research-article

Spatio-Temporal-Social Multi-Feature-based Fine-Grained Hot Spots Prediction for Content Delivery Services in 5G Era

Authors:

Shaoyuan Huang,

Victor C. M. LeungAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 750 - 759

https://doi.org/10.1145/3459637.3482298

Published: 30 October 2021 Publication History

Abstract

The arrival of 5G networks has extensively promoted the growth of content delivery services (CDSs). Understanding and predicting the spatio-temporal distribution of CDSs are beneficial to mobile users, Internet Content Providers and carriers. Conventional methods for predicting the spatio-temporal distribution of CDSs are mostly base-stations (BSs) centric, leading to weak generalization and spatio coarse-grained. To improve the spatio accuracy and generalization of modeling, we propose user-centric methods for CDSs spatio-temporal analysis. With geocoding and spatio-temporal graphs modeling algorithms, CDSs records collected from mobile devices are modeled as dynamic graphs with spatio-temporal attributes. Moreover, we propose a spatio-temporal-social multi-feature extraction framework for spatio fine-grained CDSs hot spots prediction. Specifically, an edge-enhanced graph convolutional block is designed to encode CDSs information based on the social relations and the spatio dependence features. Besides, we introduce the Long Short Term Memory (LSTM) to further capture the temporal dependence. Experiments on two real-world CDSs datasets verified the effectiveness of the proposed framework, and ablation studies are taken to evaluate the importance of each feature.

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Huang SWang ZZhang ZZhang HWang XWang W(2024)Seer: Proactive Revenue-Aware Scheduling for Live Streaming Services in Crowdsourced Cloud-Edge PlatformsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621424(1801-1810)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621424
Zhang HHuang SWang XLi JWang XLeung V(2023)A Measurement-Driven Analysis and Prediction of Content Propagation in the Device-to-Device Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.321939935:8(7651-7664)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3219399

Index Terms

Spatio-Temporal-Social Multi-Feature-based Fine-Grained Hot Spots Prediction for Content Delivery Services in 5G Era
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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National Science Foundation of China
Australian Research Council Linkage Project
National Key Research and Development Program of China

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Huang SWang ZZhang ZZhang HWang XWang W(2024)Seer: Proactive Revenue-Aware Scheduling for Live Streaming Services in Crowdsourced Cloud-Edge PlatformsIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621424(1801-1810)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621424
Zhang HHuang SWang XLi JWang XLeung V(2023)A Measurement-Driven Analysis and Prediction of Content Propagation in the Device-to-Device Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.321939935:8(7651-7664)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3219399

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