Managing Metaverse Data Tsunami: Actionable Insights

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-01-16 DOI:10.1109/TKDE.2024.3354960
Bingxue Zhang;Gang Chen;Beng Chin Ooi;Mike Zheng Shou;Kian-Lee Tan;Anthony K. H. Tung;Xiaokui Xiao;James Wei Luen Yip;Meihui Zhang
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Abstract

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, and efficient data storage, 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. Then, we further discuss several emerging technologies that empower the construction of metaverse. After that, we discuss comprehensively the data centric challenges. Finally, we discuss and envision what are likely to be required from the database and system perspectives.
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管理元数据海啸:可行的见解
在元宇宙中,物理空间和虚拟空间共存,同时互动。物理空间的信息在虚拟空间得到增强,而虚拟空间则不断刷新实时的现实世界信息。为了让用户能够在现实空间和数字空间之间无缝地处理和操作信息,必须开发新的技术。这些技术包括智能界面、新的增强现实技术以及高效的数据存储、管理和传播技术。在本文中,我们将首先讨论一些有前景的共空间应用。这些应用提供了任何一个空间都无法单独实现的机会。然后,我们进一步讨论了几项新兴技术,这些技术为构建元宇宙提供了能力。之后,我们将全面讨论以数据为中心的挑战。最后,我们从数据库和系统的角度讨论并展望了可能需要解决的问题。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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