The Metaverse Data Deluge: What Can We Do About It?
Not on the map. Why?
Abstract (arXiv)
In the Metaverse, the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, efficient storage and data management and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer opportunities that neither of the spaces can realize on its own. We then discuss challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 104 references that are in the map. First posted on arXiv 14 Jun 2022.
Builds on
30 of 104 references.
References · 30
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
- 2019Advances and Open Problems in Federated Learningcited by 3,021
- 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
- 2022Block-NeRF: Scalable Large Scene Neural View Synthesiscited by 720
- 2022Egocentric Video-Language Pretrainingcited by 231
- 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 105
- 2022DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenescited by 103
- 2020Privacy Preserving Vertical Federated Learning for Tree-based Modelscited by 89
- 2020Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Providercited by 65
- 2022A Survey on Metaverse: Fundamentals, Security, and Privacycited by 51
- 2022AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistantcited by 32
18 more
- 2019Database Meets Deep Learning: Challenges and Opportunitiescited by 30
- 2022Artificial Intelligence for the Metaverse: A Surveycited by 25
- 2021ARM-Net: Adaptive Relation Modeling Network for Structured Datacited by 24
- 2022Label-Efficient Online Continual Object Detection in Streaming Videocited by 16
- 2018Serverless Computing: One Step Forward, Two Steps Backcited by 12
- 2019Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraintscited by 12
- 2021A Survey on Deep Reinforcement Learning for Data Processing and Analyticscited by 10
- 2020Communication-efficient Decentralized Machine Learning over Heterogeneous Networkscited by 9
- 2021AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistantcited by 8
- 2019The Disruptions of 5G on Data-driven Technologies and Applicationscited by 6
- 2022MetaSlicing: A Novel Resource Allocation Framework for Metaversecited by 5
- 2022Novel View Synthesis for High-fidelity Headshot Scenescited by 4
- 2021Serverless Data Science -- Are We There Yet? A Case Study of Model Servingcited by 3
- 2019BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architecturescited by 2
- 2020Modular Politics: Toward a Governance Layer for Online Communitiescited by 2
- 2022Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrievalcited by 2
- 2022Towards Polyglot Data Stores -- Overview and Open Research Questionscited by 2
- 2015High-Speed Query Processing over High-Speed Networkscited by 1
Built on by
Not cited yet.