This book aims to identify promising future developmental opportunities and applications for Tech Mining. Specifically, the enclosed contributions will pursue three converging themes: The increasing availability of electronic text data resources relating to Science, Technology and Innovation (ST&I). The multiple methods that are able to treat this data effectively and incorporate means to tap into human expertise and interests. Translating those analyses to provide useful intelligence on likely future developments of particular emerging S&T targets. Tech Mining can be defined as text analyses of ST&I information resources to generate Competitive Technical Intelligence (CTI). It combines bibliometrics and advanced text analytic, drawing on specialized knowledge pertaining to ST&I. Tech Mining may also be viewed as a special form of "Big Data" analytics because it searches on a target emerging technology (or key organization) of interest in global databases. One then downloads, typically, thousands of field-structured text records (usually abstracts), and analyses those for useful CTI. Forecasting Innovation Pathways (FIP) is a methodology drawing on Tech Mining plus additional steps to elicit stakeholder and expert knowledge to link recent ST&I activity to likely future development. A decade ago, we demeaned Management of Technology (MOT) as somewhat self-satisfied and ignorant. Most technology managers relied overwhelmingly on casual human judgment, largely oblivious of the potential of empirical analyses to inform R&D management and science policy. CTI, Tech Mining, and FIP are changing that. The accumulation of Tech Mining research over the past decade offers a rich resource of means to get at emerging technology developments and organizational networks to date. Efforts to bridge from those recent histories of development to project likely FIP, however, prove considerably harder. One focus of this volume is to extend the repertoire of information resources; that will enrich FIP. Featuring cases of novel approaches and applications of Tech Mining and FIP, this volume will present frontier advances in ST&I text analytics that will be of interest to students, researchers, practitioners, scholars and policy makers in the fields of R&D planning, technology management, science policy and innovation strategy.

This two volume set (CCIS 623 and 634) constitutes the refereed proceedings of the Second International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, held in Harbin, China, in August 2016. The 91 revised full papers presented were carefully reviewed and selected from 338 submissions. The papers are organized in topical sections on Research Track (Part I) and Education Track, Industry Track, and Demo Track (Part II) and cover a wide range of topics related to social computing, social media, social network analysis, social modeling, social recommendation, machine learning, data mining.

This two volume set (CCIS 623 and 634) constitutes the refereed proceedings of the Second International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2016, held in Harbin, China, in August 2016. The 91 revised full papers presented were carefully reviewed and selected from 338 submissions. The papers are organized in topical sections on Research Track (Part I) and Education Track, Industry Track, and Demo Track (Part II) and cover a wide range of topics related to social computing, social media, social network analysis, social modeling, social recommendation, machine learning, data mining.

This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.

This book constitutes the refereed proceedings of the 15th International Conference on Systems Simulation, Asia Simulation 2015, held in Jeju, Korea, in November 2016.The 11 revised full papers presented were carefully reviewed and selected from 126 submissions. The papers are organized in topical sections on model and design; simulation and analysis.

This book offers a thorough yet easy-to-read reference guide to various aspects of cloud computing security. It begins with an introduction to the general concepts of cloud computing, followed by a discussion of security aspects that examines how cloud security differs from conventional information security and reviews cloud-specific classes of threats and attacks. A range of varying threats in cloud computing are covered, from threats of data loss and data breaches, to threats to availability and threats posed by malicious insiders. Further, the book discusses attacks launched on different levels, including attacks on the hypervisor, and on the confidentiality of data. Newer types, such as side-channel attacks and resource-freeing attacks, are also described. The work closes by providing a set of general security recommendations for the cloud.

This book constitutes the thoroughly refereed proceedings of the 9th Russian Summer School on Information Retrieval, RuSSIR 2015, held in Saint Petersburg, Russia, in August 2015. The volume includes 5 tutorial papers, summarizing lectures given at the event, and 6 revised papers from the school participants. The papers focus on various aspects of information retrieval.

In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers. The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader.

This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2016, held in conjunction with PAKDD, the 20th Pacific-Asia Conference on Knowledge Discovery and Data Mining in Auckland, New Zealand, in April 2016. The 23 revised papers presented were carefully reviewed and selected from 38 submissions. The workshops affiliated with PAKDD 2016 include: Biologically Inspired Data Mining Techniques, BDM; Machine Learning for Sensory Data Analysis, MLSDA; Predictive Analytics for Critical Care, PACC; as well as Data Mining in Business and Finance, WDMBF.

This illuminating text/reference surveys the state of the art in data science, and provides practical guidance on big data analytics. Expert perspectives are provided by authoritative researchers and practitioners from around the world, discussing research developments and emerging trends, presenting case studies on helpful frameworks and innovative methodologies, and suggesting best practices for efficient and effective data analytics. Features: reviews a framework for fast data applications, a technique for complex event processing, and agglomerative approaches for the partitioning of networks; introduces a unified approach to data modeling and management, and a distributed computing perspective on interfacing physical and cyber worlds; presents techniques for machine learning for big data, and identifying duplicate records in data repositories; examines enabling technologies and tools for data mining; proposes frameworks for data extraction, and adaptive decision making and social media analysis.

This book contains the refereed proceedings of the 19th International Conference on Business Information Systems, BIS 2016, held in Leipzig, Germany, in July 2016. The BIS conference series follows trends in academia and business research; thus the theme of the BIS 2016 conference was Smart Business Ecosystems". This recognizes that no business is an island and competition is increasingly taking place between business networks and no longer between individual companies. A variety of aspects is relevant for designing and understanding smart business ecosystems. They reach from new business models, value chains and processes to all aspects of analytical, social and enterprise applications and platforms as well as cyber-physical infrastructures. The 33 full and 1 short papers were carefully reviewed and selected from 87 submissions. They are grouped into sections on ecosystems; big and smart data; smart infrastructures; process management; business and enterprise modeling; service science; social media; and applications.

This book constitutes the refereed proceedings of the 12th International Baltic Conference on Databases and Information Systems, DB&IS 2016, held in Riga, Latvia, in July 2016. The 25 revised full papers presented were carefully reviewed and selected from 62 submissions. The papers are organized in topical sections on ontology, conceptual modeling and databases; tools, technologies and languages for model-driven development; decision support systems and data mining; advanced systems and technologies; business process modeling and performance measurement; software testing and quality assurance; linguistic components of IS; information technology in teaching and learning.

This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness.

This proceedings book presents recent research work and results in the area of communication and information technologies. The chapters of this book contain the main, well-selected and reviewed contributions of scientists who met at the 12th International Conference on Computing and Information Technology (IC2IT) held during 7th - 8th July 2016 in Khon Kaen, Thailand The book is divided into three parts: "User Centric Data Mining and Text Processing", "Data Mining Algoritms and their Applications" and "Optimization of Complex Networks".

In response to the need to improve road traffic operation, researchers implement advanced technologies and integration of systems and data, and develop state-of-the-art applications to assist traffic engineers. This SpringerBrief introduces three novel Web applications which can be an exceptional resource and a good visualization tool for traffic operators, managers, and analysts to monitor the congestion, and analyze incidents and signal performance measures. The applications offer more detailed analysis providing users with insights from different levels and perspectives. The benefit of providing these automated and interactive visualization tools is more efficient estimation of the local transport networks' performance, thus facilitating the decision making process in case of emergency events.

This book reports on advanced theories and cutting-edge applications in the field of soft computing. The individual chapters, written by leading researchers, are based on contributions presented during the 4th World Conference on Soft Computing, held May 25-27, 2014, in Berkeley. The book covers a wealth of key topics in soft computing, focusing on both fundamental aspects and applications. The former include fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation and neural networks, while the latter include soft computing in data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling. By providing readers with a timely, authoritative view on the field, and by discussing thought-provoking developments and challenges, the book will foster new research directions in the diverse areas of soft computing.

This book contains the refereed proceedings of the 17th International Conference on Business Process Modeling, Development and Support, BPMDS 2016, and the 21st International Conference on Exploring Modeling Methods for Systems Analysis and Design, EMMSAD 2016, held together with the 28th International Conference on Advanced Information Systems Engineering (CAiSE 2016) in Ljubljana, Slovenia, in June 2016. The focus theme for BPMDS 2016 papers was "Business Processes in a Connected World", for which three subthemes were identified: business processes for connecting people, connecting intelligent objects to business processes and connecting information/data/knowledge to business processes. The 17 full and 1 short paper accepted for BPMDS were selected from 48 submissions and are grouped into topical sections on process execution support; improving usability of process models; social and human perspectives; new directions in process modeling; consistency, correctness and compliance; process and data mining; and process variability. The intention of EMMSAD is to solicit papers related to the field of information systems analysis and design including numerous information modeling methods and notations that are typically evolving. These ongoing changes significantly impact the way information systems, enterprises, and business processes are being analyzed and designed in practice. The 12 full papers accepted for EMMSAD were chosen from 19 submissions and are grouped into topical sections on fundamental issues in modeling; requirements and regulations; enterprise and software ecosystem modeling; information and process model quality; meta-modeling and domain specific modeling and model composition; and modeling of architecture and design.

DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users' behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users' behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.

This book constitutes the refereed proceedings of the Second International Conference on Decision Support Systems Technology, ICDSST 2016, held in Plymouth, UK, May 23-25. The theme of the event was "Decision Support Systems Addressing Sustainability & Societal Challenges", organized by the EURO (Association of European Operational Research Societies) working group of Decision Support Systems (EWG-DSS). The 15 full papers presented in this book were selected out of 51 submissions after being carefully reviewed by internationally experts from the ICDSST 2016 Program Committee and external invited reviewers. The selected papers are representative of current and relevant research activities in various areas of decision support systems, such as sustainability and societal challenges; risk management and project portfolio management; business intelligence and knowledge management; and technologies to improve system usability.

The concept of digital risk, which has become ubiquitous in the media, sustains a number of myths and beliefs about the digital world. This book explores the opposite view of these ideologies by focusing on digital risks as perceived by actors in their respective contexts. Perceptions and Analysis of Digital Risks identifies the different types of risks that concern actors and actually impact their daily lives, within education or various socio-professional environments. It provides an analysis of the strategies used by the latter to deal with these risks as they conduct their activities; thus making it possible to characterize the digital cultures and, more broadly, the informational cultures at work. This book offers many avenues for action in terms of educating the younger generations, training teachers and leaders, and mediating risks.

This book springs from a multidisciplinary, multi-organizational, and multi-sector conversation about the privacy and ethical implications of research in human affairs using big data. The need to cultivate and enlist the public's trust in the abilities of particular scientists and scientific institutions constitutes one of this book's major themes. The advent of the Internet, the mass digitization of research information, and social media brought about, among many other things, the ability to harvest - sometimes implicitly - a wealth of human genomic, biological, behavioral, economic, political, and social data for the purposes of scientific research as well as commerce, government affairs, and social interaction. What type of ethical dilemmas did such changes generate? How should scientists collect, manipulate, and disseminate this information? The effects of this revolution and its ethical implications are wide-ranging. This book includes the opinions of myriad investigators, practitioners, and stakeholders in big data on human beings who also routinely reflect on the privacy and ethical issues of this phenomenon. Dedicated to the practice of ethical reasoning and reflection in action, the book offers a range of observations, lessons learned, reasoning tools, and suggestions for institutional practice to promote responsible big data research on human affairs. It caters to a broad audience of educators, researchers, and practitioners. Educators can use the volume in courses related to big data handling and processing. Researchers can use it for designing new methods of collecting, processing, and disseminating big data, whether in raw form or as analysis results. Lastly, practitioners can use it to steer future tools or procedures for handling big data. As this topic represents an area of great interest that still remains largely undeveloped, this book is sure to attract significant interest by filling an obvious gap in currently available literature.

This volume details several important databases and data mining tools. Data Mining Techniques for the Life Sciences, Second Edition guides readers through archives of macromolecular three-dimensional structures, databases of protein-protein interactions, thermodynamics information on protein and mutant stability, "Kbdock" protein domain structure database, PDB_REDO databank, erroneous sequences, substitution matrices, tools to align RNA sequences, interesting procedures for kinase family/subfamily classifications, new tools to predict protein crystallizability, metabolomics data, drug-target interaction predictions, and a recipe for protein-sequence-based function prediction and its implementation in the latest version of the ANNOTATOR software suite. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Data Mining Techniques for the Life Sciences, Second Edition aims to ensure successful results in the further study of this vital field.

This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence.All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible.The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. "'From curve fitting to machine learning' is ... a useful book. ... It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results.All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code". Leslie A. Piegl (Review of the first edition, 2012).

Interest in visualization design has increased in recent years. While there is a large body of existing work from which visualization designers can draw, much of the past research has focused on developing new tools and techniques that are aimed at specific contexts. Less focus has been placed on developing holistic frameworks, models, and theories that can guide visualization design at a general level-a level that transcends domains, data types, users, and other contextual factors. In addition, little emphasis has been placed on the thinking processes of designers, including the concepts that designers use, while they are engaged in a visualization design activity. In this book we present a general, holistic framework that is intended to support visualization design for human-information interaction. The framework is composed of a number of conceptual elements that can aid in design thinking. The core of the framework is a pattern language-consisting of a set of 14 basic, abstract patterns-and a simple syntax for describing how the patterns are blended. We also present a design process, made up of four main stages, for creating static or interactive visualizations. The 4-stage design process places the patterns at the core of designers' thinking, and employs a number of conceptual tools that help designers think systematically about creating visualizations based on the information they intend to represent. Although the framework can be used to design static visualizations for simple tasks, its real utility can be found when designing visualizations with interactive possibilities in mind-in other words, designing to support a human-information interactive discourse. This is especially true in contexts where interactive visualizations need to support complex tasks and activities involving large and complex information spaces. The framework is intended to be general and can thus be used to design visualizations for diverse domains, users, information spaces, and tasks in different fields such as business intelligence, health and medical informatics, digital libraries, journalism, education, scientific discovery, and others. Drawing from research in multiple disciplines, we introduce novel concepts and terms that can positively contribute to visualization design practice and education, and will hopefully stimulate further research in this area.