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Data-Driven Intelligence in Wireless Networks: Concepts, Solutions, and Applications
by Muhammad Khalil Afzal Muhammad Ateeq Sung Won KimThis book highlights the importance of data-driven techniques to solve wireless communication problems. It presents a number of problems (e.g., related to performance, security, and social networking), and provides solutions using various data-driven techniques, including machine learning, deep learning, federated learning, and artificial intelligence.This book details wireless communication problems that can be solved by data-driven solutions. It presents a generalized approach toward solving problems using specific data-driven techniques. The book also develops a taxonomy of problems according to the type of solution presented and includes several case studies that examine data-driven solutions for issues such as quality of service (QoS) in heterogeneous wireless networks, 5G/6G networks, and security in wireless networks. The target audience of this book includes professionals, researchers, professors, and students working in the field of networking, communications, machine learning, and related fields.
Data-Driven Law: Data Analytics and the New Legal Services (Data Analytics Applications)
by Edward J. WaltersFor increasingly data-savvy clients, lawyers can no longer give "it depends" answers rooted in anecdata. Clients insist that their lawyers justify their reasoning, and with more than a limited set of war stories. The considered judgment of an experienced lawyer is unquestionably valuable. However, on balance, clients would rather have the considered judgment of an experienced lawyer informed by the most relevant information required to answer their questions. Data-Driven Law: Data Analytics and the New Legal Services helps legal professionals meet the challenges posed by a data-driven approach to delivering legal services. Its chapters are written by leading experts who cover such topics as: Mining legal data Computational law Uncovering bias through the use of Big Data Quantifying the quality of legal services Data mining and decision-making Contract analytics and contract standards In addition to providing clients with data-based insight, legal firms can track a matter with data from beginning to end, from the marketing spend through to the type of matter, hours spent, billed, and collected, including metrics on profitability and success. Firms can organize and collect documents after a matter and even automate them for reuse. Data on marketing related to a matter can be an amazing source of insight about which practice areas are most profitable. Data-driven decision-making requires firms to think differently about their workflow. Most firms warehouse their files, never to be seen again after the matter closes. Running a data-driven firm requires lawyers and their teams to treat information about the work as part of the service, and to collect, standardize, and analyze matter data from cradle to grave. More than anything, using data in a law practice requires a different mindset about the value of this information. This book helps legal professionals to develop this data-driven mindset.
Data-driven Marketing: Insights aus Wissenschaft und Praxis
by Silvia Boßow-Thies Christina Hofmann-Stölting Heike JochimsState-of-the-art Wissen zum Data-driven Marketing aus Forschung und PraxisFokussiert auf die entscheidenden Aspekte für ein erfolgreiches datengetriebenes MarketingAutoren sind Top-Experten aus der Praxis und der WissenschaftDieses Buch adressiert die entscheidenden Aspekte für ein erfolgreiches, datengetriebenes Marketing: Datenqualität, Datenanalyse, kreative, aber datenschutzkonforme und ethisch vertretbare Datennutzung. Die Herausgeberinnen haben dazu das aktuelle Know-how aus Wissenschaft und Praxis für die strategische und operative Marketingarbeit zusammengetragen. So ist ein wertvoller Impulsgeber und Leitfaden für Marketing-Professionals entstanden, die Ihre Marketingarbeit konsequent datenzentriert und kundenindividuell gestalten wollen. Dabei bleiben auch spezielle Aspekte wie eine visuelle Präsentation von Datenanalyse, der Einfluss der Tonalität einer Website auf die Werbewirksamkeit von Display Advertising und Prinzipien des digitalen Vertrauensaufbaus beim Einsatz von digitalen Kanälen nicht außen vor.Aus dem InhaltStrategischer Einsatz von Daten im Marketing Datenmanagement als Grundlage für MarketingentscheidungenSmarte Insights fürs Marketing (psychografisches Targeting, Programmatic Advertising, Uplift von Werbemaßnahmen, A/B-Testing)Data-driven Marketing in der realen Welt (Geointelligenz Im Outernet, digitale Komponenten bei Messen, Privacy Concerns in the Carsharing Economy)Datenschutz und die ethischen Grenzen der Datennutzung im Data-driven Marketing Mit Beiträgen von Prof. Dr. Silvia Boßow-Thies +++ Prof. Dr. Annette Corves +++ Prof. Dr. Nicole Fabisch +++ Prof. Dr. Lars-Gunnar Frahm +++ Dr. Björn Goerke +++ Prof. Dr. Goetz Greve +++ Prof. Dr. Susanne Hensel-Börner +++ Prof. Dr. Christina Hofmann-Stölting +++ Prof. Dr. Gregor Hopf +++ Luise Jacobs +++ Prof. Dr. Heike Jochims +++ Dr. Gwen Kaufmann +++ Carsten Köster +++ Terence Lutz +++ Prof. Dr. Doreén Pick +++ Dr. Dennis Proppe +++ Mareike Scheibe +++ Prof. Dr. Eva Schön +++ Prof. Dr. Manuel Stegemann +++ Prof. Dr. Thorsten Suwelack +++ Prof. Dr. Kai-Marcus Thäsler +++ Christian Westerkamp +++ Dr. Heike M. Wolters
Data Driven Mathematical Modeling in Agriculture: Tools and Technologies (River Publishers Series in Mathematical, Statistical and Computational Modelling for Engineering)
by Sandip Roy Sabyasachi Pramanik Rajesh BoseThe research in this book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models are utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.Technical topics discussed in the book include: Precision agriculture Machine learning Wireless sensor networks IoT Deep learning
Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces
by Oliver Lemon Olivier PietquinData driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present "end-to-end" in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expansion by simulation. In this book, we provide an overview of the current state of the field and of recent advances, with a specific focus on adaptivity.
Data-Driven Mining, Learning and Analytics for Secured Smart Cities: Trends and Advances (Advanced Sciences and Technologies for Security Applications)
by Chinmay Chakraborty Jerry Chun-Wei Lin Mamoun AlazabThis book provides information on data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart cities. This book aims to attract works on multidisciplinary research spanning across the computer science and engineering, environmental studies, services, urban planning and development, social sciences and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative solutions and big data-powered applications to cope with the real world challenges for building smart cities.
Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering (Water Science and Technology Library #67)
by Shahab Araghinejad"Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering" provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras. springer. com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.
Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis
by Mohammad Abdullah Al Faruque Sujit Rokka ChhetriThis book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.
Data-Driven Modelling and Predictive Analytics in Business and Finance: Concepts, Designs, Technologies, and Applications (Advances in Computational Collective Intelligence)
by Alex Khang Rashmi Gujrati Hayri Uygun R. K. Tailor Sanjaya Singh GaurData-driven and AI-aided applications are next-generation technologies that can be used to visualize and realize intelligent transactions in finance, banking, and business. These transactions will be enabled by powerful data-driven solutions, IoT technologies, AI-aided techniques, data analytics, and visualization tools. To implement these solutions, frameworks will be needed to support human control of intelligent computing and modern business systems. The power and consistency of data-driven competencies are a critical challenge, and so is developing explainable AI (XAI) to make data-driven transactions transparent.Data- Driven Modelling and Predictive Analytics in Business and Finance covers the need for intelligent business solutions and applications. Explaining how business applications use algorithms and models to bring out the desired results, the book covers: Data-driven modelling Predictive analytics Data analytics and visualization tools AI-aided applications Cybersecurity techniques Cloud computing IoT-enabled systems for developing smart financial systems This book was written for business analysts, financial analysts, scholars, researchers, academics, professionals, and students so they may be able to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices.
Data-driven Modelling and Scientific Machine Learning in Continuum Physics (Interdisciplinary Applied Mathematics #60)
by Krishna GarikipatiThis monograph takes the reader through recent advances in data-driven methods and machine learning for problems in science—specifically in continuum physics. It develops the foundations and details a number of scientific machine learning approaches to enrich current computational models of continuum physics, or to use the data generated by these models to infer more information on these problems. The perspective presented here is drawn from recent research by the author and collaborators. Applications drawn from the physics of materials or from biophysics illustrate each topic. Some elements of the theoretical background in continuum physics that are essential to address these applications are developed first. These chapters focus on nonlinear elasticity and mass transport, with particular attention directed at descriptions of phase separation. This is followed by a brief treatment of the finite element method, since it is the most widely used approach to solve coupled partial differential equations in continuum physics. With these foundations established, the treatment proceeds to a number of recent developments in data-driven methods and scientific machine learning in the context of the continuum physics of materials and biosystems. This part of the monograph begins by addressing numerical homogenization of microstructural response using feed-forward as well as convolutional neural networks. Next is surrogate optimization using multifidelity learning for problems of phase evolution. Graph theory bears many equivalences to partial differential equations in its properties of representation and avenues for analysis as well as reduced-order descriptions--all ideas that offer fruitful opportunities for exploration. Neural networks, by their capacity for representation of high-dimensional functions, are powerful for scale bridging in physics--an idea on which we present a particular perspective in the context of alloys. One of the most compelling ideas in scientific machine learning is the identification of governing equations from dynamical data--another topic that we explore from the viewpoint of partial differential equations encoding mechanisms. This is followed by an examination of approaches to replace traditional, discretization-based solvers of partial differential equations with deterministic and probabilistic neural networks that generalize across boundary value problems. The monograph closes with a brief outlook on current emerging ideas in scientific machine learning.
Data-Driven Modelling with Fuzzy Sets: A Neutrosophic Perspective (Intelligent Data-Driven Systems and Artificial Intelligence)
by Said Broumi D. Nagarajan Michael Gr. Voskoglou S. A. EdalatpanahFuzzy sets have long been employed to handle imprecise and uncertain information in the real world, but their limitations in dealing with incomplete and inconsistent data led to the emergence of neutrosophic sets. In this thought-provoking book, titled Data-Driven Modelling with Fuzzy Sets: A Neutrosophic Perspective, the authors delve into the theories and extensive applications of neutrosophic sets, ranging from neutrosophic graphs to single-valued trapezoidal neutrosophic sets and their practical implications in knowledge management, including student learning assessment, academic performance evaluation, and technical article screening. This comprehensive resource is intended to benefit mathematicians, physicists, computer experts, engineers, scholars, practitioners, and students seeking to deepen their understanding of neutrosophic sets and their practical applications in diverse fields. This book comprises 11 chapters that provide a thorough examination of neutrosophic set theory and its extensions. Each chapter presents valuable insights into various aspects of data-driven modeling with neutrosophic sets and explores their applications in different domains. The book covers a wide range of topics. The specific topics covered in the book include neutrosophic submodules, applications of neutrosophic sets, solutions to differential equations with neutrosophic uncertainty, cardinalities of neutrosophic sets, neutrosophic cylindrical coordinates, applications to graphs and climatic analysis, neutrosophic differential equation approaches to growth models, neutrosophic aggregation operators for decision making, and similarity measures for Fermatean neutrosophic sets. The diverse contributions from experts in the field, coupled with the constructive feedback from reviewers, ensure the book's high quality and relevance.This book presents a qualitative assessment of big data in the education sector using linguistic quadripartitioned single-valued neutrosophic soft sets showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity index covers scientific evaluation of student academic performance using single-valued neutrosophic Markov chain illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment examines estimation of distribution algorithms based on multiple-attribute group decision-making to evaluate teaching quality With its wealth of knowledge, this book aims to inspire further research and innovation in the field of neutrosophic sets and their extensions, providing a valuable resource for scholars, practitioners, and students alike.
Data-Driven Modelling with Fuzzy Sets: Embracing Uncertainty (Intelligent Data-Driven Systems and Artificial Intelligence)
by Said Broumi D. Nagarajan Michael Gr. Voskoglou S. A. EdalatpanahZadeh introduced in 1965 the theory of fuzzy sets, in which truth values are modelled by numbers in the unit interval [0, 1], for tackling mathematically the frequently appearing in everyday life partial truths. In a second stage, when membership functions were reinterpreted as possibility distributions, fuzzy sets were extensively used to embrace uncertainty modelling. Uncertainty is defined as the shortage of precise knowledge or complete information and possibility theory is devoted to the handling of incomplete information. Zadeh articulated the relationship between possibility and probability, noticing that what is probable must preliminarily be possible. Following the Zadeh’s fuzzy set, various generalizations (intuitionistic, neutrosophic, rough, soft sets, etc.) have been introduced enabling a more effective management of all types of the existing in real world uncertainty. This book presents recent theoretical advances and applications of fuzzy sets and their extensions to Science, Humanities and Education.This book: Presents a qualitative assessment of big data in the education sector using linguistic Quadri partitioned single valued neutrosophic soft sets. Showcases application of n-cylindrical fuzzy neutrosophic sets in education using neutrosophic affinity degree and neutrosophic similarity Index. Covers scientific evaluation of student academic performance using single value neutrosophic Markov chain. Illustrates multi-granulation single-valued neutrosophic probabilistic rough sets for teamwork assessment. Examines estimation of distribution algorithm based on multiple attribute group decision-making to evaluate teaching quality. It is primarily written for Senior undergraduate and graduate students and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering.
Data-driven Multivalence in the Built Environment (S.M.A.R.T. Environments)
by Nimish BiloriaThis book sets the stage for understanding how the exponential escalation of digital ubiquity in the contemporary environment is being absorbed, modulated, processed and actively used for enhancing the performance of our built environment. S.M.A.R.T., in this context, is thus used as an acronym for Systems & Materials in Architectural Research and Technology, with a specific focus on interrogating the intricate relationship between information systems and associative material, cultural and socioeconomic formations within the built environment. This interrogation is deeply rooted in exploring inter-disciplinary research and design strategies involving nonlinear processes for developing meta-design systems, evidence based design solutions and methodological frameworks, some of which, are presented in this issue. Urban health and wellbeing, urban mobility and infrastructure, smart manufacturing, Interaction Design, Urban Design & Planning as well as Data Science, as prominent symbiotic domains constituting the Built Environment are represented in this first book in the S.M.A.R.T. series. The spectrum of chapters included in this volume helps in understanding the multivalence of data from a socio-technical perspective and provides insight into the methodological nuances involved in capturing, analysing and improving urban life via data driven technologies.
Data-Driven Optimization and Knowledge Discovery for an Enterprise Information System
by Krishnendu Chakrabarty Qing Duan Jun ZengThis book provides a comprehensive set of optimization and prediction techniques for an enterprise information system. Readers with a background in operations research, system engineering, statistics, or data analytics can use this book as a reference to derive insight from data and use this knowledge as guidance for production management. The authors identify the key challenges in enterprise information management and present results that have emerged from leading-edge research in this domain. Coverage includes topics ranging from task scheduling and resource allocation, to workflow optimization, process time and status prediction, order admission policies optimization, and enterprise service-level performance analysis and prediction. With its emphasis on the above topics, this book provides an in-depth look at enterprise information management solutions that are needed for greater automation and reconfigurability-based fault tolerance, as well as to obtain data-driven recommendations for effective decision-making.
The Data-driven Organization: Using Data for the Success of Your Company (Business Guides on the Go)
by Jonas RashediData has become an indispensable success factor for every company. However, the road towards a data-driven organization is paved with numerous challenges. This book presents a process model for the path to a data-driven company and provides recommendations for the design of all relevant fields of action: Which structures need to be created? Which systems and processes have proven beneficial? How can the quality of the data be ensured and what requirements exist for a data-driven organization in the areas of governance and communication? And last but not least: How can employees be brought along on the journey and what implications does the data-driven organization have for our corporate culture? The book presents an orientation and action framework for the strategic and operational design of a data-driven organization and is valuable for managers who are involved in data management in companies and organizations.
Data-driven Organization Design
by Rupert MorrisonData is changing the nature of competition. Making sense of it is tough. Taking advantage of it is tougher. There is a business opportunity for organizations to use data and analytics to transform business performance. Organizations are by their nature complex. They are a constantly evolving system made up of objectives, processes designed to meet those objectives, people with skills and behaviours to do the work required, and all of this organised in a governance structure. It is dynamic, fluid and constantly moving over time. Using data and analytics you can connect all the elements of the system to design an environment for people to perform; an organization which has the right people, in the right place, doing the right things, at the right time. Only when everyone performs to their potential, do organizations have a hope of getting and sustaining a competitive edge. Data-driven Organization Design provides a practical framework for HR and Organization design practitioners to build a baseline of data, set objectives, carry out fixed and dynamic process design, map competencies, and right-size the organization. It shows how to collect the right data, present it meaningfully and ask the right questions of it. Whether looking to implement a long term transformation, large redesign, or a one-off small scale project, this book will show you how to make the most of your organizational data and analytics to drive business performance.
Data-Driven Personalization: How to Use Consumer Insights to Generate Customer Loyalty
by Zontee HouMake your marketing truly resonate by personalizing every message, powered by data, research and behavioral economics. To break through the noise, marketers today need to be hyper-relevant to their customers. To do that takes data and a deep understanding of your audience. Data-Driven Personalization breaks down the best ways to reach new customers and better engage your best customers. By combining principles of persuasion, behavioral economics and industry research, this book provides readers with an actionable blueprint for how to implement a customer-centric approach to marketing that will drive results. The book is broken into six parts that detail everything from what data is most valuable for personalization to how to build a data-driven marketing team that's prepared for the next five years and beyond. Each chapter includes actionable insights to guide marketers as they implement a data-driven personalization approach to their strategy. The chapters also focus on hands-on tactics like identifying messages that will move the needle with customers, how to generate seamless omnichannel experiences and how to balance personalization with data privacy. The book features case studies from top brands, including FreshDirect, Target, Adobe, Cisco and Spotify.
Data-Driven Prediction for Industrial Processes and Their Applications (Information Fusion and Data Science)
by Jun Zhao Wei Wang Chunyang ShengThis book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
Data-Driven Process Discovery and Analysis: Third IFIP WG 2.6, 2.12 International Symposium, SIMPDA 2013, Riva del Garda, Italy, August 30, 2013, Revised Selected Papers (Lecture Notes in Business Information Processing #203)
by Paolo Ceravolo Rafael Accorsi Philippe Cudre-MaurouxThis book constitutes the thoroughly refereed proceedings of the Third International Symposium on Data-Driven Process Discovery and Analysis held in Riva del Garda, Italy, in August 2013. The six revised full papers were carefully selected from 18 submissions. Following the event, authors were given the opportunity to improve their papers with the insights they gained from the symposium. The selected papers cover theoretical issues related to process representation, discovery and analysis or provide practical and operational experiences in process discovery and analysis.
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.