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Long description: Know-how für Data Scientists übersichtliche und anwendungsbezogene Einführung zahlreiche Anwendungsfälle und Praxisbeispiele aus unterschiedlichen Branchen Potenziale, aber auch mögliche Fallstricke werden aufgezeigt Data Science steht derzeit wie kein anderer Begriff für die Auswertung großer Datenmengen mit analytischen Konzepten des Machine Learning oder der künstlichen Intelligenz. Nach der bewussten Wahrnehmung der Big Data und dabei insbesondere der Verfügbarmachung in Unternehmen sind Technologien und Methoden zur Auswertung dort gefordert, wo klassische Business Intelligence an ihre Grenzen stößt. Dieses Buch bietet eine umfassende Einführung in Data Science und deren praktische Relevanz für Unternehmen. Dabei wird auch die Integration von Data Science in ein bereits bestehendes Business-Intelligence-Ökosystem thematisiert. In verschiedenen Beiträgen werden sowohl Aufgabenfelder und Methoden als auch Rollen- und Organisationsmodelle erläutert, die im Zusammenspiel mit Konzepten und Architekturen auf Data Science wirken. Diese 2., überarbeitete Auflage wurde um neue Themen wie Feature Selection und Deep Reinforcement Learning sowie eine neue Fallstudie erweitert. Biographical note: Prof. Dr. Uwe Haneke ist seit 2003 Professor für Betriebswirtschaftslehre und betriebliche Informationssysteme an der Hochschule Karlsruhe – Technik und Wirtschaft. Dort vertritt er u.a. die Bereiche Business Intelligence, Geschäftsprozessmanagement im Fachgebiet Informatik. Seine Publikationen beschäftigen sich mit den Themen Open Source Business Intelligence, Self-Service-BI und Analytics.Prof. Dr. Stephan Trahasch ist Professor für betriebliche Kommunikationssysteme und IT-Sicherheit an der Hochschule Offenburg. Seine Forschungsschwerpunkte liegen in den Bereichen Data Mining, Big Data und Agile Business Intelligence. In Forschungsprojekten beschäftigt er sich mit der praktischen Anwendung von Data Mining und Big-Data-Technologien und deren Herausforderungen in Unternehmen. Er ist Leiter des Institute for Machine Learning and Analytics und Mitglied der Forschungsgruppe Analytics und Data Science an der Hochschule OffenburIst Professor für betriebliche Kommunikationssysteme und IT-Sicherheit an der Hochschule Offenburg. Seine Forschungsschwerpunkte liegen in den Bereichen Data Mining, Big Data und Agile Business Intelligence. In Forschungsprojekten beschäftigt er sich mit der praktischen Anwendung von Data Mining und Big-Data-Technologien und deren Herausforderungen in Unternehmen. Er ist Leiter des Institute for Machine Learning and Analytics und Mitglied der Forschungsgruppe Analytics und Data Science an der Hochschule Offenburg. Dr. Michael Zimmer verantwortet bei der Zurich Gruppe Deutschland das Thema künstliche Intelligenz. Hierbei beschäftigt er sich sparten- und ressortübergreifend mit der Identifikation, Entwicklung, Produktivsetzung und Industrialisierung von KI-Anwendungsfällen. Er hat über Data & Analytics Governance promoviert, ist Autor und Herausgeber diverser Publikationen und TDWI Fellow. Vor seiner Zeit bei der Zurich Deutschland war er fast 14 Jahre in der Beratung tätig und beschäftigte sich mit dem Aufbau komplexer Data-, Analytics- und KI-Architekturen sowie der Einführung und Konzeption zugehöriger Governance-Strukturen.Prof. Dr. Carsten Felden ist Direktor des Instituts für Wirtschaftsinformatik an der TU Bergakademie Freiberg (Sachsen). Er hat dort die Professur für ABWL, insbes. Informationswirtschaft/Wirtschaftsinformatik inne und vertritt in der Lehre die Themen der Wirtschaftsinformatik mit dem Fokus auf Business Analytics (BA). Zentrale Forschungsthemen sind neben Business Analytics Data Warehousing, eXtensible Business Reporting Language (XBRL) und IT-Reifegradmodelle sowie Digitalisierung im Kontext der Business Intelligence. Er ist Vorstandsvorsitzender des TDWI e.V. und war Vorstandsmitglied des XBRL Deutschland e.V. Er veröffentlichte zahlreiche Artikel sowohl auf internationalen Konferenzen als auch in wissenschaftlichen und praxisorientierten Zeitschriften. Im Weiteren ist er häufig Program Chair bei internationalen Konferenzen wie WI, ECIS oder A MCIS. In Kooperation mit anderen Autoren verfasst er regelmäßig Bücher zu Themen der analytischen Ansätze im betrieblichen Umfeld.

Ethik --- Datenanalyse --- Big Data --- Künstliche Intelligenz --- Rollenkonzepte --- KI --- Data Privacy --- Deep Learning --- Analytics --- predictive maintenance --- TensorFlow --- DWH-Modernisierung --- DWH-Erweiterung --- Data-Architekturen --- Analytic-Architekturen --- Organisationskonzepte

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Mechanical processing is just one step in the value chain of metal production, but to some exten,t it determines an effectiveness of separation through suitable preparation of the raw material for beneficiation processes through production of required particle sze composition and useful mineral liberation. The issue is mostly related to techniques of comminution and size classification, but it also concerns methods of gravity separation, as well as modeling and optimization. Technological and economic assessment supplements the issue.

belt ore measurement --- convolutional neural network --- image processing --- contour detection --- OpenCV --- brittle materials --- uniaxial compression --- comminution --- particle size --- movement characteristics --- particle velocity --- kinetic energy --- spatial distribution --- flotation --- copper ore --- lithology --- flotation agents --- particle size distribution --- taxonomic methods --- Dual-Energy X-ray Transmission (DE-XRT) --- sulphide --- polymetallic --- vibrating flip-flow screen --- DEM --- wet stick material --- JKR model --- separation performance --- aggregates --- jig beneficiation --- mineral processing --- raw materials --- separation --- enrichment --- HPGR --- approximation of particle size --- sieving screen --- diagnostics --- predictive maintenance --- wavelet transformation --- n/a

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Cognitive building is a pioneering topic envisioning the future of our built environment. The concept of "cognitive" provides a paradigm shift that steps from the static concept of the building as a container of human activities towards the modernist vision of "machine à habiter" of Le Corbusier, where the technological content adds the capability of learning from users' behavior and environmental variables to adapt itself to achieve major goals such as user comfort, energy-saving, flexible functionality, high durability, and good maintainability. The concept is based on digital frameworks and IoT networks towards the concept of a smart city.

explainable post occupancy --- humanoid robot --- lighting simulation software --- BIM --- openBIM --- IFC --- IoT --- sensors --- cognitive buildings --- asset management --- digital twin --- BEM --- simulation modelling --- dynamic simulation --- Building Information Modelling (BIM) --- Internet of Things (IoT) --- facility management --- cyber-physical systems --- Building Management System --- Digital Twin --- Post-Occupancy Evaluations --- cognitive --- digital twins --- building lifecycle management --- artificial intelligence --- decision support --- self-learning --- optimization --- building performance simulation --- lighting simulation --- lighting quality --- visual comfort --- office field study --- evidence-based design --- building information modeling --- HVAC --- fan coil --- Internet of Things --- predictive maintenance --- fault detection --- smart building --- sustainable building --- construction projects --- BIM implementation --- stakeholders --- barriers --- construction product --- servitization --- level of evidence --- level curves --- ground slopes --- embankments --- road and rail design --- n/a

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In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems.

Technology: general issues --- passive house --- enclosure structure --- heat transfer coefficient --- energy consumption --- turbo-propeller --- regional --- fuel --- weight --- range --- design --- CO2 reduction --- multi-objective combinatorial optimization --- meta-heuristics --- ant colony optimization --- non-intrusive load monitoring --- appliance classification --- appliance feature --- recurrence graph --- weighted recurrence graph --- V-I trajectory --- convolutional neural network --- energy baselines --- machine learning --- clustering --- neural methods --- smart intelligent systems --- building energy consumption --- building load forecasting --- energy efficiency --- thermal improved of buildings --- anti-icing --- heat and mass transfer --- heating power distribution --- heat load reduction --- optimization method --- experimental validation --- big data process --- predictive maintenance --- fracturing roofs to maintain entry (FRME) --- field measurement --- numerical simulation --- side abutment pressure --- strata movement --- energy --- manufacturing --- prediction --- forecasting --- modelling --- passive house --- enclosure structure --- heat transfer coefficient --- energy consumption --- turbo-propeller --- regional --- fuel --- weight --- range --- design --- CO2 reduction --- multi-objective combinatorial optimization --- meta-heuristics --- ant colony optimization --- non-intrusive load monitoring --- appliance classification --- appliance feature --- recurrence graph --- weighted recurrence graph --- V-I trajectory --- convolutional neural network --- energy baselines --- machine learning --- clustering --- neural methods --- smart intelligent systems --- building energy consumption --- building load forecasting --- energy efficiency --- thermal improved of buildings --- anti-icing --- heat and mass transfer --- heating power distribution --- heat load reduction --- optimization method --- experimental validation --- big data process --- predictive maintenance --- fracturing roofs to maintain entry (FRME) --- field measurement --- numerical simulation --- side abutment pressure --- strata movement --- energy --- manufacturing --- prediction --- forecasting --- modelling

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The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may be better suited to some applications, such as during the design stage of a new product.

Technology: general issues --- Kalman filter --- motion capture --- gait analysis --- inertial sensor --- rail vehicles --- track irregularities --- multibody dynamics --- inertial sensors --- computer vision --- singular configuration --- parallel robot --- motion control --- 3D tracking --- screw theory --- Kalman filtering --- coupled states-inputs estimation --- virtual sensors --- slider-crank mechanism --- virtual sensoring --- physical sensors --- smart/intelligent sensors --- sensor technology and applications --- sensing principles --- signal processing in sensor systems --- symbolic generation --- real-time computation --- human-in-the-loop --- haptic devices --- parameter estimation --- curve fitting method --- hydraulic system --- predictive maintenance --- characteristic curve --- product life cycle --- digital twin --- adaptive Kalman filter --- nonlinear models --- virtual sensing --- multibody based observers --- vehicle dynamics estimation --- sideslip angle estimation --- factor graph --- graphical models --- movable repetitive lander --- fault-tolerant soft-landing --- landing configuration --- stability optimization --- Kalman filter --- motion capture --- gait analysis --- inertial sensor --- rail vehicles --- track irregularities --- multibody dynamics --- inertial sensors --- computer vision --- singular configuration --- parallel robot --- motion control --- 3D tracking --- screw theory --- Kalman filtering --- coupled states-inputs estimation --- virtual sensors --- slider-crank mechanism --- virtual sensoring --- physical sensors --- smart/intelligent sensors --- sensor technology and applications --- sensing principles --- signal processing in sensor systems --- symbolic generation --- real-time computation --- human-in-the-loop --- haptic devices --- parameter estimation --- curve fitting method --- hydraulic system --- predictive maintenance --- characteristic curve --- product life cycle --- digital twin --- adaptive Kalman filter --- nonlinear models --- virtual sensing --- multibody based observers --- vehicle dynamics estimation --- sideslip angle estimation --- factor graph --- graphical models --- movable repetitive lander --- fault-tolerant soft-landing --- landing configuration --- stability optimization