contains 85 selected full papers presentedat the 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - MCO 2015, held on May 11-13, 2015 at Lorraine University, France. The present part II of the 2 volume set includes articles devoted to Data analysis and Data mining, Heuristic / Meta heuristic methods for operational research applications, Optimization applied to surveillance and threat detection, Maintenance and Scheduling, Post Crises banking and eco-finance modelling, Transportation, as well as Technologies and methods for multi-stakeholder decision analysis in public settings.

There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled DATA MINING: Foundations and Intelligent Paradigms: Volume 3: Medical, Health, Social, Biological and other Applications we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field."

The two-volume set LNCS 8935 and 8936 constitutes the thoroughly refereed proceedings of the 21st International Conference on Multimedia Modeling, MMM 2015, held in Sydney, Australia, in January 2015. The 49 revised regular papers, 24 poster presentations, were carefully reviewed and selected from 189 submissions. For the three special session, a total of 18 papers were accepted for MMM 2015. The three special sessions are Personal (Big) Data Modeling for Information Access and Retrieval, Social Geo-Media Analytics and Retrieval and Image or video processing, semantic analysis and understanding. In addition, 9 demonstrations and 9 video showcase papers were accepted for MMM 2015. The accepted contributions included in these two volumes represent the state-of-the-art in multimedia modeling research and cover a diverse range of topics including: Image and Video Processing, Multimedia encoding and streaming, applications of multimedia modelling and 3D and augmented reality.

Online social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, technological progressions in mobile devices (i.e. smart phones) enabled the incorporation of geo-location data in the traditional web-based online social networks, bringing the new era of Social and Mobile Web. The goal of this book is to bring together important research in a new family of recommender systems aimed at serving Location-based Social Networks (LBSNs). The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs. The book is organized into three parts. Part 1 provides introductory material on recommender systems, online social networks and LBSNs. Part 2 presents a wide variety of recommendation algorithms, ranging from basic to cutting edge, as well as a comparison of the characteristics of these recommender systems. Part 3 provides a step-by-step case study on the technical aspects of deploying and evaluating a real-world LBSN, which provides location, activity and friend recommendations. The material covered in the book is intended for graduate students, teachers, researchers, and practitioners in the areas of web data mining, information retrieval, and machine learning.

This book primarily discusses issues related to the mining aspects of data streams and it is unique in its primary focus on the subject. This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for that particular topic, and future research directions. The book is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for advanced-level students in computer science.

Data mining has emerged as one of the most active areas in information and c- munication technologies(ICT). With the boomingof the global economy,and ub- uitouscomputingandnetworkingacrosseverysectorand business,data andits deep analysis becomes a particularly important issue for enhancing the soft power of an organization, its production systems, decision-making and performance. The last ten years have seen ever-increasingapplications of data mining in business, gove- ment, social networks and the like. However, a crucial problem that prevents data mining from playing a strategic decision-support role in ICT is its usually limited decision-support power in the real world. Typical concerns include its actionability, workability, transferability, and the trustworthy, dependable, repeatable, operable and explainable capabilities of data mining algorithms, tools and outputs. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies and techniques, which are critical issues faced by data mining, as well as the ?ndings, thoughts and lessons learned in conducting several large-scale real-world data mining bu- ness applications. The aim and objective of domain driven data mining is to study effective and ef?cient methodologies, techniques, tools, and applications that can discover and deliver actionable knowledge that can be passed on to business people for direct decision-making and action-taking.

This book introduces the fundamental concepts of homomorphic encryption. From these foundations, applications are developed in the fields of private information retrieval, private searching on streaming data, privacy-preserving data mining, electronic voting and cloud computing. The content is presented in an instructional and practical style, with concrete examples to enhance the reader's understanding. This volume achieves a balance between the theoretical and the practical components of modern information security. Readers will learn key principles of homomorphic encryption as well as their application in solving real world problems.

This book constitutes the proceedings of the third International Workshop on Symbiotic Interaction, Symbiotic 2014, held in Helsinki, Finland, in October 2014. The 8 full papers and 5 short papers presented in this volume were carefully reviewed and selected from 16 submissions. They are organized in topical sections named: definitions of symbiotic interaction; reviews of implicit interaction; example applications; experimenting with users; and demos and posters.

This volume constitutes the refereed proceedings of the 10th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2015, held in Hong Kong, China, in January 2015. The 36 revised full papers were carefully reviewed and selected from 45 submissions. The papers are organized in topical sections on discrete and continuous optimization; image restoration and inpainting; segmentation; PDE and variational methods; motion, tracking and multiview reconstruction; statistical methods and learning; and medical image analysis.

As we entered the 21st century, the rapid growth of information technology has changed our lives more conveniently than we have ever speculated. Recently in all fields of the industry, heterogeneous technologies have converged with information technology resulting in a new paradigm, information technology convergence. In the process of information technology convergence, the latest issues in the structure of data, system, network, and infrastructure have become the most challenging task. Proceedings of the International Conference on IT Convergence and Security 2011 approaches the subject matter with problems in technical convergence and convergences of security technology by looking at new issues that arise from techniques converging. The general scope is convergence security and the latest information technology with the following most important features and benefits: 1. Introduction of the most recent information technology and its related ideas 2. Applications and problems related to technology convergence, and its case studies 3. Introduction of converging existing security techniques through convergence security Overall, after reading Proceedings of the International Conference on IT Convergence and Security 2011, readers will understand the most state of the art information strategies and technologies of convergence security.

Data Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: * supply chain design, * product development, * manufacturing system design, * product quality control, and * preservation of privacy. Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the 18th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of seven papers presented at the 24th International Conference on Database and Expert Systems Applications, DEXA 2013, held in Prague, in the Czech Republic, in August 2013. Following the conference, and two further rounds of reviewing and selection, five extended papers and two invited keynote papers were chosen for inclusion in this special issue. The subject areas covered include argumentation, e-government, business processes, predictive traffic estimation, semantic model integration, top-k query processing, uncertainty handling, graph comparison, community detection, genetic programming, and web services.

This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.