Advancements in Household Data Mining: Fine-Tuning of Usage Pattern Inference Pipeline

Ramona Tolas, Raluca Portase, Rodica Potolea

2024

Abstract

In the era of rapidly expanding smart household devices, a surge in data generation within domestic environments has occurred. This paper focuses on optimizing knowledge inference methods from this rich household-generated data, building upon our earlier work for uncovering intricate usage patterns. This work addresses non-functional requirements, emphasizing data processing efficiency by introducing innovative techniques for dimensionality reduction. Another contribution of this research is the formalization of a synthetic data generation process, crucial for comprehensive testing and data privacy compliance. Overall, this work advances household data mining by refining usage pattern inference pipeline, enhancing performance, and providing a framework for synthetic data generation.

Paper Citation

in Harvard Style

Tolas R., Portase R. and Potolea R. (2024). Advancements in Household Data Mining: Fine-Tuning of Usage Pattern Inference Pipeline. In Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-699-6, SciTePress, pages 53-61. DOI: 10.5220/0012598000003705

in Bibtex Style

@conference{iotbds24,
author={Ramona Tolas and Raluca Portase and Rodica Potolea},
title={Advancements in Household Data Mining: Fine-Tuning of Usage Pattern Inference Pipeline},
booktitle={Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2024},
pages={53-61},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012598000003705},
isbn={978-989-758-699-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Advancements in Household Data Mining: Fine-Tuning of Usage Pattern Inference Pipeline
SN - 978-989-758-699-6
AU - Tolas R.
AU - Portase R.
AU - Potolea R.
PY - 2024
SP - 53
EP - 61
DO - 10.5220/0012598000003705
PB - SciTePress