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Data-Driven Process Discovery and Analysis: 8th IFIP WG 2.6 International Symposium, SIMPDA 2018, Seville, Spain, December 13–14, 2018, and 9th International Symposium, SIMPDA 2019, Bled, Slovenia, September 8, 2019, Revised Selected Papers (Lecture Notes in Business Information Processing #379)
by Paolo Ceravolo Maurice Van Keulen María Teresa Gómez-LópezThis book constitutes revised selected papers from the 8th and 9th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2018, held in Seville, Spain, on December 13–14, 2018, and SIMPDA 2019, held in Bled, Slovenia, on September 8, 2019. From 16 submissions received for SIMPDA 2018 and 9 submissions received for SIMPDA 2019, 3 papers each were carefully reviewed and selected for presentation in this volume. They cover theoretical issues related to process representation, discovery, and analysis or provide practical and operational examples of their application.
Data-Driven Process Discovery and Analysis: 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Neuchatel, Switzerland, December 6-8, 2017, Revised Selected Papers (Lecture Notes in Business Information Processing #340)
by Paolo Ceravolo Maurice Van Keulen Kilian StoffelThis book constitutes the revised selected papers from the 7th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017, held in Neuchatel, Switzerland, in December 2017. The 6 papers presented in this volume were carefully reviewed and selected from 19 submissions. They cover theoretical issues related to process representation, discovery, and analysis or provide practical and operational examples of their application.
Data-Driven Process Discovery and Analysis: 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers (Lecture Notes in Business Information Processing #244)
by Paolo Ceravolo Stefanie Rinderle-MaThis book constitutes the thoroughlyrefereed proceedings of the Fourth InternationalSymposium on Data-Driven Process Discovery and Analysis held in Riva del Milan,Italy, in November 2014. The five revised full papers were carefully selected from21 submissions. Following the event, authors weregiven the opportunity to improve their papers with the insights they gainedfrom the symposium. During this edition, the presentations and discussionsfrequently focused on the implementation of process mining algorithms incontexts where the analytical process is fed by data streams. The selectedpapers underline the most relevant challenges identified and propose novelsolutions and approaches for their solution.
Data-Driven Process Discovery and Analysis: Second Ifip Wg 2. 6, 2. 12 International Symposium, Simpda 2012, Campione D'italia, Italy, June 18-20, 2012, Revised Selected Papers (Lecture Notes In Business Information Processing #162)
by Paolo Ceravolo Stefanie Rinderle-Ma Christian GuetlThis book constitutes the thoroughlyrefereed proceedings of the Fourth InternationalSymposium on Data-Driven Process Discovery and Analysis held in Riva del Milan,Italy, in November 2014. The five revised full papers were carefully selected from21 submissions. Following the event, authors weregiven the opportunity to improve their papers with the insights they gainedfrom the symposium. During this edition, the presentations and discussionsfrequently focused on the implementation of process mining algorithms incontexts where the analytical process is fed by data streams. The selectedpapers underline the most relevant challenges identified and propose novelsolutions and approaches for their solution.
Data-Driven Process Discovery and Analysis: 4th International Symposium, SIMPDA 2014, Milan, Italy, November 19-21, 2014, Revised Selected Papers (Lecture Notes in Business Information Processing #237)
by Barbara Russo Paolo Ceravolo Rafael AccorsiThis book constitutes the thoroughlyrefereed proceedings of the Fourth InternationalSymposium on Data-Driven Process Discovery and Analysis held in Riva del Milan,Italy, in November 2014. The five revised full papers were carefully selected from21 submissions. Following the event, authors weregiven the opportunity to improve their papers with the insights they gainedfrom the symposium. During this edition, the presentations and discussionsfrequently focused on the implementation of process mining algorithms incontexts where the analytical process is fed by data streams. The selectedpapers underline the most relevant challenges identified and propose novelsolutions and approaches for their solution.
Data-Driven Reproductive Health: Role of Bioinformatics and Machine Learning Methods
by Abhishek Sengupta Priyanka Narad Gaurav Majumdar Deepak ModiThis book provides insight into the transformative impact of data-driven approaches on reproductive health. Chapters cover a wealth of intricate algorithms of genomic analysis, predictive modeling, and personalized treatment strategies, providing an up-to-date view of the reproductive healthcare landscape. With more than 20 code-based examples, the book decodes complex biological data using bioinformatics and machine learning and provides valuable insights into fertility, genetic disorders, and personalized medicine. This book is relevant for healthcare professionals, researchers, and students in the fields of reproductive medicine, bioinformatics, and genetics.
Data-driven Retailing: A Non-technical Practitioners' Guide (Management for Professionals)
by Louis-Philippe KerkhoveThis book provides retail managers with a practical guide to using data. It covers three topics that are key areas of innovation for retailers: Algorithmic Marketing, Logistics, and Pricing. Use cases from these areas are presented and discussed in a conceptual and comprehensive manner. Retail managers will learn how data analysis can be used to optimize pricing, customer loyalty and logistics without complex algorithms.The goal of the book is to help managers ask the right questions during a project, which will put them on the path to making the right decisions. It is thus aimed at practitioners who want to use advanced techniques to optimize their retail organization.
Data-Driven Scheduling of Semiconductor Manufacturing Systems (Advanced and Intelligent Manufacturing in China)
by Li Li Qingyun Yu Kuo-Yi Lin Yumin Ma Fei QiaoThis book systematically discusses the intelligent scheduling problem of complex semiconductor manufacturing systems from theory to method and then to application. The main contents include data-driven scheduling framework of semiconductor manufacturing system, data preprocessing of semiconductor manufacturing system, correlation analysis of performance index of semiconductor production line, intelligent release control strategy, dynamic dispatching rules simulating pheromone mechanism, and load balancing dynamic scheduling of semiconductor production line, performance index-driven dynamic scheduling method of semiconductor production line, scheduling trend of semi-conductor manufacturing system in big data environment.This book aims to provide readers with valuable reference and assistance in the theoretical methods, techniques, and application cases of semiconductor manufacturing systems and their intelligent scheduling.
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
by Steven L. Brunton J. Nathan KutzData-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
Data-Driven Security
by Jay Jacobs Bob RudisUncover hidden patterns of data and respond with countermeasures Security professionals need all the tools at their disposal to increase their visibility in order to prevent security breaches and attacks. This careful guide explores two of the most powerful ? data analysis and visualization. You'll soon understand how to harness and wield data, from collection and storage to management and analysis as well as visualization and presentation. Using a hands-on approach with real-world examples, this book shows you how to gather feedback, measure the effectiveness of your security methods, and make better decisions. Everything in this book will have practical application for information security professionals. Helps IT and security professionals understand and use data, so they can thwart attacks and understand and visualize vulnerabilities in their networks Includes more than a dozen real-world examples and hands-on exercises that demonstrate how to analyze security data and intelligence and translate that information into visualizations that make plain how to prevent attacks Covers topics such as how to acquire and prepare security data, use simple statistical methods to detect malware, predict rogue behavior, correlate security events, and more Written by a team of well-known experts in the field of security and data analysis Lock down your networks, prevent hacks, and thwart malware by improving visibility into the environment, all through the power of data and Security Using Data Analysis, Visualization, and Dashboards.
Data-Driven SEO with Python: Solve SEO Challenges with Data Science Using Python
by Andreas VoniatisSolve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.This book is ideal for SEO professionals who want to take their industry skills to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both. What You'll LearnSee how data science works in the SEO contextThink about SEO challenges in a data driven wayApply the range of data science techniques to solve SEO issuesUnderstand site migration and relaunches areWho This Book Is ForSEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.
Data-Driven Services with Silverlight 2: Data Access and Web Services for Rich Internet Applications
by John PapaThis comprehensive book teaches you how to build data-rich business applications with Silverlight 2 that draw on multiple sources of data. Packed with reusable examples, Data-Driven Services with Silverlight 2 covers all of the data access and web service tools you need, including data binding, the LINQ data querying component, RESTful and SOAP web service support, cross-domain web service calls, and Microsoft's new ADO.NET Data Services and the ADO.NET Entity Framework. With this book, you will: Know when and how to use LINQ to JSON, LINQ to XML, and LINQ to Objects Learn how Silverlight 2 applications bind, pass, read, save, query, and present data Discover how your application can call web services to work with SOAP, REST, RSS, AtomPub, POX and JSON Design REST, ASMX, and WCF web services that communicate with Silverlight 2 Harness RESTful web services such as Digg, Amazon, and Twitter Retrieve and save data using the new Entity Framework and WCF Work with RESTful ADO.NET Data Services and its Silverlight client library to move data between your Silverlight application and a database Data-Driven Services with Silverlight 2 offers many tips and tricks for building data-rich business applications, and covers the scenarios you're most likely to encounter. Complete examples in C# and VB can be downloaded from the book's companion website.
Data Driven Smart Manufacturing Technologies and Applications (Springer Series in Advanced Manufacturing)
by Weidong Li Yuchen Liang Sheng WangThis book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.
Data-Driven Storytelling (AK Peters Visualization Series)
by Nathalie Henry Riche Christophe Hurter Nicholas Diakopoulos Sheelagh CarpendaleThis book presents an accessible introduction to data-driven storytelling. Resulting from unique discussions between data visualization researchers and data journalists, it offers an integrated definition of the topic, presents vivid examples and patterns for data storytelling, and calls out key challenges and new opportunities for researchers and practitioners.
Data Driven Strategies: Theory and Applications
by Wang Jianhong Ricardo A. Ramirez-Mendoza Ruben Morales-MenendezA key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.
Data-Driven Systems and Intelligent Applications (Intelligent Data-Driven Systems and Artificial Intelligence)
by Mangesh M. Ghonge N. Krishna Chaitanya Pradeep N Harish Garg Alessandro BrunoThis book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with artificial intelligence. It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing.This book: Discusses intelligent systems and cloud computing with the help of artificial intelligence and machine learning. Showcases the importance of machine learning and deep learning in data-driven and cloud-based applications to improve their capabilities and intelligence. Presents the latest developments in data-driven and cloud applications with respect to their design and architecture. Covers artificial intelligence methods along with their experimental result analysis through data processing tools. Presents the advent of machine learning, deep learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering.
Data-Driven Technologies and Artificial Intelligence in Supply Chain: Tools and Techniques (Intelligent Data-Driven Systems and Artificial Intelligence)
by Mahesh Chand Vineet Jain Puneeta AjmeraThis book highlights the importance of data-driven technologies and artificial intelligence in supply chain management. It covers important concepts such as enabling technologies in Industry 4.0, the impact of artificial intelligence, and data-driven technologies in lean manufacturing. "Provides solutions to solve complex supply chain management issues using artificial intelligence and data-driven technologies" Emphasizes the impact of a data-driven supply chain on quality management "Discusses applications of artificial intelligence, and data-driven technologies in the service industry, and lean manufacturing" Highlights the barriers to implementing artificial intelligence in small and medium enterprises Presents a better understanding of different risks such as procurement risks, process risks, demand risks, transportation risks, and operational risks The book comprehensively discusses the applications of artificial intelligence and data-driven technologies in supply chain management for diverse fields such as service industries, manufacturing industries, and healthcare. It further covers the impact of artificial intelligence and data-driven technologies in managing the FMGC supply chain. It will be a valuable resource for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, industrial engineering, manufacturing engineering, production engineering, and computer engineering.
Data Economy in the Digital Age (Data-Intensive Research)
by Samiksha Shukla Kritica Bisht Kapil Tiwari Shahid BashirThe book is a comprehensive guide that explores the concept of data economy and its implications in today's world. The book discusses the principles and components of the ecosystem, the challenges and opportunities presented by data monetization, and the potential risks related to data privacy. Real-life examples and case studies are included to understand the concepts better. The book is suitable for individuals in data science, economics, business, and technology and for students, academics, and policymakers. It is an excellent read for anyone interested in the data economy.
Data-Enabled Analytics: DEA for Big Data (International Series in Operations Research & Management Science #312)
by Joe Zhu Vincent CharlesThis book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
Data Engineering: Mining, Information and Intelligence (International Series in Operations Research & Management Science #132)
by John Talburt Terry M. Talley Yupo ChanDATA ENGINEERING: Mining, Information, and Intelligence describes applied research aimed at the task of collecting data and distilling useful information from that data. Most of the work presented emanates from research completed through collaborations between Acxiom Corporation and its academic research partners under the aegis of the Acxiom Laboratory for Applied Research (ALAR). Chapters are roughly ordered to follow the logical sequence of the transformation of data from raw input data streams to refined information. Four discrete sections cover Data Integration and Information Quality; Grid Computing; Data Mining; and Visualization. Additionally, there are exercises at the end of each chapter. The primary audience for this book is the broad base of anyone interested in data engineering, whether from academia, market research firms, or business-intelligence companies. The volume is ideally suited for researchers, practitioners, and postgraduate students alike. With its focus on problems arising from industry rather than a basic research perspective, combined with its intelligent organization, extensive references, and subject and author indices, it can serve the academic, research, and industrial audiences.
Data Engineering and Applications: Proceedings of the International Conference, IDEA 2K22, Volume 2 (Lecture Notes in Electrical Engineering #1189)
by Jitendra Agrawal Rajesh K. Shukla Sanjeev Sharma Chin-Shiuh ShiehThis book comprises select proceedings from the 4th International Conference on Data, Engineering, and Applications (IDEA 2022). The contents discuss novel contributions and latest developments in the domains of data structures and data management algorithms, information retrieval and information integration, social data analytics, IoT and data intelligence, Industry 4.0 and digital manufacturing, data fusion, natural language processing, geolocation handling, image, video and signal processing, ICT applications and e-governance, among others. This book is of interest to researchers in academia and industry working in big data, data mining, machine learning, data science, and their associated learning systems and applications.
Data Engineering and Applications: Proceedings of the International Conference, IDEA 2K22, Volume 1 (Lecture Notes in Electrical Engineering #1146)
by Jitendra Agrawal Rajesh K. Shukla Sanjeev Sharma Chin-Shiuh ShiehThis book comprises select proceedings from the 4th International Conference on Data, Engineering, and Applications (IDEA 2022). The contents discuss novel contributions and latest developments in the domains of data structures and data management algorithms, information retrieval and information integration, social data analytics, IoT and data intelligence, Industry 4.0 and digital manufacturing, data fusion, natural language processing, geolocation handling, image, video and signal processing, ICT applications and e-governance, among others. This book is of interest to researchers in academia and industry working in big data, data mining, machine learning, data science, and their associated learning systems and applications.
Data, Engineering and Applications: Select Proceedings of IDEA 2021 (Lecture Notes in Electrical Engineering #907)
by Sanjeev Sharma Sheng-Lung Peng Jitendra Agrawal Rajesh K. Shukla Dac-Nhuong LeThe book contains select proceedings of the 3rd International Conference on Data, Engineering, and Applications (IDEA 2021). It includes papers from experts in industry and academia that address state-of-the-art research in the areas of big data, data mining, machine learning, data science, and their associated learning systems and applications. This book will be a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of big data applications.
Data, Engineering and Applications: Volume 2
by Rajesh Kumar Shukla Jitendra Agrawal Sanjeev Sharma Geetam Singh TomerThis book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.
Data Engineering and Communication Technology: Proceedings of ICDECT 2020 (Lecture Notes on Data Engineering and Communications Technologies #63)
by K. Ashoka Reddy B. Rama Devi Boby George K. Srujan RajuThis book includes selected papers presented at the 4th International Conference on Data Engineering and Communication Technology (ICDECT 2020), held at Kakatiya Institute of Technology & Science, Warangal, India, during 25–26 September 2020. It features advanced, multidisciplinary research towards the design of smart computing, information systems and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment and industry.