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.

The two volume set LNAI 8481 and 8482 constitutes the refereed conference proceedings of the 27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, held in Kaohsiung, Taiwan, in June 2014. The total of 106 papers selected for the proceedings were carefully reviewed and selected from various submissions. The papers deal with a wide range of topics from applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation and robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace and human-machine interaction.

What are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.

The latest inventions in internet technology influence most of business and daily activities. Internet security, internet data management, web search, data grids, cloud computing, and web-based applications play vital roles, especially in business and industry, as more transactions go online and mobile. Issues related to ubiquitous computing are becoming critical. Internet technology and data engineering should reinforce efficiency and effectiveness of business processes. These technologies should help people make better and more accurate decisions by presenting necessary information and possible consequences for the decisions. Intelligent information systems should help us better understand and manage information with ubiquitous data repository and cloud computing. This book is a compilation of some recent research findings in Internet Technology and Data Engineering. This book provides state-of-the-art accounts in computational algorithms/tools, database management and database technologies, intelligent information systems, data engineering applications, internet security, internet data management, web search, data grids, cloud computing, web-based application, and other related topics.

This book constitutes the refereed proceedings of the 22nd International Symposium on String Processing and Information Retrieval, SPIRE 2015, held in London, UK, in September 2015. The 28 full and 6 short papers included in this volume were carefully reviewed and selected from 90 submissions. The papers cover research in all aspects of string processing, information retrieval, computational biology, pattern matching, semi-structured data, and related applications.

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).

"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.

This book constitutes the refereed proceedings of the 10th IEEE International Conference Beyond Databases, Architectures, and Structures, BDAS 2014, held in Ustron, Poland, in May 2014. This book consists of 56 carefully revised selected papers that are assigned to 11 thematic groups: query languages, transactions and query optimization; data warehousing and big data; ontologies and semantic web; computational intelligence and data mining; collective intelligence, scheduling, and parallel processing; bioinformatics and biological data analysis; image analysis and multimedia mining; security of database systems; spatial data analysis; applications of database systems; Web and XML in database systems.

Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting to the user. The total process is highly computation intensive. The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation).

This book contains the refereed proceedings of the 17th International Conference on Business Information Systems, BIS 2014, held in Larnaca, Cyprus, in May 2014. The BIS conference series follows trends in academic and business research; thus, the theme of the BIS 2014 conference was "Big Data: Problems Solved and Remaining Challenges." Currently, big data is one of the most prominent trends in areas such as recommendation engines, fraud detection, risk modeling, marketing campaign and social network analysis.

The 22 revised full papers were carefully reviewed and selected from 58 submissions. They are grouped into sections on big data, business process management, ontologies and conceptual modeling, collaboration, service science and interoperability and specific BIS applications.

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 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.

After a brief presentation of the state of the art of process-mining techniques, Andrea Burratin proposes different scenarios for the deployment of process-mining projects, and in particular a characterization of companies in terms of their process awareness. The approaches proposed in this book belong to two different computational paradigms: first to classic "batch process mining," and second to more recent "online process mining." The book encompasses a revised version of the author's PhD thesis, which won the "Best Process Mining Dissertation Award" in 2014, awarded by the IEEE Task Force on Process Mining.

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.

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.

The book will focus on exploiting state of the art research in semantic web and web science. The rapidly evolving world-wide-web has led to revolutionary changes in the whole of society. The research and development of the semantic web covers a number of global standards of the web and cutting edge technologies, such as: linked data, social semantic web, semantic web search, smart data integration, semantic web mining and web scale computing. These proceedings are from the 6th Chinese Semantics Web Symposium.

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.

Community structure is a salient structural characteristic of many real-world networks. Communities are generally hierarchical, overlapping, multi-scale and coexist with other types of structural regularities of networks. This poses major challenges for conventional methods of community detection. This book will comprehensively introduce the latest advances in community detection, especially the detection of overlapping and hierarchical community structures, the detection of multi-scale communities in heterogeneous networks, and the exploration of multiple types of structural regularities. These advances have been successfully applied to analyze large-scale online social networks, such as Facebook and Twitter. This book provides readers a convenient way to grasp the cutting edge of community detection in complex networks. The thesis on which this book is based was honored with the "Top 100 Excellent Doctoral Dissertations Award" from the Chinese Academy of Sciences and was nominated as the "Outstanding Doctoral Dissertation" by the Chinese Computer Federation.

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.

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.

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.

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 1: Clustering, Association and Classification we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

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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.

Mohamed Medhat Gaber "It is not my aim to surprise or shock you - but the simplest way I can summarise is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until - in a visible future - the range of problems they can handle will be coextensive with the range to which the human mind has been applied" by Herbert A. Simon (1916-2001) 1Overview This book suits both graduate students and researchers with a focus on discovering knowledge from scienti c data. The use of computational power for data analysis and knowledge discovery in scienti c disciplines has found its roots with the re- lution of high-performance computing systems. Computational science in physics, chemistry, and biology represents the rst step towards automation of data analysis tasks. The rational behind the developmentof computationalscience in different - eas was automating mathematical operations performed in those areas. There was no attention paid to the scienti c discovery process. Automated Scienti c Disc- ery (ASD) [1-3] represents the second natural step. ASD attempted to automate the process of theory discovery supported by studies in philosophy of science and cognitive sciences. Although early research articles have shown great successes, the area has not evolved due to many reasons. The most important reason was the lack of interaction between scientists and the automating systems.

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.