作者： admin

2025年5月19日，香港——亚洲领先的AI驱动动画与内容平台GIBO Holdings Ltd.（纳斯达克代码：GIBO）今日宣布其创作者生态系统的最新升级，推出了GIBO Click——一款旨在激活和变现智能资产的模块化引擎。凭借超过7200万注册用户，GIBO正逐步打通全球社区，将实体人偶与数字创作转化为AI驱动的虚拟人物（Avatar），开启人机共创的新时代。

此次升级的核心，是新推出的GIBO ai-Avatar功能，它集成于GIBO Click生态中，使用户可以将任何实体人偶转化为数字孪生体——具备个性特征、创造性身份及变现潜能。无论是通过AI生成的角色，还是嵌有NFC芯片的实体收藏品，GIBO Click都提供了连接技术，实现数字化转型、所有权验证和互动追踪。

GIBO公司CEO顾泽尔（Zelt Kueh）表示：“GIBO Click不仅仅是基础设施，更是一台创造引擎。它让用户实现从创作到拥有，从拥有到身份，从身份到价值的跨越。借助AI虚拟人，用户可以共同塑造GIBO宇宙的未来。”

### GIBO ai-Avatar：由实体人偶到活生生的数字角色

通过GIBO Click，用户只需在实体人偶上贴上微型NFC标签，用手机扫描注册，即可将其绑定到平台。一旦绑定成功，GIBO.ai便利用其自主研发的AIGC引擎，生成个性化的AI虚拟形象，详尽设定人物背景、特征，以及稀有度等级。这些AI虚拟人不仅仅是静态的代表，它们是具有生命力的数字实体，可以参加比赛、共创动画，甚至随着时间不断进化升级。一切互动、使用情况和创意表现都记录在其由GIBO Click支撑的数字身份档案中。

### GIBO ai-Avatar与GIBO Click的主要特色

– **NFC绑定与验证**：保证实体人偶与数字孪生的安全绑定，为每个虚拟角色赋予独一无二的身份。
– **AI生成个性化虚拟人**：根据用户需求，生成带有故事背景、外观和能力的风格化虚拟人物，赋予每个角色独特的创意生命。
– **等级提升与成就追踪**：虚拟人可通过在平台上的创作和社区互动，获得经验值（XP），升级成长，激发持续创新。
– **Create-to-Earn™变现模式**：用户可以通过其虚拟人在内容中出现、赢得比赛或授权许可获利，创造经济价值。
– **O2O（线上线下）生态系统**：构建一个完整的线下到线上再到线下的闭环生态，将实体商品、数字内容和实际经济紧密结合。

### 构建下一代角色经济的重要基础

借助GIBO Click和ai-Avatar技术，GIBO正打造一个新兴的角色型知识产权（IP）生态，呈现出以下几个鲜明特征：
– **AI增强**：结合人工智能赋能虚拟人物，提高内容丰富度和互动性。
– **用户拥有**：实体与数字资源皆由用户拥有，增强归属感和控制权。
– **变现能力**：为创作者、收藏者和品牌提供多样盈利渠道。
– **跨媒介互通**：实现不同媒介和平台之间的角色迁移与合作。

这一系列创新，既是GIBO长期发展战略的重要布局，也是赋能全球创作者、粉丝和收藏家的关键途径。未来几年，GIBO计划逐步推行，先在精选社区和早期合作伙伴中开启试用，然后扩展到更广泛的生态体系中。

### 未来展望

GIBO董事长顾泽尔强调：“AI虚拟人是GIBO Click的核心灵魂。每一次扫描都孕育出一个新生命，每一次互动都创造价值。这不仅关乎个人娱乐，更关乎娱乐产业的未来。”借助这些技术与理念，GIBO希望能推动虚拟偶像、数字艺人乃至虚拟角色产业的升级，打造一个以用户为核心、跨界融合的全新数字生态系统。

### 公司简介

GIBO Holdings Ltd.是一家独特且集成的AIGC动画流媒体平台，为大量亚洲年轻用户提供丰富的AI动画内容创作、发布、分享与享受的服务。公司拥有庞大的用户基础及先进的AI技术工具，致力于重新定义数字内容的生产与变现方式。

### 展望未来

GIBO通过推出GIBO Click和ai-Avatar，正在谋划一场角色经济的新革命。这一系统不仅增强虚拟角色的互动与持久性，也为内容创作者、品牌及消费者提供了前所未有的商业机会。公司相信，随着技术的不断成熟与生态的日益完善，未来的数字娱乐和虚拟人物产业将迎来一个充满无限可能的新时代。

### 免责声明

本新闻稿包含“前瞻性陈述”，这些陈述基于公司目前的预期和假设，涉及未来业绩、市场机会及公司战略，存在一定风险与不确定性。公司提醒投资者注意，未来实际表现可能与预期不符，敬请审慎评估。

联系方式：
投资者关系：Bill Zima，ICR公司，邮箱：[联系邮箱]
媒体联系：Edmond Lococo，ICR公司，邮箱：[联系邮箱]

更多信息及最新动态，请访问：[GIBO官方网址]

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香港，2025年5月19日 /PRNewswire/ — 亚洲领先的AI动画与内容平台GIBO Holdings Ltd.（纳斯达克股票代码：GIBO）今日宣布其创作者生态系统迈入下一阶段，推出扩展版GIBO Click——一套旨在转换、验证及变现智能资产的模块化基础设施。目前，GIBO Click已服务全球超过7200万用户，使实体收藏品和数字创作都能蜕变为拥有“真实世界资产（RWA）”表现的AI虚拟形象（Avatar）。

创新的核心在于GIBO ai-Avatar™，这是GIBO Click中的一项突破性功能，能将任何形象—无论是AI生成还是实物收藏—转化为智能的数字孪生（Digital Twin）。这些虚拟形象不仅模仿实体收藏品的外观，还能展示其真实性、使用情况和潜在价值，利用AI技术赋予实体资产动态数字生命。

通过RWA（Real World Asset）上链，每件收藏品都能实现唯一识别、可追溯性，成为沉浸式故事讲述、创作者互动以及数字商务中的核心资产。换句话说，实体藏品在数字与现实的融合中获得了全新的生命力。

GIBO ai-Avatar™的核心功能是：用户只需在实体收藏品上粘贴微型NFC标签，并通过扫描和提交元数据，将收藏品注册到平台，即可实现数字身份的绑定。这一过程将实体藏品转换为代币化的RWA资产，绑定到由GIBO ai-Avatar™生成的独特数字身份上。

每个AI虚拟形象包含三个主要层面：由GIBO自主研发的AIGC（人工智能生成内容）工具打造的风格化数字外观、完整的背景与故事设定（如人物传说、性格特征和稀有等级分类）、以及一个以成长为导向的系统，追踪其在平台上的活跃度、互动记录和创造性使用。这些虚拟形象不是静态文件，而是可编程、能不断演进的实体，反映其“成长”的过程、状态变化和叙事存在感。所有动作、外观和发展轨迹都通过GIBO Click的数字身份档案透明记录，并与其RWA来源相挂钩。

GIBO ai-Avatar+GIBO Click结合的关键特性包括：

– NFC绑定与验证：确保每个实体收藏品能安全链接到独一无二的数字身份，验证其真实的RWA来源
– AI生成虚拟形象：将静态实体转变为具备叙事和平台互动能力的智能角色
– 基于经验值（XP）的成长机制：通过平台上丰富的活动和社交互动，追踪虚拟形象的成长和价值变化
– Create-to-Earn™变现模式：当虚拟形象在内容中被展示、获得授权或进行交易时，奖励创作者及收藏者
– O2O（线上到线下）生态：实现实体资产到数字资产的无缝转换，构建线下、线上和线下的闭环生态系统

通过将RWA协议引入GIBO Click，GIBO.ai不仅让实体藏品数字化，更赋予它们“智慧”和“创造力”。每个AI虚拟形象代表的不仅仅是实体的复制品，还携带资产的身份信息，会随着使用逐渐演变，甚至能带来实际的经济回报。此一框架打通了数字藏品与经济生态之间的界限，树立了以角色IP为核心的内容创造、AI赋能的用户互动，以及 ownership-driven（所有权驱动）的变现新标准。

GIBO正通过赋予创作者与收藏者将实体转化为动态虚拟角色的工具，开启娱乐产业的新篇章：每一件收藏品都能“会思考、会成长、会赚钱”。这不仅推动了数字资产的创新应用，也为实体资产的未来提供了无限可能。

关于GIBO Holdings Limited

GIBO Holdings Ltd. 是一家融合AIGC动画内容和流媒体服务的平台，拥有广泛的功能，服务于亚洲青少年用户，提供创建、发布、分享和欣赏AI生成动画视频的空间。平台用户规模已超过7200万，结合先进的AI工具，GIBO致力于重塑数字内容的创造与体验方式。

前瞻声明

本新闻稿包含“前瞻性陈述”，根据美国1995年私营证券诉讼改革法案中的“安全港”条款，涉及未来业绩、市场预期、公司增长、人才招聘、财务状况等方面的预测。这些陈述基于公司管理层目前的预期和假设，具有不确定性，可能因多重风险而偏离实际表现。公司对未来陈述不承担更新责任，投资者应谨慎对待。

联系方式

投资者关系：比尔·齐玛（Bill Zima），ICR公司，邮箱：[email protected]

媒体关系：埃德蒙·洛科科（Edmond Lococo），ICR公司，邮箱：[email protected]

欲了解更多最新信息，请访问：https://www.globalibo.com/gibo-click/

Source: GIBO Holdings Ltd.

【注：本篇文章旨在全面介绍GIBO Click的技术创新、功能特点及其行业意义，帮助读者理解这一新型数字资产生态的未来潜力。】

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随着智慧城市的发展，数字孪生（Digital Twins, DTs）技术逐渐成为城市管理、交通规划、环境监控等领域的重要工具。数字孪生通过构建城市的虚拟模型，实现对城市空间、基础设施和居民行为的全方位模拟与优化。然而，在实现这些功能的过程中，位置数据的采集和使用带来了巨大的隐私和安全挑战。本文围绕“保护智能城市数字孪生中的位置数据”展开，综述相关技术现状、面临的问题及未来的发展方向。

一、空间数字孪生（SDTs）概述
空间数字孪生（Spatial Digital Twins, SDTs）指的是利用数字孪生技术打造的，集成空间信息的虚拟城市模型。SDTs能够反映城市中的地理空间特征、交通流量、环境参数等，为城市规划和管理提供科学依据。其应用涵盖交通流优化、公共安全监控、环境保护、应急响应等多个方面。构建SDTs的主要组成部分包括数据采集与处理、空间建模与存储、数据分析、以及地图与地理信息系统（GIS）中介层。

二、相关技术与案例
近年来，许多研究和实践已在SDTs领域取得突破。例如，国内外多个城市已开发出基于GIS的数字孪生平台，支持城市级别的空间模拟。典型案例包括波士顿数字孪生项目、苏黎世城市模型等。这些案例中，关键技术涉及三维空间建模、实时数据接入、以及大规模数据处理，为SDT的可靠性和实用性提供技术保障。

三、构建空间数字孪生的核心要素
构建SDT的基础环节主要包括：

1.数据采集与处理：利用传感器、无人机、遥感技术等多模态数据源，采集城市空间相关信息。数据在传输途中需经过清洗和处理，确保其质量和一致性。

2.空间建模、存储和管理：采用空间数据库（如PostGIS、3D CityDB）存储地理信息，支持空间查询与分析。数据模型需支持三维、多尺度、多时态的空间表达。

3.大数据分析系统：应用机器学习、统计分析或仿真模型，挖掘数据中的潜在关系，为城市管理提供决策支持。

4.地图与GIS中介层：利用GIS中间件实现空间数据的可视化和交互，增强用户体验。

5.关键功能组件：包括空间查询、动态监控、模型预测和优化算法，以实现智能调度和风险预警等功能。

四、现代先进技术在SDTs中的应用
智能化、区块链、云计算等现代技术为SDTs赋能。例如，AI与机器学习提升数据分析与预测能力；区块链确保数据的安全、可信和可追溯；云平台提供弹性存储与大规模计算能力。此外，结合边缘计算可以实现实时数据处理，增强系统的响应速度和稳定性。

五、面临的挑战与未来方向
尽管技术日益成熟，SDTs在实际应用中仍面临诸多挑战，包括：

1.多模态、多尺度数据的采集与整合：不同来源数据具有格式、时间、空间尺度的差异，影响模型的准确性。

2.空间查询的自然语言处理（NLP）：实现用户用自然语言进行空间查询，提升平台的使用便捷性。

3.数据库和大数据平台的性能评估：建立标准化的评测体系，优化存储与处理效率。

4.自动生成空间洞察：借助AI实现智能分析，洞悉城市空间中的隐含规律。

5.多模态分析：融合多源、多类型数据，全面理解城市运动与变化。

6.仿真环境构建：虚拟城市仿真平台，支持多场景测试。

7.复杂交互的可视化：通过沉浸式、多维度的可视化手段，展示空间互动。

8.安全和隐私保护：制定多层次的安全策略，解决位置数据可能带来的隐私泄露问题。例如，移动轨迹数据虽支持车流和人流监控，但连续共享可能泄露个体隐私甚至敏感信息（如居住地、健康状况）。为此，研究者提出包括模糊化、k-匿名性、空间变换等技术手段，在保证数据实用性的同时，保护个人隐私。

在安全方面，已有一些技术方案尝试解决这些问题。如利用区块链技术实现数据的不可篡改性，保证数据链的信任度；采用联邦学习等分布式模型，防止数据集中存储带来的风险。未来，研究重点应在于不断识别新兴威胁，设计防护措施，并评估已有方案的适用性。

六、结论
本文系统整理了构建空间数字孪生的关键技术，划分为数据采集、存储、分析与可视化等四层，并指出结合AI、区块链、云计算等现代技术能显著提升SDT的效率和应用价值。此外，强调在数据隐私和安全方面的挑战，呼吁行业和学术界共同努力，制定标准、完善技术方案，从而实现既保障个人隐私，又发挥SDT在智能城市中的巨大潜力。未来，随着技术不断进步，SDTs将在智慧城市的规划、管理和服务中扮演更加重要的角色，为城市的可持续发展提供坚实的数字基础。

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标题：认识AlphaEvolve：Gemini AI设计先进算法

摘要：近日，谷歌推出了一项具有突破性的技术，名为AlphaEvolve。这一由人工智能驱动的算法设计系统，将AI从传统的工具转变为自主创新的“合作者”，能够自主优化和改进基础技术架构。AlphaEvolve结合了谷歌自主研发的Gemini语言模型与演化计算技术，通过模仿生物进化过程，生成和优化新的算法。早期的测试结果显示，这一系统在多个领域表现出色：它显著提升了数据中心的效率，有助于降低运营成本，并且解决了一些长期困扰数学界的问题，提出的解决方案曾让专业人士束手无策。这一创新不仅展示了AI在复杂技术设计中的潜力，还标志着未来算法开发方式的变革，使AI能够自主进行创新，为科技进步带来新的动力。

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标题：提升营销职业生涯的顶尖AI认证

摘要：随着生成式人工智能（GenAI）技术的普及，营销领域正迅速发生变化。这项技术帮助营销人员创建个性化广告，自动化内容，并更好地与客户建立联系。为了跟上步伐，许多营销人员正在获得能证明他们在GenAI领域技能的认证。以下是2025年对营销专业人士而言最好的GenAI认证之一：

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Abstract and 1 Introduction

1.1. Spatial Digital Twins (SDTs)

1.2. Applications

1.3. Different Components of SDTs

1.4. Scope of This Work and Contributions

2. Related Work and 2.1. Digital Twins and Variants

2.2. Spatial Digital Twin Case Studies

3. Building Blocks of Spatial Digital Twins and 3.1. Data Acquisition and Processing

3.2. Data Modeling, Storage and Management

3.3. Big Data Analytics System

3.4. Maps and GIS Based Middleware

3.5. Key Functional Components

4. Other Relevant Modern Technologies and 4.1. AI & ML

4.2. Blockchain

4.3. Cloud Computing

5. Challenges and Future Work, and 5.1. Multi-modal and Multi-resolution Data Acquisition

5.2. NLP for Spatial Queries and 5.3. Benchmarking the Databases and Big Data Platform for SDT

5.4. Automated Spatial Insights and 5.5. Multi-modal Analysis

5.6. Building Simulation Environment

5.7. Visualizing Complex and Diverse Interactions

5.8. Mitigating the Security and Privacy Concerns

6. Conclusion and References

5.8. Mitigating the Security and Privacy Concerns

Researchers have emphasised on the importance of integrating security and privacy solutions in digital twins to ensure the quality of services like providing data-driven decisions [104, 105]. Reliability, trust, transparency, integrity, authenticity, anonymity and selective disclosure are some of the security and privacy aspects that need attention to make DTs a success. The security and privacy challenges that have been identified for DTs in general also apply for SDTs. Besides, the use of location data imposes additional security and privacy concerns for the SDTs as location data may act as an identifier of an individual and may reveal sensitive information [106, 107]. For example, although mobility data of an individual allows an SDT to monitor traffic in indoor and outdoor spaces or contact tracing [108], continuous sharing of locations of an individual will allow others to know the individual’s movement trajectory and infer the places visited by the individual. If a place represents an individual’s office then the individual would be identified, and if a place is a liver clinic then it may reveal the individual’s sensitive health information. Researchers have developed techniques like obfuscation, k-anonymity and space transformation that trad-off between utility and privacy of location data.

There are a few works [109, 110, 111] that proposed countermeasures to prevent security and privacy attacks for DTs. In these works, blockchain technology [109], federated learning models [111], and cryptographic protocols [110] have been shown as techniques to address various security and privacy concerns. Considering that location data may raise new threats, future research should focus on identifying new security and privacy attacks, designing solutions to protect them, and investigating the applicability of existing security and privacy solutions for SDTs.

6. Conclusion

In this paper, a thorough and organized collection of spatial technologies is presented, forming the fundamental components of Spatial Digital Twins (SDTs). The building blocks of SDTs are categorized into four layers, and a comprehensive overview of key spatial technologies is provided for each layer, covering aspects such as data acquisition, processing, storage, and visualization. We have also presented how modern technologies like AI &ML, blockchain, and cloud computing can facilitate more efficient and effective SDTs. It is important to note that there is currently no existing study that specifically focuses on identifying these crucial technologies essential for the development of SDTs. Consequently, researchers and practitioners working in this multidisciplinary domain, particularly those with limited knowledge of geo-spatial advancements, may encounter difficulties in adopting these technologies for SDTs. Therefore, this interdisciplinary work holds immense potential in bridging the gaps among researchers and practitioners in fields such as geo-spatial, urban and transport engineering, and city planning.

References

[1] E. Glaessgen, D. Stargel, The digital twin paradigm for future nasa and us air force vehicles, in: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, 2012, p. 1818.

[2] WGIC, Spatial digital twins: Global status, opportunities and the way forward (2022).

[3] W. Kritzinger, M. Karner, G. Traar, J. Henjes, W. Sihn, Digital twin in manufacturing: A categorical literature review and classification, Ifac-PapersOnline 51 (11) (2018) 1016–1022.

[4] F. Tao, Q. Qi, Make more digital twins, Nature 573 (7775) (2019) 490–491.

[5] G. Shao, M. Helu, Framework for a digital twin in manufacturing: Scope and requirements, Manufacturing Letters 24 (2020) 105–107.

[6] S. A. Rizvi, M. Umair, M. A. Cheema, Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators, Chaos, Solitons & Fractals 151 (2021) 111240.

[7] C. Semeraro, A. Olabi, H. Aljaghoub, A. H. Alami, M. Al Radi, M. Dassisti, M. A. Abdelkareem, Digital twin application in energy storage: Trends and challenges, Journal of Energy Storage 58 (2023) 106347.

[8] G. White, A. Zink, L. Codec´a, S. Clarke, A digital twin smart city for citizen feedback, Cities 110 (2021) 103064.

[9] T. Clemen, N. Ahmady-Moghaddam, U. A. Lenfers, F. Ocker, D. Osterholz, J. Str¨obele, D. Glake, Multi-agent systems and digital twins for smarter cities, in: SIGSIM-PADS ’21: SIGSIM Conference on Principles of Advanced Discrete Simulation, 2021, pp. 45–55.

[10] B. Lei, P. Janssen, J. Stoter, F. Biljecki, Challenges of urban digital twins: A systematic review and a delphi expert survey, Automation in Construction 147 (2023) 104716.

[11] F. Dembski, U. W¨ossner, M. Letzgus, M. Ruddat, C. Yamu, Urban digital twins for smart cities and citizens: The case study of herrenberg, germany, Sustainability 12 (6) (2020) 2307.

[12] G. Mylonas, A. Kalogeras, G. Kalogeras, C. Anagnostopoulos, C. Alexakos, L. Mu˜noz, Digital twins from smart manufacturing to smart cities: A survey, Ieee Access 9 (2021) 143222–143249.

[13] Arcgis, https://www.esri.com/en-us/digital-twin/overview/, accessed: 2023-05-08.

[14] Microsoft azure digital twins platform, https://azure.microsoft.com/en-us/products/digital-twins/, accessed: 2023-05-08.

[15] 3dexperience, https://www.3ds.com/3dexperience, accessed: 2023-05-08.

[16] Z. Wang, R. Gupta, K. Han, H. Wang, A. Ganlath, N. Ammar, P. Tiwari, Mobility digital twin: Concept, architecture, case study, and future challenges, IEEE Internet Things J. 9 (18) (2022) 17452–17467.

[17] Z. Fan, X. Yang, W. Yuan, R. Jiang, Q. Chen, X. Song, R. Shibasaki, Online trajectory prediction for metropolitan scale mobility digital twin, in: Proceedings of the 30th International Conference on Advances in Geographic Information Systems, SIGSPATIAL, 2022, pp. 103:1–103:12.

[18] E. VanDerHorn, S. Mahadevan, Digital twin: Generalization, characterization and implementation, Decis. Support Syst. 145 (2021) 113524. doi:10.1016/j.dss.2021.113524. URL https://doi.org/10.1016/j.dss.2021.113524

[19] G. Mylonas, A. P. Kalogeras, G. Kalogeras, C. Anagnostopoulos, C. Alexakos, L. Mu˜noz, Digital twins from smart manufacturing to smart cities: A survey, IEEE Access 9 (2021) 143222–143249. doi:10.1109/ACCESS.2021.3120843.

[20] H. Xia, Z. Liu, E. Maria, X. Liu, C. Lin, Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration, Sustainable Cities and Society (2022) 104009.

[21] J. D¨ollner, Geospatial artificial intelligence: potentials of machine learning for 3d point clouds and geospatial digital twins, PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 88 (2020) 15–24.

[22] E. Shahat, C. T. Hyun, C. Yeom, City digital twin potentials: A review and research agenda, Sustainability 13 (6) (2021) 3386.

[23] A digital twin-enhanced system for engineering product family design and optimization, Journal of Manufacturing Systems 57 (2020) 82–93. doi:https://doi.org/10.1016/j.jmsy.2020.08.011.

[24] Machine learning based digital twin framework for production optimization in petrochemical industry, International Journal of Information Management 49 (2019) 502–519. doi:https://doi.org/10.1016/j.ijinfomgt.2019.05.020.

[25] Digital twins in manufacturing: An assessment of key features, Procedia CIRP 97 (2021) 178–183, 8th CIRP Conference of Assembly Technology and Systems. doi:https://doi.org/10.1016/j.procir.2020.05.222.

[26] G. Sirigu, B. Carminati, E. Ferrari, Privacy and security issues for human digital twins, in: 2022 IEEE 4th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), 2022, pp. 1–9. doi: 10.1109/TPS-ISA56441.2022.00011.

[27] W. Shengli, Is human digital twin possible?, Computer Methods and Programs in Biomedicine Update 1 (2021) 100014. doi:https://doi.org/10.1016/j.cmpbup.2021.100014. URL https://www.sciencedirect.com/science/article/pii/S2666990021000136

[28] K. P. Venkatesh, M. M. Raza, J. C. Kvedar, Health digital twins as tools for precision medicine: Considerations for computation, implementation, and regulation, npj Digit. Medicine 5 (2022). doi:10.1038/s41746-022-00694-7. URL https://doi.org/10.1038/s41746-022-00694-7

[29] K. Ahuja, D. Shah, S. Pareddy, F. Xhakaj, A. Ogan, Y. Agarwal, C. Harrison, Classroom digital twins with instrumentation-free gaze tracking, in: CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, 2021, pp. 484:1–484:9.

[30] A survey of digital twin techniques in smart manufacturing and management of energy applications, Green Energy and Intelligent Transportation 1 (2) (2022) 100014. doi:https://doi.org/10.1016/j.geits.2022.100014.

[31] G. Schrotter, C. H¨urzeler, The digital twin of the city of zurich for urban planning, PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science 88 (1) (2020) 99–112.

[32] H. Li, H. Lu, C. S. Jensen, B. Tang, M. A. Cheema, Spatial data quality in the internet of things: Management, exploitation, and prospects, ACM Computing Surveys (CSUR) 55 (3) (2022) 1–41.

[33] A. Fahmin, S. Zhang, M. A. Cheema, A. N. Toosi, H. A. Rakha, Improving eco-friendly routing considering detailed mobility profiles, driving behavior and vehicle type, in: Databases Theory and Applications: 33rd Australasian Database Conference, ADC 2022, Sydney, NSW, Australia, September 2–4, 2022, Proceedings, Springer, 2022, pp. 128–140.

[34] H. J. Jo, Y. J. Jang, J. W. Lee, J. H. Oh, Dense thermal 3d point cloud generation of building envelope by drone-based photogrammetry, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography 39 (2) (2021) 73–79.

[35] A. To, M. Liu, M. Hazeeq Bin Muhammad Hairul, J. G. Davis, J. S. Lee, H. Hesse, H. D. Nguyen, Drone-based ai and 3d reconstruction for digital twin augmentation, in: Social Computing and Social Media: Experience Design and Social Network Analysis: 13th International Conference, SCSM 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I, Springer, 2021, pp. 511–529.

[36] D. M. Bot´ın-Sanabria, J. G. Lozoya-Reyes, R. C. Vargas-Maldonado, K. L. Rodr´ıguez-Hern´andez, R. A. Ram´ırezMendoza, M. A. Ram´ırez-Moreno, J. d. J. Lozoya-Santos, Digital twin for urban spaces: An application, in: Proceedings of the International Conference on Industrial Engineering and Operations Management, Monterrey, Mexico, 2021, pp. 3–5.

[37] H. Samet, Foundations of multidimensional and metric data structures, Morgan Kaufmann, 2006.

[38] R. Chen, J. Xie, Open source databases and their spatial extensions, Open source approaches in spatial data handling (2008) 105–129.

[39] C. Oracle, Oracle spatial, http://www. oracle. com/ (2000).

[40] A. Aitchison, Beginning spatial with SQL Server 2008, Apress, 2009.

[41] C. Strobl, Postgis (2008).

[42] D. Guliato, E. V. de Melo, R. M. Rangayyan, R. C. Soares, Postgresql-ie: An image-handling extension for postgresql, Journal of digital imaging 22 (2009) 149–165.

[43] D. Shukla, C. Shivnani, D. Shah, Comparing oracle spatial and postgres postgis, IJCSC 7 (2) (2016) 95–100. [44] A. F. Sveen, Efficient storage of heterogeneous geospatial data in spatial databases, Journal of Big Data 6 (1) (2019) 1–14.

[45] Z. Yao, C. Nagel, F. Kunde, G. Hudra, P. Willkomm, A. Donaubauer, T. Adolphi, T. H. Kolbe, 3dcitydb-a 3d geodatabase solution for the management, analysis, and visualization of semantic 3d city models based on citygml, Open Geospatial Data, Software and Standards 3 (1) (2018) 1–26.

[46] A. Davoudian, L. Chen, M. Liu, A survey on nosql stores, ACM Computing Surveys (CSUR) 51 (2) (2018) 1–43.

[47] S. Agarwal, K. Rajan, Performance analysis of mongodb versus postgis/postgresql databases for line intersection and point containment spatial queries, Spatial Information Research 24 (2016) 671–677.

[48] B. Baas, Nosql spatial–neo4j versus postgis, Master’s thesis (2012).

[49] M. Ben Brahim, W. Drira, F. Filali, N. Hamdi, Spatial data extension for cassandra nosql database, Journal of Big Data 3 (2016) 1–16.

[50] Gisformats, https://gisgeography.com/gis-formats/, accessed: 2023-05-08.

[51] K. Shvachko, H. Kuang, S. Radia, R. Chansler, The hadoop distributed file system, in: 2010 IEEE 26th symposium on mass storage systems and technologies (MSST), Ieee, 2010, pp. 1–10.

[52] S. Salloum, R. Dautov, X. Chen, P. X. Peng, J. Z. Huang, Big data analytics on apache spark, International Journal of Data Science and Analytics 1 (2016) 145–164.

[53] A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, J. Saltz, Hadoop gis: a high performance spatial data warehousing system over mapreduce, Proceedings of the VLDB Endowment 6 (11) (2013) 1009–1020.

[54] A. Eldawy, M. F. Mokbel, Spatialhadoop: A mapreduce framework for spatial data, in: 2015 IEEE 31st international conference on Data Engineering, IEEE, 2015, pp. 1352–1363.

[55] J. Yu, Z. Zhang, M. Sarwat, Spatial data management in apache spark: the geospark perspective and beyond, GeoInformatica 23 (2019) 37–78.

[56] S. Hagedorn, P. G¨otze, K.-U. Sattler, Big spatial data processing frameworks: Feature and performance evaluation., in: EDBT, 2017, pp. 490–493.

[57] S. Nishimura, S. Das, D. Agrawal, A. El Abbadi, Md-hbase: A scalable multi-dimensional data infrastructure for location aware services, in: 2011 IEEE 12th International Conference on Mobile Data Management, Vol. 1, IEEE, 2011, pp. 7–16.

[58] R. Li, H. He, R. Wang, S. Ruan, Y. Sui, J. Bao, Y. Zheng, Trajmesa: A distributed nosql storage engine for big trajectory data, in: 2020 IEEE 36th International Conference on Data Engineering (ICDE), IEEE, 2020, pp. 2002–2005.

[59] M. D. Kennedy, Introducing geographic information systems with ARCGIS: a workbook approach to learning GIS, John Wiley & Sons, 2013.

[60] G. Kurt Menke, R. Smith Jr, L. Pirelli, G. John Van Hoesen, et al., Mastering QGIS, Packt Publishing Ltd, 2016.

[61] Cmeet boston’s digital twin, https://www.esri.com/about/newsroom/blog/3d-gis-boston-digital-twin/, accessed: 20823-05-0. [62] Cesium, https://cesium.com/, accessed: 2023-05-08.

[63] Nsw digital twins, https://nsw.digitaltwin.terria.io/, accessed: 20823-05-0.

[64] Jts spatial libraries, https://locationtech.github.io/jts/jts-features.html, accessed: 20823-05-0.

[65] GEOS libraries, https://libgeos.org/, accessed: 20823-05-0.

[66] Spatial4j libraries, https://github.com/locationtech/spatial4j, accessed: 20823-05-0.

[67] Geopanda, https://geopandas.org/en/stable/, accessed: 20823-05-0.

[68] Python spatial algorithms and datastructures, https://docs.scipy.org/doc/scipy/reference/spatial.html, accessed: 20823-05-0.

[69] Python spatial analysis library, https://pysal.org/, accessed: 20823-05-0.

[70] Python libraries for trajectories, https://github.com/traja-team/traja, accessed: 20823-05-0.

[71] E. Westra, Python Geospatial Analysis Essentials, Packt Publishing Ltd, 2015.

[72] Hxdr, https://hxdr.com/, accessed: 2023-05-08. [73] Opencities, https://www.bentley.com/software/opencities-planner/, accessed: 2023-05-08.

[74] A. Arman, M. E. Ali, F. M. Choudhury, K. Abdullah, Vizq: A system for scalable processing of visibility queries in 3d spatial databases, in: Proceedings of the 2017 ACM on conference on information and knowledge management, 2017, pp. 2447–2450.

[75] M. E. Ali, S. S. Eusuf, K. Abdullah, F. M. Choudhury, J. S. Culpepper, T. Sellis, The maximum trajectory coverage query in spatial databases, Proceedings of the VLDB Endowment 12 (3).

[76] A. Kumar, P. Nagarkar, P. Nalhe, S. Vijayakumar, Deep learning driven natural languages text to sql query conversion: A survey, arXiv preprint arXiv:2208.04415 (2022).

[77] R. Ding, S. Han, Y. Xu, H. Zhang, D. Zhang, Quickinsights: Quick and automatic discovery of insights from multidimensional data, in: Proceedings of the 2019 International Conference on Management of Data, 2019, pp. 317–332.

[78] R. Martinez-Maldonado, V. Echeverria, G. Fernandez Nieto, S. Buckingham Shum, From data to insights: A layered storytelling approach for multimodal learning analytics, in: Proceedings of the 2020 chi conference on human factors in computing systems, 2020, pp. 1–15.

[79] C¸ . Demiralp, P. J. Haas, S. Parthasarathy, T. Pedapati, Foresight: Recommending visual insights, arXiv preprint arXiv:1707.03877 (2017).

[80] S. Boschert, R. Rosen, Digital twin—the simulation aspect, Mechatronic futures: Challenges and solutions for mechatronic systems and their designers (2016) 59–74.

[81] P. Biswas, T. Hashem, M. A. Cheema, Safest nearby neighbor queries in road networks, IEEE Transactions on Intelligent Transportation Systems (2023).

[82] K. Agalianos, S. Ponis, E. Aretoulaki, G. Plakas, O. Efthymiou, Discrete event simulation and digital twins: review and challenges for logistics, Procedia Manufacturing 51 (2020) 1636–1641.

[83] M. Schluse, J. Rossmann, From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems, in: 2016 IEEE International Symposium on Systems Engineering (ISSE), IEEE, 2016, pp. 1–6.

[84] M. Umair, M. A. Cheema, O. Cheema, H. Li, H. Lu, Impact of covid-19 on iot adoption in healthcare, smart homes, smart buildings, smart cities, transportation and industrial iot, Sensors 21 (11) (2021) 3838.

[85] M. Schluse, M. Priggemeyer, L. Atorf, J. Rossmann, Experimentable digital twins—streamlining simulation-based systems engineering for industry 4.0, IEEE Transactions on industrial informatics 14 (4) (2018) 1722–1731.

[86] Z. Gao, A. Paul, X. Wang, Guest editorial: Digital twinning: Integrating ai-ml and big data analytics for virtual

representation, IEEE Transactions on Industrial Informatics 18 (2) (2022) 1355–1358.

[87] H. Haße, B. Li, N. Weißenberg, J. Cirullies, B. Otto, Digital twin for real-time data processing in logistics, in: Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 27, Berlin: epubli GmbH, 2019, pp. 4–28.

[88] T. Ruppert, J. Abonyi, Integration of real-time locating systems into digital twins, Journal of industrial information integration 20 (2020) 100174.

[89] T. He, A. N. Toosi, N. Akbari, M. T. Islam, M. A. Cheema, An intent-based framework for vehicular edge computing, in: 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom), IEEE, 2023, pp. 121–130.

[90] F. Alam, A. Toosi, M. Cheema, C. Cicconetti, P. Serrano, A. Iosup, Z. Tari, A. Sarvi, Serverless vehicular edge computing for the internet of vehicles, IEEE Internet Computing (2023).

[91] J. Jagannath, K. Ramezanpour, A. Jagannath, Digital twin virtualization with machine learning for iot and beyond 5g networks: research directions for security and optimal control, in: Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning, 2022, pp. 81–86.

[92] J. Baranda, J. Mangues-Bafalluy, E. Zeydan, C. Casetti, C.-F. Chiasserini, M. Malinverno, C. Puligheddu, M. Groshev, C. Guimar˜aes, K. Tomakh, et al., Aiml-as-a-service for sla management of a digital twin virtual network service, in: IEEE INFOCOM 2021-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, 2021, pp. 1–2.

[93] Z. Lv, S. Xie, Artificial intelligence in the digital twins: State of the art, challenges, and future research topics, Digital Twin 1 (12) (2022) 12.

[94] A. Sohail, M. A. Cheema, M. E. Ali, A. N. Toosi, H. A. Rakha, Data-driven approaches for road safety: A comprehensive systematic literature review, Safety Science 158 (2023) 105949.

[95] H. H. Hosamo, P. R. Svennevig, K. Svidt, D. Han, H. K. Nielsen, A digital twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics, Energy and Buildings 261 (2022) 111988.

[96] A. E. Onile, R. Machlev, E. Petlenkov, Y. Levron, J. Belikov, Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review, Energy Reports 7 (2021) 997–1015.

[97] N. K. Tran, M. A. Babar, J. Boan, Integrating blockchain and internet of things systems: A systematic review on objectives and designs, J. Netw. Comput. Appl. 173 (2021) 102844. doi:10.1016/j.jnca.2020.102844. URL https://doi.org/10.1016/j.jnca.2020.102844

[98] V. Prokhorenko, M. A. Babar, Architectural resilience in cloud, fog and edge systems: A survey, IEEE Access 8 (2020) 28078–28095. doi:10.1109/ACCESS.2020.2971007. URL https://doi.org/10.1109/ACCESS.2020.2971007

[99] Gpt-4, https://openai.com/research/gpt-4, accessed: 2023-05-08.

[100] Labda, https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html, accessed: 2023-05-08.

[101] A. Makris, K. Tserpes, G. Spiliopoulos, D. Zissis, D. Anagnostopoulos, Mongodb vs postgresql: A comparative study on performance aspects, GeoInformatica 25 (2021) 243–268.

[102] H. Shin, K. Lee, H.-Y. Kwon, A comparative experimental study of distributed storage engines for big spatial data processing using geospark, The Journal of Supercomputing (2022) 1–24

.[103] Clip: Connecting text and images, https://openai.com/research/clip, accessed: 2023-05-08. [104] M. Charitonidou, Urban scale digital twins in data-driven society: Challenging digital universalism in urban planning decision-making, International Journal of Architectural Computing 20 (2) (2022) 238–253. doi:10.1177/ 14780771211070005.

[105] L. Raes, P. Michiels, T. Adolphi, C. Tampere, A. Dalianis, S. McAleer, P. Kogut, Duet: A framework for building interoperable and trusted digital twins of smart cities, IEEE Internet Computing 26 (3) (2022) 43–50. doi:10.1109/MIC. 2021.3060962.

[106] T. Hashem, L. Kulik, Safeguarding location privacy in wireless ad-hoc networks, in: UbiComp, 2007, pp. 372–390. doi:10.1007/978-3-540-74853-3_22.

[107] M. F. Mokbel, C. Chow, W. G. Aref, The new casper: Query processing for location services without compromising privacy, in: VLDB, 2006, pp. 763–774.

[108] T. Liu, H. Li, H. Lu, M. A. Cheema, H. K.-H. Chan, Contact tracing over uncertain indoor positioning data, IEEE Transactions on Knowledge and Data Engineering (2023).

[109] Z. Lv, L. Qiao, Y. Li, Y. Yuan, F. Wang, Blocknet: Beyond reliable spatial digital twins to parallel metaverse, Patterns 3 (5) (2022) 100468.

[110] Z. Pervez, Z. Khan, A. Ghafoor, K. Soomro, SIGNED: smart city digital twin verifiable data framework, IEEE Access 11 (2023) 29430–29446. doi:10.1109/ACCESS.2023.3260621. URL https://doi.org/10.1109/ACCESS.2023.3260621

[111] S. P. Ramu, P. Boopalan, Q.-V. Pham, P. K. R. Maddikunta, T. Huynh-The, M. Alazab, T. T. Nguyen, T. R. Gadekallu, Federated learning enabled digital twins for smart cities: Concepts, recent advances, and future directions, Sustainable Cities and Society 79 (2022) 103663. doi:https://doi.org/10.1016/j.scs.2021.103663.

Authors: (1) Mohammed Eunus Ali, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (2) Muhammad Aamir Cheema, Faculty of Information Technology, Monash University, 20 Exhibition Walk, Clayton, 3164, VIC, Australia; (3) Tanzima Hashem, Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, Dhaka, 1000, Bangladesh; (4) Anwaar Ulhaq, School of Computing, Charles Sturt University, Port Macquarie, 2444, NSW, Australia; (5) Muhammad Ali Babar, School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, 5005, SA, Australia.

This paper is available on arxiv under CC BY 4.0 DEED license.

[Original Source]

Spatial digital twins face extra privacy and security risks from location data. Techniques like obfuscation, k‐anonymity, blockchain, federated learning, and cryptography help protect user identity and sensitive information.
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