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Data Analytics for Renewable Energy Integration: 4th ECML PKDD Workshop, DARE 2016, Riva del Garda, Italy, September 23, 2016, Revised Selected Papers (Lecture Notes in Computer Science #10097)
by Oliver Kramer Wei Lee Woon Zeyar Aung Stuart MadnickThis book constitutes revised selected papers from the second ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2014, held in Nancy, France, in September 2014. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book.
Data Analytics for Renewable Energy Integration: 5th ECML PKDD Workshop, DARE 2017, Skopje, Macedonia, September 22, 2017, Revised Selected Papers (Lecture Notes in Computer Science #10691)
by Wei Lee Woon, Zeyar Aung, Oliver Kramer and Stuart MadnickThis book constitutes revised selected papers from the 5th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2017, held in Skopje, Macedonia, in September 2017. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response and many others.
Data Analytics for Renewable Energy Integration: Third ECML PKDD Workshop, DARE 2015, Porto, Portugal, September 11, 2015. Revised Selected Papers (Lecture Notes in Computer Science #9518)
by Wei Lee Woon Zeyar Aung Stuart MadnickThis book constitutes revised selected papers from the second ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2014, held in Nancy, France, in September 2014. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book.
Data Analytics for Renewable Energy Integration. Technologies, Systems and Society: 6th ECML PKDD Workshop, DARE 2018, Dublin, Ireland, September 10, 2018, Revised Selected Papers (Lecture Notes in Computer Science #11325)
by Wei Lee Woon Zeyar Aung Alejandro Catalina Feliú Stuart MadnickThis book constitutes the revised selected papers from the 6th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2018, held in Dublin, Ireland, in September 2018. The 9 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response, and many others.
Data Analytics for Smart Cities (Data Analytics Applications)
by Amir Alavi William G. ButtlarThe development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications. Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications.
Data Analytics for Smart Grids Applications—A Key to Smart City Development (Intelligent Systems Reference Library #247)
by Devendra Kumar Sharma Rohit Sharma Gwanggil Jeon Raghvendra KumarThis book introduces big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as a huge amount of data collection are first discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this book. By dealing with a huge amount of data from electricity networks, meteorological information system, geographical information system, etc., many benefits can be brought to the existing power system and improve customer service as well as social welfare in the era of big data. However, to advance the applications of big data analytics in real smart grids, many issues such as techniques, awareness, and synergies have to be overcome. This book provides deployment of semantic technologies in data analysis along with the latest applications across the field such as smart grids.
Data Analytics for Traditional Chinese Medicine Research
by Josiah Poon Simon K. PoonThis contributed volume explores how data mining, machine learning, and similar statistical techniques can analyze the types of problems arising from Traditional Chinese Medicine (TCM) research. The book focuses on the study of clinical data and the analysis of herbal data. Challenges addressed include diagnosis, prescription analysis, ingredient discoveries, network based mechanism deciphering, pattern-activity relationships, and medical informatics. Each author demonstrates how they made use of machine learning, data mining, statistics and other analytic techniques to resolve their research challenges, how successful if these techniques were applied, any insight noted and how these insights define the most appropriate future work to be carried out. Readers are given an opportunity to understand the complexity of diagnosis and treatment decision, the difficulty of modeling of efficacy in terms of herbs, the identification of constituent compounds in an herb, the relationship between these compounds and biological outcome so that evidence-based predictions can be made. Drawing on a wide range of experienced contributors, Data Analytics for Traditional Chinese Medicine Research is a valuable reference for professionals and researchers working in health informatics and data mining. The techniques are also useful for biostatisticians and health practitioners interested in traditional medicine and data analytics.
Data Analytics in Bioinformatics: A Machine Learning Perspective
by Rabinarayan Satpathy Tanupriya Choudhury Suneeta Satpathy Sachi Nandan Mohanty Xiaobo ZhangMachine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.
Data Analytics in Digital Humanities (Multimedia Systems and Applications)
by Shalin Hai-JewThis book covers computationally innovative methods and technologies including data collection and elicitation, data processing, data analysis, data visualizations, and data presentation. It explores how digital humanists have harnessed the hypersociality and social technologies, benefited from the open-source sharing not only of data but of code, and made technological capabilities a critical part of humanities work. Chapters are written by researchers from around the world, bringing perspectives from diverse fields and subject areas. The respective authors describe their work, their research, and their learning. Topics include semantic web for cultural heritage valorization, machine learning for parody detection by classification, psychological text analysis, crowdsourcing imagery coding in natural disasters, and creating inheritable digital codebooks. Designed for researchers and academics, this book is suitable for those interested in methodologies and analytics that can be applied in literature, history, philosophy, linguistics, and related disciplines. Professionals such as librarians, archivists, and historians will also find the content informative and instructive.
Data Analytics in e-Learning: Approaches and Applications (Intelligent Systems Reference Library #220)
by Marian Cristian MihăescuThis book focuses on research and development aspects of building data analytics workflows that address various challenges of e-learning applications. This book represents a guideline for building a data analysis workflow from scratch. Each chapter presents a step of the entire workflow, starting from an available dataset and continuing with building interpretable models, enhancing models, and tackling aspects of evaluating engagement and usability. The related work shows that many papers have focused on machine learning usage and advancement within e-learning systems. However, limited discussions have been found on presenting a detailed complete roadmap from the raw dataset up to the engagement and usability issues. Practical examples and guidelines are provided for designing and implementing new algorithms that address specific problems or functionalities. This roadmap represents a potential resource for various advances of researchers and practitioners in educational data mining and learning analytics.
Data Analytics in Marketing, Entrepreneurship, and Innovation (Data Analytics Applications)
by Mounir Kehal Shahira El AlfyInnovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences. Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics. It features case studies that provide realistic examples of applications. This multifaceted examination of data analytics looks at: Business analytics Applying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analytics Developing new products Technopreneurship Disruptive versus incremental innovation The book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing. Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery. Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques. Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns.
Data Analytics in Power Markets
by Yi Wang Qixin Chen Hongye Guo Kedi ZhengThis book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.
Data Analytics in Project Management (Data Analytics Applications)
by Seweryn SpalekThis book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book’s integrated approach to investigating both fields enhances the value of research findings.
Data Analytics in System Engineering: Proceedings of 7th Computational Methods in Systems and Software 2023, Vol. 3 (Lecture Notes in Networks and Systems #910)
by Radek Silhavy Petr SilhavyThese proceedings offer an insightful exploration of integrating data analytics in system engineering. This book highlights the essential role of data in driving innovation, optimizing processes, and solving complex challenges in the field. Targeted at industry professionals, researchers, and enthusiasts, this book serves as a comprehensive resource, providing actionable insights and showcasing transformative applications of data in engineering. It is a must-read for anyone keen on understanding and participating in the ongoing evolution of system engineering in our data-centric world.
Data Analytics in System Engineering: Proceedings of 7th Computational Methods in Systems and Software 2023, Vol. 4 (Lecture Notes in Networks and Systems #935)
by Radek Silhavy Petr SilhavyThese proceedings offer an insightful exploration of integrating data analytics in system engineering. This book highlights the essential role of data in driving innovation, optimizing processes, and solving complex challenges in the field. Targeted at industry professionals, researchers, and enthusiasts, this book serves as a comprehensive resource, providing actionable insights and showcasing transformative applications of data in engineering. It is a must-read for anyone keen on understanding and participating in the ongoing evolution of system engineering in our data-centric world.
Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS
by Joe MinichinoA comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS, accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics—from data engineering to analysis, business intelligence, DevOps, and MLOps—as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenanceA can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.
Data Analytics in the Era of the Industrial Internet of Things
by Aldo DagninoThis book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts’ decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.
Data Analytics Initiatives: Managing Analytics for Success
by Ondřej Bothe Ondřej Kubera David Bednář Martin Potančok Ota NovotnýThe categorisation of analytical projects could help to simplify complexity reasonably and, at the same time, clarify the critical aspects of analytical initiatives. But how can this complex work be categorized? What makes it so complex? Data Analytics Initiatives: Managing Analytics for Success emphasizes that each analytics project is different. At the same time, analytics projects have many common aspects, and these features make them unique compared to other projects. Describing these commonalities helps to develop a conceptual understanding of analytical work. However, features specific to each initiative affects the entire analytics project lifecycle. Neglecting them by trying to use general approaches without tailoring them to each project can lead to failure. In addition to examining typical characteristics of the analytics project and how to categorise them, the book looks at specific types of projects, provides a high-level assessment of their characteristics from a risk perspective, and comments on the most common problems or challenges. The book also presents examples of questions that could be asked of relevant people to analyse an analytics project. These questions help to position properly the project and to find commonalities and general project challenges.
Data Analytics Made Easy: Use machine learning and data storytelling in your work without writing any code
by Andrea De Mauro Francesco Marzoni Andrew J. WalterMake informed decisions using data analytics, machine learning, and data visualizationsKey FeaturesTake raw data and transform it to add value to your organizationLearn the art of telling stories with your data to engage with your audienceApply machine learning algorithms to your data with a few clicks of a buttonBook DescriptionData analytics has become a necessity in modern business, and skills such as data visualization, machine learning, and digital storytelling are now essential in every field. If you want to make sense of your data and add value with informed decisions, this is the book for you. Data Analytics Made Easy is an accessible guide to help you start analyzing data and quickly apply these skills to your work. It focuses on how to generate insights from your data at the click of a few buttons, using the popular tools KNIME and Microsoft Power BI. The book introduces the concepts of data analytics and shows you how to get your data ready and apply machine learning algorithms. Implement a complete predictive analytics solution with KNIME and assess its level of accuracy. Create impressive visualizations with Microsoft Power BI and learn the greatest secret in successful analytics – how to tell a story with your data. You'll connect the dots on the various stages of the data-to-insights process and gain an overview of alternative tools, including Tableau and H20 Driverless AI. By the end of this book, you will have learned how to implement machine learning algorithms and sell the results to your customers without writing a line of code.What you will learnUnderstand the potential of data and its impact on any businessInfluence business decisions with effective data storytelling when delivering insightsUse KNIME to import, clean, transform, combine data feeds, and automate recurring workflowsLearn the basics of machine learning and AutoML to add value to your organizationBuild, test, and validate simple supervised and unsupervised machine learning models with KNIMEUse Power BI and Tableau to build professional-looking and business-centric visuals and dashboardsWho this book is forWhether you are working with data experts or want to find insights in your business' data, you'll find this book an effective way to add analytics to your skill stack.No previous math, statistics, or computer science knowledge is required.
Data Analytics Using Splunk 9.x: A practical guide to implementing Splunk's features for performing data analysis at scale
by Dr. Nadine ShillingfordMake the most of Splunk 9.x to build insightful reports and dashboards with a detailed walk-through of its extensive features and capabilitiesKey FeaturesBe well-versed with the Splunk 9. x architecture, installation, onboarding, and indexing data featuresCreate advanced visualizations using the Splunk search processing languageExplore advanced Splunk administration techniques, including clustering, data modeling, and container managementBook DescriptionSplunk 9 improves on the existing Splunk tool to include important features such as federated search, observability, performance improvements, and dashboarding. This book helps you to make the best use of the impressive and new features to prepare a Splunk installation that can be employed in the data analysis process.Starting with an introduction to the different Splunk components, such as indexers, search heads, and forwarders, this Splunk book takes you through the step-by-step installation and configuration instructions for basic Splunk components using Amazon Web Services (AWS) instances. You'll import the BOTS v1 dataset into a search head and begin exploring data using the Splunk Search Processing Language (SPL), covering various types of Splunk commands, lookups, and macros. After that, you'll create tables, charts, and dashboards using Splunk's new Dashboard Studio, and then advance to work with clustering, container management, data models, federated search, bucket merging, and more.By the end of the book, you'll not only have learned everything about the latest features of Splunk 9 but also have a solid understanding of the performance tuning techniques in the latest version.What you will learnInstall and configure the Splunk 9 environmentCreate advanced dashboards using the flexible layout options in Dashboard StudioUnderstand the Splunk licensing modelsCreate tables and make use of the various types of charts available in Splunk 9.xExplore the new configuration management featuresImplement the performance improvements introduced in Splunk 9.xIntegrate Splunk with Kubernetes for optimizing CI/CD managementWho this book is forThe book is for data analysts, Splunk users, and administrators who want to become well-versed in the data analytics services offered by Splunk 9. You need to have a basic understanding of Splunk fundamentals to get the most out of this book.
Data Analytics & Visualization All-in-One For Dummies
by Jack A. Hyman Luca Massaron Paul McFedries John Paul Mueller Lillian Pierson Jonathan Reichental Joseph Schmuller Alan R. Simon Allen G. TaylorInstall data analytics into your brain with this comprehensive introduction Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place. Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one. You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau. You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level. With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey. Mine data from data sources Organize and analyze data Use data to tell a story with Tableau Expand your know-how with Python and R New and novice data analysts will love this All-in-One reference on how to make sense of data. Get ready to watch as your career in data takes off.
Data Analytics with Hadoop: An Introduction for Data Scientists
by Benjamin Bengfort Jenny KimReady to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce.Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data.Understand core concepts behind Hadoop and cluster computingUse design patterns and parallel analytical algorithms to create distributed data analysis jobsLearn about data management, mining, and warehousing in a distributed context using Apache Hive and HBaseUse Sqoop and Apache Flume to ingest data from relational databasesProgram complex Hadoop and Spark applications with Apache Pig and Spark DataFramesPerform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib
Data and AI Driving Smart Cities (Studies in Big Data #128)
by Pedro Ponce Therese Peffer Juana Isabel Mendez Garduno Ursula Eicker Arturo Molina Troy McDaniel Edgard D. Musafiri Mimo Ramanunni Parakkal Menon Kathryn Kaspar Sadam HussainThis book illustrates how the advanced technology developed for smart cities requires increasing interaction with citizens to motivate and incentive them. Megacities' needs have been encouraging for the creation of smart cities in which the needs of inhabitants are collected using virtualization and digitalization systems. On the other hand, machine learning algorithms have been implemented to provide better solutions for diverse areas in smart cities, such as transportation and health. Besides, conventional electric grids have transformed into smart grids, improving energy quality. Gamification, serious games, machine learning, dynamic interfaces, and social networks are some elements integrated holistically to provide novel solutions to design and develop smart cities. Also, this book presents in a friendly way the concept of social devices that are incorporated into smart homes and buildings. This book is used to understand and design smart cities where citizens are strongly interconnected so the demand response time can be reduced.
Data and Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a Results-Driven Data Strategy
by Simon Asplen-TaylorFor many organizations data is a by-product, but for the smarter ones it is the heartbeat of their business. Most businesses have a wealth of data buried in their systems which, if used effectively, could increase revenue, reduce costs and risk and improve customer satisfaction and employee experience. Beginning with how to choose projects which reflect your organization's goals and how to make the business case for investing in data, this book then takes the reader through the five 'waves' of organizational data maturity. It takes the reader from getting started on the data journey with some quick wins, to how data can help your business become a leading innovator which systematically outperforms competitors.Data and Analytics Strategy for Business outlines how to build consistent, high-quality sources of data which will create business value and explores how automation, AI and machine learning can improve performance and decision making. Filled with real-world examples and case studies, this book is a stage-by-stage guide to designing and implementing a results-driven data strategy.
Data and Applications Security and Privacy XXIX: 29th Annual IFIP WG 11.3 Working Conference, DBSec 2015, Fairfax, VA, USA, July 13-15, 2015, Proceedings (Lecture Notes in Computer Science #9149)
by Pierangela SamaratiThis book constitutes the refereed proceedings of the 29th Annual IFIP WG 11. 3 International Working Conference on Data and Applications Security and Privacy, DBSec 2015, held in Fairfax, VA, USA, in July 2015. The 18 revised full papers and 6 short papers presented were carefully reviewed and selected from 45 submissions. The papers are organized in the following topical sections: data anonymization and computation; access control and authorization; user privacy; authentication and information integration; privacy and trust; access control and usage policies; network and internet security; and information flow and inference.