Sampling and Evaluating the Big Data for Knowledge Discovery

Andrew H. Sung, Bernardete Ribeiro, Qingzhong Liu

Abstract

The era of Internet of Things and big data has seen individuals, businesses, and organizations increasingly rely on data for routine operations, decision making, intelligence gathering, and knowledge discovery. As the big data is being generated by all sorts of sources at accelerated velocity, in increasing volumes, and with unprecedented variety, it is also increasingly being traded as commodity in the new “data economy” for utilization. With regard to data analytics for knowledge discovery, this leads to the question, among various others, of how much data is really necessary and/or sufficient for getting the analytic results that will reasonably satisfy the requirements of an application. In this work-in-progress paper, we address the sampling problem in big data analytics and propose that (1) the problem of sampling the big data for analytics is “hard”specifically, it is a theoretically intractable problem when formal measures are incorporated into performance evaluation; therefore, (2) heuristic, rather than algorithmic, methods are necessarily needed in data sampling, and a plausible heuristic method is proposed (3) a measure of dataset quality is proposed to facilitate the evaluation of the worthiness of datasets with respect to model building and knowledge discovery in big data analytics.

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Paper Citation


in Harvard Style

Sung A., Ribeiro B. and Liu Q. (2016). Sampling and Evaluating the Big Data for Knowledge Discovery . In Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD, ISBN 978-989-758-183-0, pages 378-382. DOI: 10.5220/0005932703780382


in Bibtex Style

@conference{iotbd16,
author={Andrew H. Sung and Bernardete Ribeiro and Qingzhong Liu},
title={Sampling and Evaluating the Big Data for Knowledge Discovery},
booktitle={Proceedings of the International Conference on Internet of Things and Big Data - Volume 1: IoTBD,},
year={2016},
pages={378-382},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005932703780382},
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 - Sampling and Evaluating the Big Data for Knowledge Discovery
SN - 978-989-758-183-0
AU - Sung A.
AU - Ribeiro B.
AU - Liu Q.
PY - 2016
SP - 378
EP - 382
DO - 10.5220/0005932703780382