Savva, F. (2019) Query-Driven Learning for Next Generation Predictive Modeling & Analytics. In: SIGMOD SRC 2019, Amsterdam, The Netherlands, 30 Jun - 05 Jul 2019, pp. 1844-1846. ISBN 9781450356435 (doi: 10.1145/3299869.3300101)
|
Text
177847.pdf - Accepted Version 654kB |
Abstract
As data-size is increasing exponentially, new paradigm shifts have to emerge allowing fast exploitation of data by every- body. Large-scale predictive analytics is restricted to wealthy organizations as small-scale enterprises (SMEs) struggle to compete and are inundated by the sheer monetary cost of either procuring data infrastructures or analyzing datasets over the Cloud. The aim of this work is to study mechanisms which can democratize analytics, in the sense of making them affordable, while at the same time ensuring high efficiency, scalability, and accuracy. The crux of this proposal lies in developing query-driven solutions that can be used off the Cloud thus minimizing costs. Our query-driven approach will learn and adapt on-the-fly machine learning models, based solely on query-answer interactions, which can be used for answering analytical queries. In this abstract we describe the methodology followed for the implementation and evaluation of the system designed.
| Item Type: | Conference Proceedings |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Savva, Mr Fotis |
| Authors: | Savva, F. |
| College/School: | College of Science and Engineering > School of Computing Science |
| ISBN: | 9781450356435 |
| Copyright Holders: | Copyright © 2019 The Author |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
| Related URLs: |
University Staff: Request a correction | Enlighten Editors: Update this record
Altmetric
Altmetric