U<sup>2</sup>-Tree: A Universal Two-Layer Distributed Indexing Scheme for Cloud Storage System

Gao, Xiaofeng; Gao, Y.; Zhu, Yichen; Chen, Guihai

doi:10.1109/tnet.2019.2891008

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Existing approaches for indexing IoT data, whether in multidimensional [6,[24][25][26][27] or metric space [28][29][30][31][32], do not provide efficient mechanisms for IoT data storage, despite their dynamic natures and continuous growth. There have been few publications in the field of continuous IoT data stream indexing.…”

Section: Related Workmentioning

confidence: 99%

“…These characteristics involve some issues, such as data overlapping and cloud computing latency, that make data searching and storage more difficult. To address these issues, many indexing methods for data storage have been proposed [4][5][6]. However, not all of the indexing methods are appropriate for the everchanging IoT environment, due to the endless flow of heterogeneous IoT data.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level

Khettabi,

Kouahla,

Farou

et al. 2023

BDCC

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Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In this work, we propose an efficient method, in the fog-cloud computing architecture, to index continuous and heterogeneous data streams in metric space. This method divides the fog layer into three levels: clustering, clusters processing and indexing. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to group the data from each stream into homogeneous clusters at the clustering fog level. Each cluster in the first data stream is stored in the clusters processing fog level and indexed directly in the indexing fog level in a Binary tree with Hyperplane (BH tree). The indexing of clusters in the subsequent data stream is determined by the coefficient of variation (CV) value of the union of the new cluster with the existing clusters in the cluster processing fog layer. An analysis and comparison of our experimental results with other results in the literature demonstrated the effectiveness of the CV method in reducing energy consumption during BH tree construction, as well as reducing the search time and energy consumption during a k Nearest Neighbor (kNN) parallel query search.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level

Khettabi,

Kouahla,

Farou

et al. 2023

BDCC

View full text Add to dashboard Cite

show abstract

MetisRL: A Reinforcement Learning Approach for Dynamic Routing in Data Center Networks

Gao

Chen

2022

Database Systems for Advanced Applications

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An efficient and scalable multi-dimensional indexing scheme for modular data centers

Gao

Zhu

et al. 2019

Data & Knowledge Engineering

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U²-Tree: A Universal Two-Layer Distributed Indexing Scheme for Cloud Storage System

Cited by 5 publications

References 29 publications

Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level

Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level

MetisRL: A Reinforcement Learning Approach for Dynamic Routing in Data Center Networks

An efficient and scalable multi-dimensional indexing scheme for modular data centers

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U2-Tree: A Universal Two-Layer Distributed Indexing Scheme for Cloud Storage System

Cited by 5 publications

References 29 publications

Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level

Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level

MetisRL: A Reinforcement Learning Approach for Dynamic Routing in Data Center Networks

An efficient and scalable multi-dimensional indexing scheme for modular data centers

Contact Info

Product

Resources

About

U²-Tree: A Universal Two-Layer Distributed Indexing Scheme for Cloud Storage System