TY - GEN
T1 - Characteristics and requirements of big data analytics applications
AU - Al-Jaroodi, Jameela
AU - Mohamed, Nader
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/6
Y1 - 2017/1/6
N2 - Big data analytics picked up pace to offer meaningful information based on analyzing big data. Big data have various distinctive characteristics that together have led to overwhelming the available infrastructures both hardware and software. Moreover, this led to creating further complexities when considering the software engineering aspects for big data applications development. Introducing cloud computing into the mix further complicates the issues. Most of the current efforts in big data analytics target finding ways to store, organize and process big data effectively in addition to investigating cloud-based big data applications perspectives. However, we noticed there is not much emphasis on defining or enhancing the software development process for developing such applications. Like any software system, it is important to identify the types of applications, requirements and constraints and use this knowledge in a well-defined process model to design and develop effective cloud-based and traditional big data analytics applications. In this paper, we investigate these applications and attempt to identify the general requirements and constraints to better support the software development process. One of the important aspects is being able to distinguish real-time from delay-tolerant big data analytics applications. When the requirements and time constraints are identified, we can decide on the type of infrastructure and software architectures that will best match these requirements. As a result, we design and deliver effective and useful big data analytics applications.
AB - Big data analytics picked up pace to offer meaningful information based on analyzing big data. Big data have various distinctive characteristics that together have led to overwhelming the available infrastructures both hardware and software. Moreover, this led to creating further complexities when considering the software engineering aspects for big data applications development. Introducing cloud computing into the mix further complicates the issues. Most of the current efforts in big data analytics target finding ways to store, organize and process big data effectively in addition to investigating cloud-based big data applications perspectives. However, we noticed there is not much emphasis on defining or enhancing the software development process for developing such applications. Like any software system, it is important to identify the types of applications, requirements and constraints and use this knowledge in a well-defined process model to design and develop effective cloud-based and traditional big data analytics applications. In this paper, we investigate these applications and attempt to identify the general requirements and constraints to better support the software development process. One of the important aspects is being able to distinguish real-time from delay-tolerant big data analytics applications. When the requirements and time constraints are identified, we can decide on the type of infrastructure and software architectures that will best match these requirements. As a result, we design and deliver effective and useful big data analytics applications.
KW - Big data
KW - Cloud computing
KW - Data analytics
KW - Software engineering
KW - Software process
KW - Time constraints
UR - http://www.scopus.com/inward/record.url?scp=85013168481&partnerID=8YFLogxK
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U2 - 10.1109/CIC.2016.062
DO - 10.1109/CIC.2016.062
M3 - Conference contribution
AN - SCOPUS:85013168481
T3 - Proceedings - 2016 IEEE 2nd International Conference on Collaboration and Internet Computing, IEEE CIC 2016
SP - 426
EP - 432
BT - Proceedings - 2016 IEEE 2nd International Conference on Collaboration and Internet Computing, IEEE CIC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Collaboration and Internet Computing, IEEE CIC 2016
Y2 - 1 November 2016 through 3 November 2016
ER -