Title :
Research Directions for Engineering Big Data Analytics Software
Author :
Otero, Carlos E. ; Peter, Adrian
Author_Institution :
Florida Inst. of Technol., Melbourne, FL, USA
Abstract :
Many software startups and research and development efforts are actively trying to harness the power of big data and create software with the potential to improve almost every aspect of human life. As these efforts continue to increase, full consideration needs to be given to the engineering aspects of big data software. Since these systems exist to make predictions on complex and continuous massive datasets, they pose unique problems during specification, design, and verification of software that needs to be delivered on time and within budget. But, given the nature of big data software, can this be done? Does big data software engineering really work? This article explores the details of big data software, discusses the main problems encountered when engineering big data software, and proposes avenues for future research.
Keywords :
data analysis; formal specification; formal verification; software engineering; big data software; complex massive datasets; continuous massive datasets; engineering big data analytics software; formal specification; formal verification; research directions; software startups; Big data; Data models; Intelligent systems; Mathematical model; Software engineering; Software reliability; big data; design; intelligent systems; quality; reliability; software engineering; testing;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2014.76