This book gathers a collection of high-quality peer-reviewed research papers presented at the International Conference on Big Data, IoT and Machine Learning (BIM 2021), held in Cox's Bazar, Bangladesh, during 23-25 September 2021. The book covers research papers in the field of big data, IoT and machine learning. The book will be helpful for active researchers and practitioners in the field.

Large corporations like IBM and Oracle are using Excel dashboards and reports as a Business Intelligence tool, and many other smaller businesses are looking to these tools in order to cut costs for budgetary reasons. An effective analyst not only has to have the technical skills to use Excel in a productive manner but must be able to synthesize data into a story, and then present that story in the most impactful way. Microsoft shows its recognition of this with Excel. In Excel, there is a major focus on business intelligence and visualization. Data Visualization with Excel Dashboards and Reports fills the gap between handling data and synthesizing data into meaningful reports. This title will show readers how to think about their data in ways other than columns and rows. Most Excel books do a nice job discussing the individual functions and tools that can be used to create an "Excel Report". Titles on Excel charts, Excel pivot tables, and other books that focus on "Tips and Tricks" are useful in their own right; however they don't hit the mark for most data analysts. The primary reason these titles miss the mark is they are too focused on the mechanical aspects of building a chart, creating a pivot table, or other functionality. They don't offer these topics in the broader picture by showing how to present and report data in the most effective way. What are the most meaningful ways to show trending? How do you show relationships in data? When is showing variances more valuable than showing actual data values? How do you deal with outliers? How do you bucket data in the most meaningful way? How do you show impossible amounts of data without inundating your audience? In Data Visualization with Excel Reports and Dashboards, readers will get answers to all of these questions. Part technical manual, part analytical guidebook; this title will help Excel users go from reporting data with simple tables full of dull numbers, to creating hi-impact reports and dashboards that will wow management both visually and substantively. This book offers a comprehensive review of a wide array of technical and analytical concepts that will help users create meaningful reports and dashboards. After reading this book, the reader will be able to: Analyze large amounts of data and report their data in a meaningful way Get better visibility into data from different perspectives Quickly slice data into various views on the fly Automate redundant reporting and analyses Create impressive dashboards and What-If analyses Understand the fundamentals of effective visualization Visualize performance comparisons Visualize changes and trends over time

Data Science and Analytics explores the solutions to problems in society, environment and in industry. With the increase in the availability of data, analytics has now become a major element in both the top line and the bottom line of any organization. This book explores perspectives on how big data and business analytics are increasingly essential in better decision making. This edited work explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. Data Science and Analytics brings together researchers, engineers and practitioners to encompass a wide and diverse range of topics in a wide range of fields. The book will provide unique insights to researchers, academics and data scientists from a variety of disciplines interested in analyzing and application of big data analytics, as well as data analysts, students and scholars pursuing advanced study in big data.

Educational Data Analytics (EDA) have been attributed with significant benefits for enhancing on-demand personalized educational support of individual learners as well as reflective course (re)design for achieving more authentic teaching, learning and assessment experiences integrated into real work-oriented tasks. This open access textbook is a tutorial for developing, practicing and self-assessing core competences on educational data analytics for digital teaching and learning. It combines theoretical knowledge on core issues related to collecting, analyzing, interpreting and using educational data, including ethics and privacy concerns. The textbook provides questions and teaching materials/ learning activities as quiz tests of multiple types of questions, added after each section, related to the topic studied or the video(s) referenced. These activities reproduce real-life contexts by using a suitable use case scenario (storytelling), encouraging learners to link theory with practice; self-assessed assignments enabling learners to apply their attained knowledge and acquired competences on EDL. By studying this book, you will know where to locate useful educational data in different sources and understand their limitations; know the basics for managing educational data to make them useful; understand relevant methods; and be able to use relevant tools; know the basics for organising, analysing, interpreting and presenting learner-generated data within their learning context, understand relevant learning analytics methods and be able to use relevant learning analytics tools; know the basics for analysing and interpreting educational data to facilitate educational decision making, including course and curricula design, understand relevant teaching analytics methods and be able to use relevant teaching analytics tools; understand issues related with educational data ethics and privacy. This book is intended for school leaders and teachers engaged in blended (using the flipped classroom model) and online (during COVID-19 crisis and beyond) teaching and learning; e-learning professionals (such as, instructional designers and e-tutors) of online and blended courses; instructional technologists; researchers as well as undergraduate and postgraduate university students studying education, educational technology and relevant fields.

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.

This book presents intelligent data analysis as a tool to fight against COVID-19 pandemic. The intelligent data analysis includes machine learning, natural language processing, and computer vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions are discussed in detail in the book to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. It also discusses primary warnings and alerts; tracking and prediction; data dashboards; diagnosis and prognosis; treatments and cures; and social control by the use of intelligent data analysis. It provides analysis reports, solutions using real-time data, and solution through web applications details.

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."

This book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.

This book provides readers the "big picture" and a comprehensive survey of the domain of big data processing systems. For the past decade, the Hadoop framework has dominated the world of big data processing, yet recently academia and industry have started to recognize its limitations in several application domains and thus, it is now gradually being replaced by a collection of engines that are dedicated to specific verticals (e.g. structured data, graph data, and streaming data). The book explores this new wave of systems, which it refers to as Big Data 2.0 processing systems. After Chapter 1 presents the general background of the big data phenomena, Chapter 2 provides an overview of various general-purpose big data processing systems that allow their users to develop various big data processing jobs for different application domains. In turn, Chapter 3 examines various systems that have been introduced to support the SQL flavor on top of the Hadoop infrastructure and provide competing and scalable performance in the processing of large-scale structured data. Chapter 4 discusses several systems that have been designed to tackle the problem of large-scale graph processing, while the main focus of Chapter 5 is on several systems that have been designed to provide scalable solutions for processing big data streams, and on other sets of systems that have been introduced to support the development of data pipelines between various types of big data processing jobs and systems. Next, Chapter 6 focuses on covering the emerging frameworks and systems in the domain of scalable machine learning and deep learning processing. Lastly, Chapter 7 shares conclusions and an outlook on future research challenges. This new and considerably enlarged second edition not only contains the completely new chapter 6, but also offers a refreshed content for the state-of-the-art in all domains of big data processing over the last years. Overall, the book offers a valuable reference guide for professional, students, and researchers in the domain of big data processing systems. Further, its comprehensive content will hopefully encourage readers to pursue further research on the subject.

Leverage the power of Talent Intelligence (TI) to make evidence-informed decisions that drive business performance by using data about people, skills, jobs, business functions and geographies. Improved access to people and business data has created huge opportunities for the HR function. However, simply having access to this data is not enough. HR professionals need to know how to analyse the data, know what questions to ask of it and where and how the insights from the data can add the most value. Talent Intelligence is a practical guide that explains everything HR professionals need to know to achieve this. It outlines what Talent Intelligence (TI) is why it's important, how to use it to improve business results and includes guidance on how HR professionals can build the business case for it. This book also explains how and why talent intelligence is different from workforce planning, sourcing research and standard predictive HR analytics and shows how to assess where in the organization talent intelligence can have the biggest impact and how to demonstrate the results to all stakeholders. Most importantly, this book covers KPIs and metrics for success, short-term and long-term TI goals, an outline of what success looks like and the skills needed for effective Talent Intelligence. It also features case studies from organizations including Philips, Barclays and Kimberly-Clark.

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.

This book constitutes revised selected papers of the 9th International Conference on Analysis of Images, Social Networks and Texts, AIST 2020, held in Moscow, Russia, in october 2020. Due to the COVID-19 pandemic the conference was held online. The 14 full papers, 9 short papers and 4 poster papers were carefully reviewed and selected from 108 qualified submissions. The papers are organized in topical sections on natural language processing; computer vision; social network analysis; data analysis and machine learning; theoretical machine learning and optimization; process mining; posters.

Hybride Leistungsbundel (HLB) dienen dazu, ein innovatives und nutzenorientiertes Produktverstandnis von Sach- und Dienstleistungen zu etablieren. Hochkomplexe Anlagen lassen sich durch diese integrierte Betrachtung von Sach- und Dienstleistungsanteilen deutlich besser vermarkten. Der Band liefert einen Uberblick zu diesem Konzept und stellt entsprechende Methoden und Werkzeuge zur Entwicklung von Sach- und Dienstleistungen vor. Dabei berucksichtigen die Autoren den gesamten Zyklus: von der Planung und Entwicklung bis zur Erbringung und Nutzung."

This book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system.

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 48th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains 8 invited papers dedicated to the memory of Prof. Dr. Roland Wagner. The topics covered include distributed database systems, NewSQL, scalable transaction management, strong consistency, caches, data warehouse, ETL, reinforcement learning, stochastic approximation, multi-agent systems, ontology, model-driven development, organisational modelling, digital government, new institutional economics and data governance.

What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company, from analysts and management to the C-Suite and the board. Through interviews and examples from data scientists and analytics leaders in a variety of industries, author Carl Anderson explains the analytics value chain you need to adopt when building predictive business models-from data collection and analysis to the insights and leadership that drive concrete actions. You'll learn what works and what doesn't, and why creating a data-driven culture throughout your organization is essential.Start from the bottom up: learn how to collect the right data the right way Hire analysts with the right skills, and organize them into teams Examine statistical and visualization tools, and fact-based story-telling methods Collect and analyze data while respecting privacy and ethics Understand how analysts and their managers can help spur a data-driven culture Learn the importance of data leadership and C-level positions such as chief data officer and chief analytics officer

This two-volume set, LNCS 11317 and 12318, constitutes the thoroughly refereed proceedings of the 4th International Joint Conference, APWeb-WAIM 2020, held in Tianjin, China, in September 2020. Due to the COVID-19 pandemic the conference was organizedas a fully online conference. The 42 full papers presented together with 17 short papers, and 6 demonstration papers were carefully reviewed and selected from 180 submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender Systems; Information Extraction and Retrieval; Machine Learning; Blockchain; Data Mining; Text Analysis and Mining; Spatial, Temporal and Multimedia Databases; Database Systems; and Demo.

Data has never mattered more. Our lives are increasingly shaped by it and how it is defined, collected and used. But who counts in the collection, analysis and application of data? This important book is the first to look at queer data - defined as data relating to gender, sex, sexual orientation and trans identity/history. The author shows us how current data practices reflect an incomplete account of LGBTQ lives and helps us understand how data biases are used to delegitimise the everyday experiences of queer people. Guyan demonstrates why it is important to understand, collect and analyse queer data, the benefits and challenges involved in doing so, and how we might better use queer data in our work. Arming us with the tools for action, this book shows how greater knowledge about queer identities is instrumental in informing decisions about resource allocation, changes to legislation, access to services, representation and visibility.

This volume presents the latest advances in statistics and data science, including theoretical, methodological and computational developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis, statistical modeling, and knowledge discovery and seeking. It includes contributions on analyzing and interpreting large, complex and aggregated datasets, and highlights numerous applications in economics, finance, computer science, political science and education. It gathers a selection of peer-reviewed contributions presented at the 16th Conference of the International Federation of Classification Societies (IFCS 2019), which was organized by the Greek Society of Data Analysis and held in Thessaloniki, Greece, on August 26-29, 2019.

Explore how User Research has been influenced over the years by a range of disciplines, such as HCI, usability, anthropology, cognitive psychology, ergonomics etc. This book aims to contribute to the User Research community and covers topics that will help UX professionals, students and stakeholders to gain a better understanding of what User Research is. Throughout the book you will acquire a practical skill set, ranging from how to get the research going, to building a case in order to receive the budget and resources needed. It will provide you with a clear account of how to organise your research, how to plan it, and how to manage stakeholders' expectations throughout the project. You'll see how to fit User Research into your organization and incorporate it through the different product development phases (Discovery, Alpha, Beta until Live), as well as how to grow a User Research team. Practical User Research reviews the methodologies used for User Research, looks at how to recruit participants along with how to collect and analyse data, finally focusing on how to interpret and present your findings. Cross-cultural research, accessibility and assisted digital research will also be discussed throughout this book. The final chapter gives you 10 project briefs, with which you will be able to apply your new skill set and put into practice what you have learnt. What You'll Learn Integrate user research into your business Apply user research to your product development cycle Review the appropriate processes necessary to carry out user research Take a pragmatic approach to user research, method by method Who This Book Is For Anyone that wants to understand more about user research.

There's growing interest in learning how to analyze streaming data in large-scale systems such as web traffic, financial transactions, machine logs, industrial sensors, and many others. But analyzing data streams at scale has been difficult to do well--until now. This practical book delivers a deep introduction to Apache Flink, a highly innovative open source stream processor with a surprising range of capabilities. Authors Ellen Friedman and Kostas Tzoumas show technical and nontechnical readers alike how Flink is engineered to overcome significant tradeoffs that have limited the effectiveness of other approaches to stream processing. You'll also learn how Flink has the ability to handle both stream and batch data processing with one technology. * Learn the consequences of not doing streaming well--in retail and marketing, IoT, telecom, and banking and finance* Explore how to design data architecture to gain the best advantage from stream processing* Get an overview of Flink's capabilities and features, along with examples of how companies use Flink, including in production* Take a technical dive into Flink, and learn how it handles time and stateful computation* Examine how Flink processes both streaming (unbounded) and batch (bounded) data without sacrificing performance

This book constitutes the proceedings of the 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018, held in Moscow, Russia, in July 2018. The 29 full papers were carefully reviewed and selected from 107 submissions (of which 26 papers were rejected without being reviewed). The papers are organized in topical sections on natural language processing; analysis of images and video; general topics of data analysis; analysis of dynamic behavior through event data; optimization problems on graphs and network structures; and innovative systems.

Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You'll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are: Forward selection component analysis Local feature selection Linking features and a target with a hidden Markov model Improvements on traditional stepwise selection Nominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets. Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts.

Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything about configuring hardware or software. By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned. What You Will Learn Discover the value of big data analytics that leverage the power of the cloud Get started with Databricks using SQL and Python in either Microsoft Azure or AWS Understand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture See how these tools are used in the real world Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free Who This Book Is For Data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. It is assumed that the reader has data experience, but perhaps minimal exposure to Apache Spark and Azure Databricks. The book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation.

This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications in medical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection.