Version 1
: Received: 16 June 2023 / Approved: 19 June 2023 / Online: 19 June 2023 (16:31:37 CEST)
How to cite:
Sulayman, I.I.M.A.; Voege, P.; Ouda, A. Algorithm-based Data Generation (ADG) Engine for Data Analytics. Preprints2023, 2023061378. https://doi.org/10.20944/preprints202306.1378.v1
Sulayman, I.I.M.A.; Voege, P.; Ouda, A. Algorithm-based Data Generation (ADG) Engine for Data Analytics. Preprints 2023, 2023061378. https://doi.org/10.20944/preprints202306.1378.v1
Sulayman, I.I.M.A.; Voege, P.; Ouda, A. Algorithm-based Data Generation (ADG) Engine for Data Analytics. Preprints2023, 2023061378. https://doi.org/10.20944/preprints202306.1378.v1
APA Style
Sulayman, I.I.M.A., Voege, P., & Ouda, A. (2023). Algorithm-based Data Generation (ADG) Engine for Data Analytics. Preprints. https://doi.org/10.20944/preprints202306.1378.v1
Chicago/Turabian Style
Sulayman, I.I.M.A., Peter Voege and Abdelkader Ouda. 2023 "Algorithm-based Data Generation (ADG) Engine for Data Analytics" Preprints. https://doi.org/10.20944/preprints202306.1378.v1
Abstract
The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.
Keywords
Data Generation; Anomaly Data; User Behavior Generation; Big Data
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
