Metrics for characterizing machine learning-based hotspot detection methods

Wuu, Jen-Yi; Pikus, Fedor G.; Marek-Sadowska, Malgorzata

doi:10.1109/isqed.2011.5770713

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…There are various performance metrics available in the literature to access the performance of the hotspot detection method. J-Y Wuu, et al [41] define prediction accuracy and memorizing accuracy as performance metrics. M. B. Ulak et al [42] define the crash prediction accuracy index as the performance metrics for evaluating the hotspot detection methods.…”

Section: Evolution Metricsmentioning

confidence: 99%

Gender-Specific Hotspot Detection of Literate and Workers in Uttar Pradesh, India using a Rough Graph-based Approach

Tabarej,

Minz

2023

Preprint

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Spatial polygon data represents the area or region of specific events, such as disease cases, crime, medical facilities, earthquakes, and fires. In spatial data analysis, locating the hotspot is essential. However, it is challenging to identify a spatially significant hotspot. This paper proposes a novel method for finding statistically significant hotspots based on the rough graph. First, the Global Moan index is used to determine the presence of spatial dependence in the data set. Then, the HSDRG algorithm is implemented to find the hotspot of the polygon vector data. Two spatial neighbour search techniques, BFS and DFS, are employed to find the spatial neighbour. The algorithm is evaluated using socio-economic data from Uttar Pradesh, India. Four variables were chosen to find the hotspot: female literacy, male literacy, female workers, and male workers. A percentage value is calculated for each variable to find the hotspot. The analysis reveals that the generated hotspots are denser, the PAI value is high, and the running time is less than the other methods found in the literature. The running time of the HSDRH algorithm using DFS as the search technique is 69.48%, 72.91%, and 73.08% less compared to the methods Moran’s I, Getis Ord Gi, and Getis Ord Gi*, respectively. Therefore, the HDSRG algorithm using a rough graph is considered the optimal method for hotspot detection. This type of analysis is vital to know whether the area has good literacy concerning males and females and to know the area has hotspot workers.

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Section: Evolution Metricsmentioning

confidence: 99%

Gender-Specific Hotspot Detection of Literate and Workers in Uttar Pradesh, India using a Rough Graph-based Approach

Tabarej,

Minz

2023

Preprint

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“…The ANN uses online statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot(s), allowing enough time for the multicore system to react to these potential hotspots. Wuu et al [39] proposed two metrics -the predictive and memorizing accuracy rates -for characterizing the accuracy of machine learning-based hotspot detection methods. Duo et al [14] proposed a high-performance lithographic hotspot detection flow with ultra-fast speed and high fidelity.…”

Section: Related Workmentioning

confidence: 99%

A study of hotspot data prediction model in I/O workloads

Yang

Tan

Xie

et al. 2013

International Journal of Computer Mathematics

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There is a notable characteristic of the data access pattern: 80% I/O requests only access 20% data. This feature brings about the concept of hotspot data, which refer to the data in the most frequent requested area. The access to these hotspot data has direct influence upon the performance of the storage system's applications. Therefore, how to predict hotspot data is a critical research focus in the optimization of the storage system. In this paper, we propose a hotspot data prediction model based on a Zipf-like distribution, which can estimate and dynamically adjust parameters according to the present statistics of I/O access. We classify the hotspot data from every trace, and analyse the prediction rate through the classified hotspot data's characteristic. We synthesize the analysis results in different time granularities and hotspot data prediction queue lengths. Finally, we use block I/O traces to discuss the effectiveness of this model. The discussion and analysis results indicate that this model can predict the hotspot data efficiently.

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Rough-graph-based hotspot detection of polygon vector data

Tabarej

Minz

2023

Multimed Tools Appl

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Metrics for characterizing machine learning-based hotspot detection methods

Cited by 5 publications

References 9 publications

Gender-Specific Hotspot Detection of Literate and Workers in Uttar Pradesh, India using a Rough Graph-based Approach

Gender-Specific Hotspot Detection of Literate and Workers in Uttar Pradesh, India using a Rough Graph-based Approach

A study of hotspot data prediction model in I/O workloads

Rough-graph-based hotspot detection of polygon vector data

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