Perspectives on Data Science for Software Engineering

Perspectives on Data Science for Software Engineering
Author: Tim Menzies,Laurie Williams,Thomas Zimmermann
Publsiher: Morgan Kaufmann
Total Pages: 408
Release: 2016-07-14
ISBN 10: 0128042613
ISBN 13: 9780128042618
Language: EN, FR, DE, ES & NL

Perspectives on Data Science for Software Engineering Book Review:

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains

Contemporary Empirical Methods in Software Engineering

Contemporary Empirical Methods in Software Engineering
Author: Michael Felderer,Guilherme Horta Travassos
Publsiher: Springer Nature
Total Pages: 525
Release: 2020-08-27
ISBN 10: 3030324893
ISBN 13: 9783030324896
Language: EN, FR, DE, ES & NL

Contemporary Empirical Methods in Software Engineering Book Review:

This book presents contemporary empirical methods in software engineering related to the plurality of research methodologies, human factors, data collection and processing, aggregation and synthesis of evidence, and impact of software engineering research. The individual chapters discuss methods that impact the current evolution of empirical software engineering and form the backbone of future research. Following an introductory chapter that outlines the background of and developments in empirical software engineering over the last 50 years and provides an overview of the subsequent contributions, the remainder of the book is divided into four parts: Study Strategies (including e.g. guidelines for surveys or design science); Data Collection, Production, and Analysis (highlighting approaches from e.g. data science, biometric measurement, and simulation-based studies); Knowledge Acquisition and Aggregation (highlighting literature research, threats to validity, and evidence aggregation); and Knowledge Transfer (discussing open science and knowledge transfer with industry). Empirical methods like experimentation have become a powerful means of advancing the field of software engineering by providing scientific evidence on software development, operation, and maintenance, but also by supporting practitioners in their decision-making and learning processes. Thus the book is equally suitable for academics aiming to expand the field and for industrial researchers and practitioners looking for novel ways to check the validity of their assumptions and experiences. Chapter 17 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Microservices in Big Data Analytics

Microservices in Big Data Analytics
Author: Anil Chaudhary,Chothmal Choudhary,Mukesh Kumar Gupta,Chhagan Lal,Tapas Badal
Publsiher: Springer Nature
Total Pages: 188
Release: 2019-11-26
ISBN 10: 9811501289
ISBN 13: 9789811501289
Language: EN, FR, DE, ES & NL

Microservices in Big Data Analytics Book Review:

These proceedings gather cutting-edge papers exploring the principles, techniques, and applications of Microservices in Big Data Analytics. The ICETCE-2019 is the latest installment in a successful series of annual conferences that began in 2011. Every year since, it has significantly contributed to the research community in the form of numerous high-quality research papers. This year, the conference’s focus was on the highly relevant area of Microservices in Big Data Analytics.

Software Engineering Perspectives in Intelligent Systems

Software Engineering Perspectives in Intelligent Systems
Author: Radek Silhavy,Petr Silhavy,Zdenka Prokopova
Publsiher: Springer Nature
Total Pages: 1150
Release: 2020-12-15
ISBN 10: 3030633225
ISBN 13: 9783030633226
Language: EN, FR, DE, ES & NL

Software Engineering Perspectives in Intelligent Systems Book Review:

This book constitutes the refereed proceedings of the 4th Computational Methods in Systems and Software 2020 (CoMeSySo 2020) proceedings. Software engineering, computer science and artificial intelligence are crucial topics for the research within an intelligent systems problem domain. The CoMeSySo 2020 conference is breaking the barriers, being held online. CoMeSySo 2020 intends to provide an international forum for the discussion of the latest high-quality research results.

Build a Career in Data Science

Build a Career in Data Science
Author: Emily Robinson,Jacqueline Nolis
Publsiher: Simon and Schuster
Total Pages: 354
Release: 2020-03-06
ISBN 10: 1638350159
ISBN 13: 9781638350156
Language: EN, FR, DE, ES & NL

Build a Career in Data Science Book Review:

Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder

Artificial Intelligence Methods For Software Engineering

Artificial Intelligence Methods For Software Engineering
Author: Meir Kalech,Rui Abreu,Mark Last
Publsiher: World Scientific
Total Pages: 456
Release: 2021-06-15
ISBN 10: 9811239932
ISBN 13: 9789811239939
Language: EN, FR, DE, ES & NL

Artificial Intelligence Methods For Software Engineering Book Review:

Software is an integral part of our lives today. Modern software systems are highly complex and often pose new challenges in different aspects of Software Engineering (SE).Artificial Intelligence (AI) is a growing field in computer science that has been proven effective in applying and developing AI techniques to address various SE challenges.This unique compendium covers applications of state-of-the-art AI techniques to the key areas of SE (design, development, debugging, testing, etc).All the materials presented are up-to-date. This reference text will benefit researchers, academics, professionals, and postgraduate students in AI, machine learning and software engineering.Related Link(s)

Agile Processes in Software Engineering and Extreme Programming Workshops

Agile Processes in Software Engineering and Extreme Programming     Workshops
Author: Rashina Hoda
Publsiher: Springer Nature
Total Pages: 159
Release: 2019-08-30
ISBN 10: 3030301265
ISBN 13: 9783030301262
Language: EN, FR, DE, ES & NL

Agile Processes in Software Engineering and Extreme Programming Workshops Book Review:

This open access book constitutes the research workshops, doctoral symposium and panel summaries presented at the 20th International Conference on Agile Software Development, XP 2019, held in Montreal, QC, Canada, in May 2019. XP is the premier agile software development conference combining research and practice. It is a hybrid forum where agile researchers, academics, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. Following this history, for both researchers and seasoned practitioners XP 2019 provided an informal environment to network, share, and discover trends in Agile for the next 20 years. Research papers and talks submissions were invited for the three XP 2019 research workshops, namely, agile transformation, autonomous teams, and large scale agile. This book includes 15 related papers. In addition, a summary for each of the four panels at XP 2019 is included. The panels were on security and privacy; the impact of the agile manifesto on culture, education, and software practices; business agility – agile’s next frontier; and Agile – the next 20 years.

Rethinking Productivity in Software Engineering

Rethinking Productivity in Software Engineering
Author: Caitlin Sadowski,Thomas Zimmermann
Publsiher: Apress
Total Pages: 310
Release: 2019-05-07
ISBN 10: 1484242211
ISBN 13: 9781484242216
Language: EN, FR, DE, ES & NL

Rethinking Productivity in Software Engineering Book Review:

Get the most out of this foundational reference and improve the productivity of your software teams. This open access book collects the wisdom of the 2017 "Dagstuhl" seminar on productivity in software engineering, a meeting of community leaders, who came together with the goal of rethinking traditional definitions and measures of productivity. The results of their work, Rethinking Productivity in Software Engineering, includes chapters covering definitions and core concepts related to productivity, guidelines for measuring productivity in specific contexts, best practices and pitfalls, and theories and open questions on productivity. You'll benefit from the many short chapters, each offering a focused discussion on one aspect of productivity in software engineering. Readers in many fields and industries will benefit from their collected work. Developers wanting to improve their personal productivity, will learn effective strategies for overcoming common issues that interfere with progress. Organizations thinking about building internal programs for measuring productivity of programmers and teams will learn best practices from industry and researchers in measuring productivity. And researchers can leverage the conceptual frameworks and rich body of literature in the book to effectively pursue new research directions. What You'll LearnReview the definitions and dimensions of software productivity See how time management is having the opposite of the intended effect Develop valuable dashboards Understand the impact of sensors on productivity Avoid software development waste Work with human-centered methods to measure productivity Look at the intersection of neuroscience and productivity Manage interruptions and context-switching Who Book Is For Industry developers and those responsible for seminar-style courses that include a segment on software developer productivity. Chapters are written for a generalist audience, without excessive use of technical terminology.

Think Like a Data Scientist

Think Like a Data Scientist
Author: Brian Godsey
Publsiher: Simon and Schuster
Total Pages: 328
Release: 2017-03-09
ISBN 10: 1638355207
ISBN 13: 9781638355205
Language: EN, FR, DE, ES & NL

Think Like a Data Scientist Book Review:

Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away

Software Engineering Perspectives and Application in Intelligent Systems

Software Engineering Perspectives and Application in Intelligent Systems
Author: Radek Silhavy,Roman Senkerik,Zuzana Kominkova Oplatkova,Petr Silhavy,Zdenka Prokopova
Publsiher: Springer
Total Pages: 484
Release: 2016-04-26
ISBN 10: 3319336223
ISBN 13: 9783319336220
Language: EN, FR, DE, ES & NL

Software Engineering Perspectives and Application in Intelligent Systems Book Review:

The volume Software Engineering Perspectives and Application in Intelligent Systems presents new approaches and methods to real-world problems, and in particular, exploratory research that describes novel approaches in the field of Software Engineering. Particular emphasis is laid on modern trends in selected fields of interest. New algorithms or methods in a variety of fields are also presented. The 5th Computer Science On-line Conference (CSOC 2016) is intended to provide an international forum for discussions on the latest research results in all areas related to Computer Science. The addressed topics are the theoretical aspects and applications of Computer Science, Artificial Intelligences, Cybernetics, Automation Control Theory and Software Engineering.

Data Science in Engineering and Management

Data Science in Engineering and Management
Author: Zdzislaw Polkowski,Sambit Kumar Mishra,Julian Vasilev
Publsiher: CRC Press
Total Pages: 160
Release: 2021-12-31
ISBN 10: 1000520846
ISBN 13: 9781000520842
Language: EN, FR, DE, ES & NL

Data Science in Engineering and Management Book Review:

This book brings insight into data science and offers applications and implementation strategies. It includes current developments and future directions and covers the concept of data science along with its origins. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis. Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origins, as well as presenting recent trends, highlighting future developments; discussing problems and offering solutions. It provides an overview of applications on data linked to engineering and management perspectives and also covers how data scientists, analysts, and program managers who are interested in productivity and improving their business can do so by incorporating a data science workflow effectively. This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.

Agile Data Science

Agile Data Science
Author: Russell Jurney
Publsiher: "O'Reilly Media, Inc."
Total Pages: 178
Release: 2013-10-15
ISBN 10: 1449326927
ISBN 13: 9781449326920
Language: EN, FR, DE, ES & NL

Agile Data Science Book Review:

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry

Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
Author: Chkoniya, Valentina
Publsiher: IGI Global
Total Pages: 653
Release: 2021-06-25
ISBN 10: 1799869865
ISBN 13: 9781799869863
Language: EN, FR, DE, ES & NL

Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Book Review:

The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students.

Advances in Information Systems Development

Advances in Information Systems Development
Author: Bo Andersson,Björn Johansson,Chris Barry,Michael Lang,Henry Linger,Christoph Schneider
Publsiher: Springer
Total Pages: 264
Release: 2019-08-02
ISBN 10: 3030229939
ISBN 13: 9783030229931
Language: EN, FR, DE, ES & NL

Advances in Information Systems Development Book Review:

This volume features a collection of papers on emerging concepts, significant insights, novel approaches and ideas in information systems research. It examines advances in information systems development in general, and their impact on the development of new methods, tools and management. The book contains invited papers selected from the 27th International Conference on Information Systems Development (ISD) held in Lund, Sweden, August 22 - 24, 2018. The revised and expanded papers present research that focuses on methods, tools and management in information systems development. These issues are significant as they provide the basis for organizations to identify new markets, support innovative technology deployment, and enable mobile applications to detect, sense, interpret and respond to the environment.

Agile Data Science 2 0

Agile Data Science 2 0
Author: Russell Jurney
Publsiher: "O'Reilly Media, Inc."
Total Pages: 352
Release: 2017-06-07
ISBN 10: 149196006X
ISBN 13: 9781491960066
Language: EN, FR, DE, ES & NL

Agile Data Science 2 0 Book Review:

Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track

Software Engineering Perspectives in Systems

Software Engineering Perspectives in Systems
Author: Radek Silhavy
Publsiher: Springer Nature
Total Pages: 755
Release: 2022-08-22
ISBN 10: 3031090705
ISBN 13: 9783031090707
Language: EN, FR, DE, ES & NL

Software Engineering Perspectives in Systems Book Review:

The study of software engineering and its applications to system engineering is critical in computer science research. Modern research methodologies, as well as the use of machine and statistical learning in software engineering research, are covered in this book. This book contains the refereed proceedings of the Software Engineering Perspectives in Systems part of the 11th Computer Science On-line Conference 2022 (CSOC 2022), which was held in April 2022 online.

Practitioner s Guide to Data Science

Practitioner   s Guide to Data Science
Author: Nasir Ali Mirza
Publsiher: BPB Publications
Total Pages: 242
Release: 2022-01-17
ISBN 10: 9391392873
ISBN 13: 9789391392871
Language: EN, FR, DE, ES & NL

Practitioner s Guide to Data Science Book Review:

Covers Data Science concepts, processes, and the real-world hands-on use cases. KEY FEATURES ● Covers the journey from a basic programmer to an effective Data Science developer. ● Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP. ● Implementation of MLOps using Microsoft Azure DevOps. DESCRIPTION "How is the Data Science project to be implemented?" has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do. This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects. The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it. By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models. WHAT YOU WILL LEARN ● Organize Data Science projects using CRISP-DM and Microsoft TDSP. ● Learn to acquire and explore data using Python visualizations. ● Get well versed with the implementation of data pre-processing and Feature Engineering. ● Understand algorithm selection, model development, and model evaluation. ● Hands-on with Azure ML Service, its architecture, and capabilities. ● Learn to use Azure ML SDK and MLOps for implementing real-world use cases. WHO THIS BOOK IS FOR This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions. TABLE OF CONTENTS 1. Data Science for Business 2. Data Science Project Methodologies and Team Processes 3. Business Understanding and Its Data Landscape 4. Acquire, Explore, and Analyze Data 5. Pre-processing and Preparing Data 6. Developing a Machine Learning Model 7. Lap Around Azure ML Service 8. Deploying and Managing Models

Evaluation of Novel Approaches to Software Engineering

Evaluation of Novel Approaches to Software Engineering
Author: Raian Ali,Hermann Kaindl,Leszek A. Maciaszek
Publsiher: Springer Nature
Total Pages: 495
Release: 2021-02-26
ISBN 10: 3030700062
ISBN 13: 9783030700065
Language: EN, FR, DE, ES & NL

Evaluation of Novel Approaches to Software Engineering Book Review:

This book constitutes selected, revised and extended papers of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2020, held in virtual format, in May 2020. The 19 revised full papers presented were carefully reviewed and selected from 96 submissions. The papers included in this book contribute to the understanding of relevant trends of current research on novel approaches to software engineering for the development and maintenance of systems and applications, specically with relation to: model-driven software engineering, requirements engineering, empirical software engineering, service-oriented software engineering, business process management and engineering, knowledge management and engineering, reverse software engineering, software process improvement, software change and configuration management, software metrics, software patterns and refactoring, application integration, software architecture, cloud computing, and formal methods.

Knowledge Management in the Development of Data Intensive Systems

Knowledge Management in the Development of Data Intensive Systems
Author: Ivan Mistrik,Matthias Galster,Bruce R. Maxim,Bedir Tekinerdogan
Publsiher: CRC Press
Total Pages: 342
Release: 2021-06-15
ISBN 10: 1000387410
ISBN 13: 9781000387414
Language: EN, FR, DE, ES & NL

Knowledge Management in the Development of Data Intensive Systems Book Review:

Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems. Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.

Managerial Perspectives on Intelligent Big Data Analytics

Managerial Perspectives on Intelligent Big Data Analytics
Author: Sun, Zhaohao
Publsiher: IGI Global
Total Pages: 335
Release: 2019-02-22
ISBN 10: 1522572783
ISBN 13: 9781522572787
Language: EN, FR, DE, ES & NL

Managerial Perspectives on Intelligent Big Data Analytics Book Review:

Big data, analytics, and artificial intelligence are revolutionizing work, management, and lifestyles and are becoming disruptive technologies for healthcare, e-commerce, and web services. However, many fundamental, technological, and managerial issues for developing and applying intelligent big data analytics in these fields have yet to be addressed. Managerial Perspectives on Intelligent Big Data Analytics is a collection of innovative research that discusses the integration and application of artificial intelligence, business intelligence, digital transformation, and intelligent big data analytics from a perspective of computing, service, and management. While highlighting topics including e-commerce, machine learning, and fuzzy logic, this book is ideally designed for students, government officials, data scientists, managers, consultants, analysts, IT specialists, academicians, researchers, and industry professionals in fields that include big data, artificial intelligence, computing, and commerce.