Many applications depend on the effective acquisition of semantic metadata, and this state-of-the-art volume provides extensive coverage of the field of semantics acquisition games (SAGs). SAGs are a part of the crowdsourcing approach family and the authors analyze their role as tools for acquisition of resource metadata and domain models. Three case studies of SAG-based semantics acquisition methods are shown, along with other existing SAGs: 1. the Little Search Game - a search query formulation game using negative search, serving for acquisition of lightweight semantics. 2. the PexAce - a card game acquiring annotations to images. 3. the CityLights - a SAG used for validation of music metadata. The authors also look at the SAGs from their design perspectives covering SAG design issues and existing patterns, including several novel patterns. For solving cold start problems, a "helper artifact" scheme is presented, and for dealing with malicious player behavior, a posteriori cheating detection scheme is given. The book also presents methods for assessing information about player expertise, which can be used to make SAGs more effective in terms of useful output.

The term "smart city" defines the new urban environment, one that is designed for performance through information and communication technologies. Given that the majority of people across the world will live in urban environments within the next few decades, it's not surprising that massive effort and investment is being placed into efforts to develop strategies and plans for achieving "smart" urban growth. Building Smart Cities: Analytics, ICT, and Design Thinking explains the technology and a methodology known as design thinking for building smart cities. Information and communications technologies form the backbone of smart cities. A comprehensive and robust data analytics program enables the right choices to be made in building these cities. Design thinking helps to create smart cities that are both livable and able to evolve. This book examines all of these components in the context of smart city development and shows how to use them in an integrated manner. Using the principles of design thinking to reframe the problems of the smart city and capture the real needs of people living in a highly efficient urban environment, the book helps city planners and technologists through the following: Presentation of the relevant technologies required for coordinated, efficient cities Exploration of the latent needs of community stakeholders in a culturally appropriate context Discussion of the tested approaches to ideation, design, prototyping, and building or retrofitting smart cities Proposal of a model for a viable smart city project The smart city vision that we can create an optimized society through technology is hypothetical at best and reflects the failed repetition through the ages of equating scientific progress with positive social change. Up until now, despite our best hopes and efforts, technology has yet to bring an end to scarcity or suffering. Technical innovation, instead, can and should be directed in the service of our shared cultural values, especially within the rapidly growing urban milieu. In Building Smart Cities: Analytics, ICT, and Design Thinking, the author discusses the need to focus on creating human-centered approaches to our cities that integrate our human needs and technology to meet our economic, environmental, and existential needs. The book shows how this approach can lead to innovative, livable urban environments that are realizable, practical, and economically and environmentally sustainable.

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Data analysis, machine learning and knowledge discovery are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medicine, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and knowledge discovery presented during the 36th annual conference of the German Classification Society (GfKl). The conference was held at the University of Hildesheim (Germany) in August 2012.

In Part I, the impact of an integro-differential operator on parity logic engines (PLEs) as a tool for scientific modeling from scratch is presented. Part II outlines the fuzzy structural modeling approach for building new linear and nonlinear dynamical causal forecasting systems in terms of fuzzy cognitive maps (FCMs). Part III introduces the new type of autogenetic algorithms (AGAs) to the field of evolutionary computing. Altogether, these PLEs, FCMs, and AGAs may serve as conceptual and computational power tools.

The theme of the meeting was Statistical Methods for the Analysis of Large Data-Sets . In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniques are usually not well suited to managing this kind of data. The conference serves as an important meeting point for European researchers working on this topic and a number of European statistical societies participated in the organization of the event.
The book includes 45 papers from a selection of the 156 papers accepted for presentation and discussed at the conference on Advanced Statistical Methods for the Analysis of Large Data-sets. "

Big Data Analytics Using Splunk is a hands-on book showing how to process and derive business value from big data in real time. Examples in the book draw from social media sources such as Twitter (tweets) and Foursquare (check-ins). You also learn to draw from machine data, enabling you to analyze, say, web server log files and patterns of user access in real time, as the access is occurring. Gone are the days when you need be caught out by shifting public opinion or sudden changes in customer behavior. Splunk's easy to use engine helps you recognize and react in real time, as events are occurring. Splunk is a powerful, yet simple analytical tool fast gaining traction in the fields of big data and operational intelligence. Using Splunk, you can monitor data in real time, or mine your data after the fact. Splunk's stunning visualizations aid in locating the needle of value in a haystack of a data. Geolocation support spreads your data across a map, allowing you to drill down to geographic areas of interest. Alerts can run in the background and trigger to warn you of shifts or events as they are taking place. With Splunk you can immediately recognize and react to changing trends and shifting public opinion as expressed through social media, and to new patterns of eCommerce and customer behavior. The ability to immediately recognize and react to changing trends provides a tremendous advantage in today's fast-paced world of Internet business. Big Data Analytics Using Splunk opens the door to an exciting world of real-time operational intelligence.Built around hands-on projects Shows how to mine social media Opens the door to real-time operational intelligence What you'll learn Monitor and mine social media for trends affecting your business Know how you are perceived, and when that perception is rising or falling Detect changing customer behavior from mining your operational data Collect and analyze in real time, or from historical files Apply basic analytical metrics to better understand your data Create compelling visualizations and easily communicate your findings Who this book is for Big Data Analytics Using Splunk is for those who are interested in exploring the heaps of data they have available, but don't know where to start. It is for the people who have knowledge of the data they want to analyze and are developers or SQL programmers at a level anywhere between beginners and intermediate. Expert developers also benefit from learning how to use such a simple and powerful tool as Splunk.

Due to increasing practical needs, software support of environmental protection and research tasks is growing in importance and scope. Software systems help to monitor basic data, to maintain and process relevant environmental information, to analyze gathered information and to carry out decision processes, which often have to take into account complex alternatives with various side effects. Therefore software is an important tool for the environmental domain. When the first software systems in the environmental domain grew - 10 to 15 years ag- users and developers were not really aware of the complexity these systems are carrying with themselves: complexity with respect to entities, tasks and procedures. I guess nobody may have figured out at that time that the environmental domain would ask for solutions which information science would not be able to provide and - in several cases - can not provide until today. Therefore environmental informatics - as we call it today - is also an important domain of computer science itself, because practical solutions need to deal with very complex, interdisciplinary, distributed, integrated, sometimes badly defined, user-centered decision processes. I doubt somebody will state that we are already capable of building such integrated systems for end users for reasonable cost on a broad range. The development of the first scientific community for environmental informatics started around 1985 in Germany, becoming a technical committee and working group of the German Computer Society in 1987.

DAQ and data processing is a basic part of all automated production systems, diagnostic systems, watching over quality of production, energy distribution, transport control or in various other areas. Demands on the speed, accuracy and reliability increase in general. It is possible to achieve not only using superior (but also more expensive) hardware, but also applying advanced data acquisition and intelligent data processing. It deals e.g. optimal data fusion of a number of sensors, new stochastic methods for accuracy increasing, new algorithms for acceleration of data processing, etc. These are the grounds for publishing this book. Advanced Data Acquisition and Intelligent Data Processing offers 10 up-to-date examples of different applications of advanced data acquisition and intelligent data processing used in monitoring, measuring and diagnostics systems. The book arose based on the most interesting papers from this area published at IDAACS 2013 conference. However, the individual chapters include not only designed solution in wider context but also relevant theoretical parts, achieved results and possible future ways. Technical topics discussed in this book include: * advanced methods of data acquisition in application that are not routine; * measured data fusion using up-to-date advanced data processing; * nonlinear dynamical systems identification; * multidimensional image processing. Advanced Data Acquisition and Intelligent Data Processing is ideal for personnel of firms deals with advanced instrumentation, energy consumption monitoring, environment monitoring, non-descructive diagnostics robotics, etc., as well as academic staff and postgraduate students in electrical, control and computer engineering. Content: 1. Introduction; 2. Waveform acquisition with resolutions exceeding those of the ADC employed; 3. Different Disaggregation Algorithms in Non-Intrusive Home Energy Monitoring Systems; 4. Design and testing of an electronic nose system sensitive to the aroma of truffles; 5. DAQ System for Ultrasonic Transducer Evaluation under Spread Spectrum Excitation; 6. Optimal Data Fusion in Decentralized Stochastic Unknown Input Observers; 7. Odor Classification by Neural Networks; 8. ANFIS Based Approach for Improved Multisensors Signal Processing; 9. Neuro-Fuzzy Sensor's Linearization Based FPGA; 10. Interpolation Method of Nonlinear Dynamical Systems Identification Based on Volterra Model in Frequency Domain ; 11. Training Cellular Automata for Hyperspectral Image Segmentation

A state-of-the-art view of recent developments in the use of artificial neural networks for analysing remotely sensed satellite data. Neural networks, as a new form of computational paradigm, appear well suited to many of the tasks involved in this image analysis. This book demonstrates a wide range of uses of neural networks for remote sensing applications and reports the views of a large number of European experts brought together as part of a concerted action supported by the European Commission.

This brief provides methods for harnessing Twitter data to discover solutions to complex inquiries. The brief introduces the process of collecting data through Twitter s APIs and offers strategies for curating large datasets. The text gives examples of Twitter data with real-world examples, the present challenges and complexities of building visual analytic tools, and the best strategies to address these issues. Examples demonstrate how powerful measures can be computed using various Twitter data sources. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build their own applications. This brief is designed to provide researchers, practitioners, project managers, as well as graduate students with an entry point to jump start their Twitter endeavors. It also serves as a convenient reference for readers seasoned in Twitter data analysis."

Biometric Solutions for Authentication in an E-World provides a collection of sixteen chapters containing tutorial articles and new material in a unified manner. This includes the basic concepts, theories, and characteristic features of integrating/formulating different facets of biometric solutions for authentication, with recent developments and significant applications in an E-world. This book provides the reader with a basic concept of biometrics, an in-depth discussion exploring biometric technologies in various applications in an E-world. It also includes a detailed description of typical biometric-based security systems and up-to-date coverage of how these issues are developed. Experts from all over the world demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms, architectures, and implementations for biometric-based applications in an E-world.

It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690) Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology? This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.

This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in descriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package "compositions," it is also a general introductory text on Compositional Data Analysis. Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software. The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics. Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained.

Traffic Measurement on the Internet presents several novel online measurement methods that are compact and fast. Traffic measurement provides critical real-world data for service providers and network administrations to perform capacity planning, accounting and billing, anomaly detection, and service provision. Statistical methods play important roles in many measurement functions including: system designing, model building, formula deriving, and error analyzing. One of the greatest challenges in designing an online measurement function is to minimize the per-packet processing time in order to keep up with the line speed of the modern routers. This book also introduces a challenging problem - the measurement of per-flow information in high-speed networks, as well as, the solution. The last chapter discusses origin-destination flow measurement.

This volume presents an extensive collection of chapters covering various aspects of the exciting and important research area of data mining techniques in biomedicine. The topics include: - new approaches for the analysis of biomedical data, - applications of data mining techniques to real-life problems in medical practice, - comprehensive reviews of recent trends in the field. The book addresses the problems arising in fundamental areas of biomedical research, such as genomics, proteomics, protein characterization, and neuroscience. This volume would be of interest to scientists and practitioners working in the field of biomedicine, as well as related areas of engineering, mathematics, and computer science. It can also be helpful to graduate students and young researchers looking for new exciting directions in their work. Since each chapter can be read independently, readers interested in specific problems and applications may find the material of certain chapters useful.

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process-including how to verify the quality of the underlying clusters-through supervision, human intervention, or the automated generation of alternative clusters.

This book constitutes the refereed proceedings of the 13th International Conference on Passive and Active Measurement, PAM 2012, held in Vienna, Austria, in March 2012. The 25 revised full papers presented were carefully reviewed and selected from 83 submissions. The papers were arranged into eight sessions traffic evolution and analysis, large scale monitoring, evaluation methodology, malicious behavior, new measurement initiatives, reassessing tools and methods, perspectives on internet structure and services, and application protocols.

ERP-Systeme gibt es seit einigen Jahrzehnten. Sie haben sich aus ursprunglich einfachen betriebswirtschaftlichen Programmen zu immer ausgefeilteren hochkomplexen Softwarepaketen entwickelt. Einige ihrer Hersteller zahlen heute mit zu den erfolgreichsten boersennotierten Unternehmen. Das Buch beschreibt zunachst die wichtigsten logischen Grundlagen, sodann Funktionalitaten und Loesungen, wie sie in der Wirtschaft gefordert sind. In einem zweiten Teil werden schematisch die heute gangigsten Systeme auf dem Markt vorgestellt und nach festgelegten Kriterien bewertet. Diese Bewertung bietet eine Orientierungshilfe fur Interessierte, die solche Systeme einfuhren bzw. vorhandene in ihren Organisationen ersetzen wollen.

Cal Elgot was a very serious and thoughtful researcher, who with great determi nation attempted to find basic explanations for certain mathematical phenomena as the selection of papers in this volume well illustrate. His approach was, for the most part, rather finitist and constructivist, and he was inevitably drawn to studies of the process of computation. It seems to me that his early work on decision problems relating automata and logic, starting with his thesis under Roger Lyndon and continuing with joint work with Biichi, Wright, Copi, Rutledge, Mezei, and then later with Rabin, set the stage for his attack on the theory of computation through the abstract treatment of the notion of a machine. This is also apparent in his joint work with A. Robinson reproduced here and in his joint papers with John Shepherdson. Of course in the light of subsequent work on decision problems by Biichi, Rabin, Shelah, and many, many others, the subject has been placed on a completely different plane from what it was when Elgot left the area. But I feel that his papers, results-and style-were very definitely influential at the time and may well have altered the course of the investigation of these problems. As Sammy Eilenberg explains, the next big influence on Elgot's thinking was category theory, which gave him a way of expressing his ideas in a sharply algebraic manner. The joint book with Eilenberg is one illustration of this influence."

This book contains extended and revised versions of a set of selected papers from two workshops organized by the Euro Working Group on Decision Support Systems (EWG-DSS), which were held in London and Paris, in June and November 2011, respectively. The workshop themes were "Decision Support Systems" and "Collaborative Decision Making." The 11 papers were selected from 52 submissions and are representative of the current research in these fields. They address challenges such as conceptual models, software specification and simulation, collaborative requirements specification, as well as multi-objective and multi-criteria decision making.

The emergence of huge amounts of data which require analysis and in some cases real-time processing has forced exploration into fast algorithms for handling very lage data sizes. Analysis of x-ray images in medical applications, cyber security data, crime data, telecommunications and stock market data, health records and business analytics data are but a few areas of interest. Applications and platforms including R, RapidMiner and Weka provide the basis for analysis, often used by practitioners who pay little to no attention to the underlying mathematics and processes impacting the data. This often leads to an inability to explain results or correct mistakes, or to spot errors. Applied Data Analytics - Principles and Applications seeks to bridge this missing gap by providing some of the most sought after techniques in big data analytics. Establishing strong foundations in these topics provides practical ease when big data analyses are undertaken using the widely available open source and commercially orientated computation platforms, languages and visualisation systems. The book, when combined with such platforms, provides a complete set of tools required to handle big data and can lead to fast implementations and applications. The book contains a mixture of machine learning foundations, deep learning, artificial intelligence, statistics and evolutionary learning mathematics written from the usage point of view with rich explanations on what the concepts mean. The author has thus avoided the complexities often associated with these concepts when found in research papers. The tutorial nature of the book and the applications provided are some of the reasons why the book is suitable for undergraduate, postgraduate and big data analytics enthusiasts. This text should ease the fear of mathematics often associated with practical data analytics and support rapid applications in artificial intelligence, environmental sensor data modelling and analysis, health informatics, business data analytics, data from Internet of Things and deep learning applications.

Machine learning and nonparametric function estimation procedures can be effectively used in forecasting. One important and current application is used to make forecasts of "future dangerousness" to inform criminal justice decision. Examples include the decision to release an individual on parole, determination of the parole conditions, bail recommendations, and sentencing. Since the 1920s, "risk assessments" of various kinds have been used in parole hearings, but the current availability of large administrative data bases, inexpensive computing power, and developments in statistics and computer science have increased their accuracy and applicability. In this book, these developments are considered with particular emphasis on the statistical and computer science tools, under the rubric of supervised learning, that can dramatically improve these kinds of forecasts in criminal justice settings. The intended audience is researchers in the social sciences and data analysts in criminal justice agencies.

Der Weg von der Inbetriebnahme eines Prozessorsystems bis zur Implementierung einer Human Machine Interface (HMI) bildet den Schwerpunkt dieses Werks. Der Autor erlautert, wie Treiber und Betriebssystem (QNX, Linux) konfiguriert, gebaut und geladen werden. Alle notwendigen Kenntnisse werden systematisch und fundiert vermittelt, auch Fragen der Virtualisierung und der Einsatz von MultiCore-Systemen. Der Band enthalt praktische Beispiele sowie Anleitungen fur die Fehlersuche und die Performance-Optimierung, Code-Snippets werden zur Verfugung gestellt."

Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.