Data Mining in Internet of Things Systems: A Literature Review

Sarhan, Amany

doi:10.21608/erjeng.2023.186378.1143

Cited by 4 publications

(1 citation statement)

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“…An in-depth analysis of the current state of outlier analysis and data mining for IoT platforms in computer engineering involves examining recent developments in the field and exploring real-world applications that have been developed using data mining techniques. It also highlights the challenges associated with collecting, storing, and managing large datasets in the IoT environment, and discusses the various techniques that have been developed to overcome these challenges [13]. A metaanalysis of the anomaly detection problem has been provided, approaches to benchmarking anomaly detection algorithms that vary in their construction across several dimensions have been identified, the effects of experimental design on experimental results have been observed and results are evaluated.…”

Section: A Tutorial and Surveymentioning

confidence: 99%

TTDD: a time series Tester, Transformer, and Decomposer framework for outlier Detection

Khattab

2023

Journal of Engineering Research

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Anomaly detection in time series has become an important aspect of data analysis and has many applications. Anomaly detection is often a challenge for statistical and pattern detection modeling. We introduce TTDD (Test, Transform, Decompose, and Detection), a pattern-based detector to detect outlier values in time series datasets. TTDD splits each time series into three components, each representing an underlying pattern category. Trend, seasonality, and residual. The outlier is determined for each component separately, which contributes to determining the outlier with high accuracy. The lag in the timing between the test set and the training set was discovered using Fourier transforms. In contrast to previous work, we support time series that are phase shifted. TTDD is resilient to consecutively outlier values with different types. The experimental results are based on real-world and benchmark datasets. The proposed TTDD framework outperforms the other methods with higher accuracy score at 95% which is higher than those of other methods about 15%. Furthermore, it can detect the delay time outliers that other methods cannot easily identify.

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Section: A Tutorial and Surveymentioning

confidence: 99%