Software Engineering for Big Data
Elsevier’s Information and Software Technology Journal
The main focus of this special issue will be to attract high quality publications pertaining to the development and provisioning of appropriate software engineering methods, techniques, processes, and tools that would support the efficient and effective development and maintenance of Big Data applications through the different stages of the software development lifecycle.
It has been widely reported that 90% of all the systematically collected data in the world has been generated over the past two years. This significant amount of data, being accumulated on a constant basis, comes from a fair share of user generated content as well as from corporate and industrial entities such as banks, internet, healthcare and transit systems, to name a few. There is immense value in harnessing data of this scale. The 2011 McKinsey report estimates that by implementing big data solutions, the US healthcare sector alone would benefit $300 billion annually. Substantial effort has thus already been dedicated to building large-scale data platforms, e.g., in-memory databases, distributed data processing architectures, and stream-processing tools, which facilitate the production or consumption of big data. However, it is time to surge ahead with advancements in the area of building complex application systems centred around Big Data.
In the past, the software and services sector has been struck powerfully by the lack of objective measures for undertaking large-scale software development. For example, it was reported by the Standish group in 2001 that over 84% of software projects either failed or became severely over-budget or over-time, wasting over $200B per year in North America. If history repeats, it is not difficult to estimate that the domain of Software Engineering in the context of Big Data will face great challenges with high-stake investments in mission critical application areas.
Important questions that need answering include, but are not limited to:
- What new requirements engineering modelling, specifications, and analysis techniques are needed to deal with the domain of Big Data and in the context of multiple stakeholders?
- What example reference architectures suit specific domain applications for Big Data systems — a product issue; and what is the role of reference architectures in rapid development of novel Big Data application systems — a process issue?
- What new scalable approaches are needed for testing Big Data application systems in the lab?
- What new architecture and design patterns are suited to specific qualities of Big Data application systems?
- What new programming paradigms better handle the programming of Big Data system components than traditional paradigms?
- What new kinds of development time and runtime tools are envisaged for improving the development and maintenance of Big Data systems?
- What new metrics are needed to measure or predict the quality, cost and timeframes of Big Data applications?
- How can we leverage operational Big Data generated by the Big Data Systems?
- How autonomic or self-adaptive systems and infrastructures can play a role on engineering and deploying efficient Big Data applications?
We solicit high quality, original papers that advance the state-of-the-art, and open new research directions in the area of Software Engineering for Big Data. The submissions, which are anticipated to be of scientific writing, may focus on research-theoretical work, evidence-based empirical studies including industrial experience, case studies and action-research, systematic literature reviews, and the like. Strong sections on Motivation, Related Work, claims of originality, Methodology, Analysis, Discussion, Interpretation, Validation, Implication, Conclusion, and Future Work are expected as applicable to the type of paper submitted.
· Submission Deadline: 1-March-2016
· Notification: [1 July, 2016]
· Major Revisions Due: 1-Sept-2016
· Re-reviews Completed: 1-Nov-2016
· Minor Revision Due: 1-Dec-2016
· Final recommendations: 15-Dec-2016
Authors will need to submit their manuscripts through the online submission and editorial system for Information and Software Technology Journal accessible at http://ees.elsevier.com/
Ebrahim Bagheri, Kostas Kontogiannis, Nazim Madhavji, Andriy Miranskyy.