Feature Driven Survey of Big Data Systems

Cigdem Avci Salma, Bedir Tekinerdogan, Ioannis N. Athanasiadis

Abstract

Big Data has become a very important driver for innovation and growth for various industries such as health, administration, agriculture, defence, and education. Storing and analysing large amounts of data are becoming increasingly common in many of these application areas. In general, different application domains might require different type of big data systems. Although, lot has been written on big data it is not easy to identify the required features for developing big data systems that meets the application requirements and the stakeholder concerns. In this paper we provide a survey of big data systems based on feature modelling which is a technique that is utilized for defining the common and variable features of a domain. The feature model has been derived following an extensive literature study on big data systems. We present the feature model and discuss the features to support the understanding of big data systems.

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Paper Citation


in Harvard Style

Salma C., Tekinerdogan B. and Athanasiadis I. (2016). Feature Driven Survey of Big Data Systems . In Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD, ISBN 978-989-758-183-0, pages 348-355. DOI: 10.5220/0005877503480355


in Bibtex Style

@conference{iotbd16,
author={Cigdem Avci Salma and Bedir Tekinerdogan and Ioannis N. Athanasiadis},
title={Feature Driven Survey of Big Data Systems},
booktitle={Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,},
year={2016},
pages={348-355},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005877503480355},
isbn={978-989-758-183-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,
TI - Feature Driven Survey of Big Data Systems
SN - 978-989-758-183-0
AU - Salma C.
AU - Tekinerdogan B.
AU - Athanasiadis I.
PY - 2016
SP - 348
EP - 355
DO - 10.5220/0005877503480355