The 26th IEEE ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel Distributed Computing (SNPD2022) brings together researchers, scientists, engineers, industry practitioners, and students to discuss, encourage and exchange new ideas, research results, and experiences on all aspects of computer and information science SNPD2022 aims to facilitate cross fertilizations among the key technology enabling areas

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

This book presents software engineering methods in the context of the intelligent systems. It discusses real-world problems and exploratory research describing novel approaches and applications of software engineering, software design and algorithms. The book constitutes the refereed proceedings of the Software Engineering Methods in Intelligent Algorithms Section of the 8th Computer Science On-line Conference 2019 (CSOC 2019), held on-line in April 2019.

Are you attracted by the promises of agile methods but put off by the fanaticism of many agile texts? Would you like to know which agile techniques work, which ones do not matter much, and which ones will harm your projects? Then you need Agile!: the first exhaustive, objective review of agile principles, techniques and tools. Agile methods are one of the most important developments in software over the past decades, but also a surprising mix of the best and the worst. Until now every project and developer had to sort out the good ideas from the bad by themselves. This book spares you the pain. It offers both a thorough descriptive presentation of agile techniques and a perceptive analysis of their benefits and limitations. Agile! serves first as a primer on agile development: one chapter each introduces agile principles, roles, managerial practices, technical practices and artifacts. A separate chapter analyzes the four major agile methods: Extreme Programming, Lean Software, Scrum and Crystal. The accompanying critical analysis explains what you should retain and discard from agile ideas. It is based on Meyer’s thorough understanding of software engineering, and his extensive personal experience of programming and project management. He highlights the limitations of agile methods as well as their truly brilliant contributions — even those to which their own authors do not do full justice. Three important chapters precede the core discussion of agile ideas: an overview, serving as a concentrate of the entire book; a dissection of the intellectual devices used by agile authors; and a review of classical software engineering techniques, such as requirements analysis and lifecycle models, which agile methods criticize. The final chapters describe the precautions that a company should take during a transition to agile development and present an overall assessment of agile ideas. This is the first book to discuss agile methods, beyond the brouhaha, in the general context of modern software engineering. It is a key resource for projects that want to combine the best of established results and agile innovations.

This book aims to give you a head start by providing a detailed down-to-earth account of how one Swedish company implemented Scrum and XP with a team of approximately 40 people and how they continuously improved their process over a year's time. Under the leadership of Henrik Kniberg they experimented with different team sizes, different sprint lengths, different ways of defining "done", different formats for product backlogs and sprint backlogs, different testing strategies, different ways of doing demos, different ways of synchronizing multiple Scrum teams, etc. They also experimented with XP practices - different ways of doing continuous build, pair programming, test driven development, etc, and how to combine this with Scrum. This second edition is an annotated version, a "director's cut" where Henrik reflects upon the content and shares new insights gained since the first version of the book.

This book provides a comprehensive survey on related work for radio link quality estimation, which covers the characteristics of low-power links, the fundamental concepts of link quality estimation in wireless sensor networks, a taxonomy of existing link quality estimators and their performance analysis. It then shows how link quality estimation can be used for designing protocols and mechanisms such as routing and hand-off. The final part is dedicated to radio interference estimation, generation and mitigation.