A Trajectory Big Data Storage Model Incorporating Partitioning and Spatio-Temporal Multidimensional Hierarchical Organization

Yao, Zhixin; Zhang, Jianqin; Li, Taizeng; Ding, Ying

doi:10.3390/ijgi11120621

Cited by 4 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, this paper conducted retrieval speed tests on the improved and spacetime-coded storage models [19]. The experimental results are shown in Figure 7.…”

Section: Comparative Analysis Of Retrieval Speedmentioning

confidence: 99%

See 1 more Smart Citation

A Clustering Visualization Method for Density Partitioning of Trajectory Big Data Based on Multi-Level Time Encoding

Wei,

Zhang,

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

The proliferation of the Internet and the widespread adoption of mobile devices have given rise to an immense volume of real-time trajectory big data. However, a single computer and conventional databases with limited scalability struggle to manage this data effectively. During the process of visual rendering, issues such as page stuttering and subpar visual outcomes often arise. This paper, founded on a distributed architecture, introduces a multi-level time encoding method using “minutes”, “hours”, and “days” as fundamental units, achieving a storage model for trajectory data at multi-scale time. Furthermore, building upon an improved DBSCAN clustering algorithm and integrating it with the K-means clustering algorithm, a novel density-based partitioning clustering algorithm has been introduced, which incorporates road coefficients to circumvent architectural obstacles, successfully resolving page stuttering issues and significantly enhancing the quality of visualization. The results indicate the following: (1) when data is extracted using the units of “minutes”, “hours”, and “days”, the retrieval efficiency of this model is 6.206 times, 12.475 times, and 18.634 times higher, respectively, compared to the retrieval efficiency of the original storage model. As the volume of retrieved data increases, the retrieval efficiency of the proposed storage model becomes increasingly superior to that of the original storage model. Under identical experimental conditions, this model’s retrieval efficiency also outperforms the space–time-coded storage model; (2) Under a consistent rendering level, the clustered trajectory data, when compared to the unclustered raw data, has shown a 40% improvement in the loading speed of generating heat maps. There is an absence of page stuttering. Furthermore, the heat kernel phenomenon in the heat map was also resolved while enhancing the visualization rendering speed.

show abstract

“…Similarly, this paper conducted retrieval speed tests on the improved and spacetime-coded storage models [19]. The experimental results are shown in Figure 7.…”

Section: Comparative Analysis Of Retrieval Speedmentioning

confidence: 99%

“…Therefore, time encoding is more direct and efficient in such a usage scenario. Similarly, this paper conducted retrieval speed tests on the improved and spacetime-coded storage models [19]. The experimental results are shown in Figure 7.…”

Section: Comparative Analysis Of Retrieval Speedmentioning

confidence: 99%

A Clustering Visualization Method for Density Partitioning of Trajectory Big Data Based on Multi-Level Time Encoding

Wei,

Zhang,

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hybrid Grid Partitioning, Grid partitioning techniques have been utilized to divide the dataset into grid cells, providing a structured representation of the data space (Zheng et al, 2006) (Frey, 2022) (Yao et al, 2022). Previous research has explored hybrid grid partitioning methods that combine the advantages of grid-based partitioning and fuzzy partitioning (Cai et al, 2022) (Ezugwu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory

Ferguson

2023

emod

View full text Add to dashboard Cite

This research presents an integrated approach for fuzzy rule generation in dataset classification by combining hybrid grid partitioning and rough set theory. The objective is to enhance the accuracy and interpretability of classification models. The approach leverages hybrid grid partitioning to achieve localized rule generation, capturing the local characteristics and patterns within different regions of the feature space. Furthermore, rough set theory is applied for attribute reduction, identifying the most relevant features and reducing the complexity of the classification problem. The generated fuzzy rules provide interpretable and understandable classification rules that facilitate domain expert interpretation. The research contributes to the field by proposing a comprehensive framework that improves both accuracy and interpretability of dataset classification. The findings demonstrate the effectiveness of the integrated approach, although certain limitations exist. Future research should focus on parameter selection, scalability challenges, and the applicability of the approach to diverse problem domains. The integrated approach presents a promising methodology for enhancing the accuracy and interpretability of dataset classification, with potential applications in various domains where accurate and interpretable classification models are crucial.

show abstract

Big Data Architecture for Air Pollution Spatial Visualization: Quito, Ecuador

Mora-Villacís,

Calle-Jimenez

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

A Trajectory Big Data Storage Model Incorporating Partitioning and Spatio-Temporal Multidimensional Hierarchical Organization

Cited by 4 publications

References 22 publications

A Clustering Visualization Method for Density Partitioning of Trajectory Big Data Based on Multi-Level Time Encoding

A Clustering Visualization Method for Density Partitioning of Trajectory Big Data Based on Multi-Level Time Encoding

An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory

Big Data Architecture for Air Pollution Spatial Visualization: Quito, Ecuador

Contact Info

Product

Resources

About