Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)
<|reference_start|>Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD): This paper introduces the Pareto Data Framework, an approach for identifying and selecting the Minimum Viable Data (MVD) required for enabling machine learning applications on constrained platforms such as embedded systems, mobile devices, and Internet of Things (IoT) devices. We demonstrate that strategic data reduction can maintain high performance while significantly reducing bandwidth, energy, computation, and storage costs. The framework identifies Minimum Viable Data (MVD) to optimize efficiency across resource-constrained environments without sacrificing performance. It addresses common inefficient practices in an IoT application such as overprovisioning of sensors and overprecision, and oversampling of signals, proposing scalable solutions for optimal sensor selection, signal extraction and transmission, and data representation. An experimental methodology demonstrates effective acoustic data characterization after downsampling, quantization, and truncation to simulate reduced-fidelity sensors and network and storage constraints; results shows that performance can be maintained up to 95\% with sample rates reduced by 75\% and bit depths and clip length reduced by 50\% which translates into substantial cost and resource reduction. These findings have implications on the design and development of constrained systems. The paper also discusses broader implications of the framework, including the potential to democratize advanced AI technologies across IoT applications and sectors such as agriculture, transportation, and manufacturing to improve access and multiply the benefits of data-driven insights.<|reference_end|>
@article{ahmed2024pareto,
    title={Pareto Data Framework: Steps Towards Resource-Efficient Decision Making
  Using Minimum Viable Data (MVD)},
    author={Tashfain Ahmed and Josh Siegel},
    journal={arXiv preprint arXiv:2409.12112},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12112},
    primaryClass={cs.LG cs.AI cs.SD eess.AS}
}