Big data analytics concerns the extraction of valuable, often critical, knowledge from data in order to use it intelligently for operations and decision-support in sciences, businesses, security management and many more industrial applications. Big data analytics deals with a vast amount of information from heterogeneous sources, with various characteristics, levels of trust, provenance as well as sensor observation methods and technologies. The aim of data analytics is to cost-effectively turn such high-volume, high-velocity and high-variety data into real-time knowledge and advanced situation awareness. The availability of big data is expected to significantly increase in the ever growing digital industries, which are building on technologies such as HPC, the Cloud, the Internet of Things and Cyber-Physical Systems. As a result, big data analytics technologies are gaining significant momentum in research and applications, while enabling the development of the next generation of innovative software systems.
The emerging software systems that use big data analytics are generating a new class of software systems altogether. These new systems are shifting existing and established software engineering principles, methods and tools towards new horizons and challenges in software development and operation.
For example, traditional software testing and verification may reach its limits due to the size of data and the changes of data in real-time. The traditional software life-cycle models may no longer address such paradigm shift in data volume and velocity as a result. We thus need novel software engineering methodologies and tools that are fit for purpose for developing, testing and operating this emerging new class of systems in the big data realm.
With the delivery of applications under flagship research projects using big data analytics, new avenues for novel and innovative software systems design and development are being explored. Hence many “predictive analytics”-based applications, which exploit big data for intelligent processing and reasoning, are rapidly increasing. These applications use advanced machine learning techniques which detect patterns in big data and reason on them with events, context and greater situation awareness. These are also being embedded as a new generation of software systems which specialise in operational decision-support.
This workshop aims to explore the impact of big data analytics on software engineering and advanced applications, serving as a forum for the exchange of ideas, solutions and experiences among researchers and practitioners.
Note: If you are interested in applying big data analytics to software engineering artefacts (e.g., considering data sources such as GitHub, Stack Overflow and Bugzilla), please consider submitting to the complementary special session on “Software Analytics”.
Topics of interest include, but are not restricted to:
Edward Curry NUI Galway, Insight Centre for Data Analytics, Ireland
Andreas Metzger University of Duisburg-Essen, Paluno (The Ruhr Institute for Software Technology), Germany
Zoheir Sabeur University of Southampton, Electronics and Computer Science, IT Innovation Centre, UK
Kenneth Anderson, U Colorado Boulder, US
Olga Baysal, U Montréal, CA
Arne Berre, SINTEF, NO
Hong-Mei Chen, U Hawaii at Manoa, US
Bojan Cukic, West Virginia U, US
Ralf Denzer, U Saarbrücken, Germany
Massimiliano Di Penta, U Sannio, IT
Fabiana Fournier, IBM, IL
Steven Frysinger, James Madison University, US
Feng Gao, Wuhan University of Science and Technology, CN
Joost Geurts, INRIA, FR
Souleiman Hasan, Maynooth University
Denis Havlik, Austrian Institute of Technology, AT
Dave Lewis, Trinity College Dublin
Tim Menzies, North Carolina State Univ., US
Mehdi Mirakhorli, Rochester Institute of Technology, US
Nazim Madhavji, U Western Ontario, CA
Zoltan Mann, U Duisburg-Essen, DE
Louie Qin, University of Huddersfield, UK
Clément Quinton, U Lille 1, FR