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SEBDS 2018 : Software Engineering of Big Data Systems

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Link: https://easychair.org/conferences/?conf=sebds2018
 
When Jan 1, 2018 - Sep 30, 2019
Where Heidelberg
Submission Deadline Feb 28, 2018
Notification Due Jun 1, 2018
Final Version Due Jan 30, 2019
Categories    software engineering   variability management   variability-intensive systems   sebds: concepts and techniques
 

Call For Papers

*** Call for Chapters ***

We invite high-quality chapters for an edited book on

Software Engineering of Big Data Systems: Concepts and Techniques

A book to be published by CRC Press and edited by:

Ivan Mistrik, Computer Scientist & Software Researcher, Heidelberg, Germany
Matthias Galster, Senior Lecturer in Software Engineering at University of Canterbury, New Zealand
Rami Bahsoon,, Senior Lecturer in Software Engineering at University of Birmingham, UK
Ayse Basar Bener, Full Professor in the department of Mechanical and Industrial Engineering at Ryerson University, Toronto, Canada
Ian Gorton, Professor in Software Engineering and Director of Computer Science at Northeastern University at Seattle, USA

OVERVIEW
Big data is characterized by the seven V’s: volume (large amounts of data), velocity (continuously processed data in real time), variety (unstructured, semi-structured or structured data in different formats and from multiple and diverse sources), veracity (uncertainty and trustworthiness of data), validity (relevance of data to the problem to solve), volatility (constant change of input data), and value (how data and its analysis adds value). Big data systems are software applications that process and potentially generate big data. Such applications receive and process data from various diverse (usually distributed) sources, such as sensors, devices, whole networks, social networks, mobile devices or devices in an Internet-of-Things. They process high workloads of data and handle high requests for data. The idea is to use large amounts of data strategically and efficiently to provide additional intelligence.
This book will explore software engineering of big data systems, including topics related to requirements as well as architecture, detailed design, implementation, maintenance and operations. Software engineering is the application of a systematic approach to designing, operating and maintaining software systems and the study of all the activities involved in achieving the same. The software engineering discipline and research into software systems flourished with the advent of computers and the technological revolution ushered in by the World Wide Web and the Internet. Software systems have grown dramatically to the point of becoming ubiquitous. They have a significant impact on the global economy and on how we interact and communicate with each other and with computers using software in our daily lives. However, there have been major changes in the type of software systems developed over the years. In the past decade owing to breakthrough advancements in cloud and mobile computing technologies, unprecedented volumes of hitherto inaccessible data, referred to as big data, has become available to technology companies and business organizations farsighted and discerning enough to use it to create new products, and services generating astounding profits.
This book will focus on several research challenges of software engineering for developing big data systems, in particular by:
- surveying the existing software engineering literature on applying software engineering principles into developing and supporting big data systems
- identifying the fields of application for big data software systems
- investigating the software engineering knowledge areas that have seen research related to big data systems
- revealing the gaps in the knowledge areas that require more focus for big data systems development
- determining the open research challenges in each software engineering knowledge area that need to be met.


BACKGROUND TOPICS
All chapters should consider the practical application of the topic through case studies, experiments, empirical validation, or systematic comparisons with other data systems, as well as allow further approaches already in practice. The book intends to discuss systematic and disciplined approaches to building big data systems, dissemination of the state-of-the-art methods and techniques for representing and evaluating these systems.

FORMAT

Chapters are invited that synthesize existing knowledge on
relevant background topics and application areas in software engineering of big systems. Chapters should be accessible to senior undergraduate students and graduate students with a background in Computer Science, Information Science, Software Engineering, Software Architecture, Systems Engineering, Requirements Engineering, Software Design or related disciplines. Chapters are not expected to correspond to the description of a single research project or technique. Each chapter should clearly highlight three to five take-away messages or key lessons at the beginning of the chapter.

PROCEDURE AND IMPORTANT DATES

Please submit your chapter in PDF format to:
https://easychair.org/conferences/?conf=sebds2018

Chapters will be peer-reviewed by 3 reviewers. The authors participating in this publishing project will also be asked to review chapters by other contributors.
We recommend using Word from very beginning, since the sources for the final manuscript are required to be in Word. Please refer to Publisher’s Guideline for Authors at https://www.crcpress.com/assets/images/crc/T%26F%20Text%20Preparation%20Instructions_Disk_Word_v1_1.pdf
You are expected to collaborate on final editing of your chapter by Publisher’s editorial project manager.
Important Dates:
Call for Chapters: November 2017
Chapters due: 28 February 2018
First round of reviews: 1 March – 30 May 2018
Revisions: 1 June - 1 Sep 2018
Second round of reviews: 1 Sep - – 30 Sep 2018
Final version due: 30 January 2019
Complete manuscript due: 30 April 2019
Expected publication: Summer 2019
For further details please contact editors at sebds2018@easychair.org

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