Periodicity detection algorithm and applications on IoT data

Tolas, Ramona; Portase, Raluca; Iosif, Andrei; Potolea, Rodica

doi:10.1109/ispdc52870.2021.9521605

Cited by 4 publications

(2 citation statements)

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“…The missing rate is computed as the number of entries that do not have a value for a certain attribute divided by the size of the entire analyzed dataset. The periodicity of the data transmission model can also be used for detecting missing values, as shown in [37]. Outlier identification is also an intensively studied topic in the literature [38][39][40][41].…”

Section: Noise Descriptorsmentioning

confidence: 99%

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GeMSyD: Generic Framework for Synthetic Data Generation

Tolas,

Portase,

Potolea

2024

Data

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In the era of data-driven technologies, the need for diverse and high-quality datasets for training and testing machine learning models has become increasingly critical. In this article, we present a versatile methodology, the Generic Methodology for Constructing Synthetic Data Generation (GeMSyD), which addresses the challenge of synthetic data creation in the context of smart devices. GeMSyD provides a framework that enables the generation of synthetic datasets, aligning them closely with real-world data. To demonstrate the utility of GeMSyD, we instantiate the methodology by constructing a synthetic data generation framework tailored to the domain of event-based data modeling, specifically focusing on user interactions with smart devices. Our framework leverages GeMSyD to create synthetic datasets that faithfully emulate the dynamics of human–device interactions, including the temporal dependencies. Furthermore, we showcase how the synthetic data generated using our framework can serve as a valuable resource for machine learning practitioners. By employing these synthetic datasets, we perform a series of experiments to evaluate the performance of a neural-network-based prediction model in the domain of smart device interaction. Our results underscore the potential of synthetic data in facilitating model development and benchmarking.

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Section: Noise Descriptorsmentioning

confidence: 99%

“…For an event that is periodically transmitted, the missing values are easy to compute, as shown in [37], where the transmission patterns are studied. In the case of a signal that has no periodicity, but is event-based (the events generated as a result of user interaction), the missing values are quite hard to quantify.…”

Section: Noise Descriptorsmentioning

confidence: 99%