# The Metaverse Data Deluge: What Can We Do About It?

arXiv: https://arxiv.org/abs/2206.10326 · HTML: https://mlsanity.com/paper/2206.10326/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 104 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 30 of 30)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [Egocentric Video-Language Pretraining](https://mlsanity.com/paper/2206.01670/) (2022)
- [Feature Inference Attack on Model Predictions in Vertical Federated Learning](https://mlsanity.com/paper/2010.10152/) (2020)
- [DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2205.15723/) (2022)
- [Privacy Preserving Vertical Federated Learning for Tree-based Models](https://mlsanity.com/paper/2008.06170/) (2020)
- [Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider](https://mlsanity.com/paper/2003.03423/) (2020)
- [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022)
- [AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant](https://mlsanity.com/paper/2203.04203/) (2022)
- [Database Meets Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/1906.08986/) (2019)
- [Artificial Intelligence for the Metaverse: A Survey](https://mlsanity.com/paper/2202.10336/) (2022)
- [ARM-Net: Adaptive Relation Modeling Network for Structured Data](https://mlsanity.com/paper/2107.01830/) (2021)
- [Label-Efficient Online Continual Object Detection in Streaming Video](https://mlsanity.com/paper/2206.00309/) (2022)
- [Serverless Computing: One Step Forward, Two Steps Back](https://mlsanity.com/paper/1812.03651/) (2018)
- [Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints](https://mlsanity.com/paper/1904.01831/) (2019)
- [A Survey on Deep Reinforcement Learning for Data Processing and Analytics](https://mlsanity.com/paper/2108.04526/) (2021)
- [Communication-efficient Decentralized Machine Learning over Heterogeneous Networks](https://mlsanity.com/paper/2009.05766/) (2020)
- [AssistSR: Task-oriented Video Segment Retrieval for Personal AI Assistant](https://mlsanity.com/paper/2111.15050/) (2021)
- [The Disruptions of 5G on Data-driven Technologies and Applications](https://mlsanity.com/paper/1909.08096/) (2019)
- [MetaSlicing: A Novel Resource Allocation Framework for Metaverse](https://mlsanity.com/paper/2205.11087/) (2022)
- [Novel View Synthesis for High-fidelity Headshot Scenes](https://mlsanity.com/paper/2205.15595/) (2022)
- [Serverless Data Science -- Are We There Yet? A Case Study of Model Serving](https://mlsanity.com/paper/2103.02958/) (2021)
- [BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures](https://mlsanity.com/paper/1904.03604/) (2019)
- [Modular Politics: Toward a Governance Layer for Online Communities](https://mlsanity.com/paper/2005.13701/) (2020)
- [Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval](https://mlsanity.com/paper/2203.07720/) (2022)
- [Towards Polyglot Data Stores -- Overview and Open Research Questions](https://mlsanity.com/paper/2204.05779/) (2022)
- [High-Speed Query Processing over High-Speed Networks](https://mlsanity.com/paper/1502.07169/) (2015)

## 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.