Which existing methods and analytical approaches can be applied to quantitatively study metaverse?
Which challenges are associated with the quantitative investigation of metaverse and the application of those methods?
Web 3.0 continues to create a more democratic, censorship-free, and more transparent internet network by producing solutions to the problems of Web 2.0.
In this direction, metaverse technology, which is a future repetition of the internet consisting of 3-dimensional, permanent virtual spaces connected to the virtual universe, continues to reveal its difference.
At this point, the new technologies of the future continue to develop.
Contents
I. Metaverse Ecosystem
-Present and Future of Metaverse Infographics
-Why Metaverse Now?
II. Digital Twin Metaverse
-Digital Twin Types
-Digital Twin Models
-Digital Twin Patent Landscape
-Digital Twin Metaverse Use Case: AI Innovation Platform
III. Metaverse Enterprise & ESG Applications
-Metaverse Enterprise
-ESG Strategic Planning and Program Management
-Scenario Planning for Metaverse Enterprise
-TCFD Scenario Analysis
IV. ESG Digital Transformation
-ESG Sustainability Imperative
-ESG Investing and Management Consideration Core Factors
-ESG + Digital Integrated Transformation (ESGDX) Imperative
-How ESGDX Can Create New Revenue Streams?
-ESGDX for ESG Sustainability Management
-ESG Sustainability Management/Assessment Issues & Challenges & Solutions
-ESG DX Forum
V. Sustainable Smart City Development
-Metaverse for Sustainable Smart City
-Smart City Components
-Smart City Design and Development
-Smart City Management
-Smart City Financing and Business Development
Metaverse Marketing: Games and Virtual Worlds in Product PromotionSebastian Küpers
Analysts tell us that the market for in-game and virtual world advertising is expected to grow by a factor of ten in the next five years. But this is still a new frontier and marketers are confused about what's required to reach audiences in these worlds and what they can expect from investing in this area.We'll look at the most common and the most creative approaches to reaching these cyber-citizens, highlight common pitfalls, and discuss how to measure the effectiveness of these programs.
Have you ever visualized anything which has an amalgamation of many technological verticals? Have you ever experienced a reality, which arrives with a twist? No Right.
Bringing to you Metaverse, a concept of virtual and augmented reality. It is a highly immersive virtually encrypted world where people from different societies and background gather, socialize, have fun, and grow together.
Want to know more about this? Get this highly informative cryptocurrency-based info-graphic into your tab and know more MetaVerse.
Want to know more about it. Scroll this through.
Web 3.0 continues to create a more democratic, censorship-free, and more transparent internet network by producing solutions to the problems of Web 2.0.
In this direction, metaverse technology, which is a future repetition of the internet consisting of 3-dimensional, permanent virtual spaces connected to the virtual universe, continues to reveal its difference.
At this point, the new technologies of the future continue to develop.
Contents
I. Metaverse Ecosystem
-Present and Future of Metaverse Infographics
-Why Metaverse Now?
II. Digital Twin Metaverse
-Digital Twin Types
-Digital Twin Models
-Digital Twin Patent Landscape
-Digital Twin Metaverse Use Case: AI Innovation Platform
III. Metaverse Enterprise & ESG Applications
-Metaverse Enterprise
-ESG Strategic Planning and Program Management
-Scenario Planning for Metaverse Enterprise
-TCFD Scenario Analysis
IV. ESG Digital Transformation
-ESG Sustainability Imperative
-ESG Investing and Management Consideration Core Factors
-ESG + Digital Integrated Transformation (ESGDX) Imperative
-How ESGDX Can Create New Revenue Streams?
-ESGDX for ESG Sustainability Management
-ESG Sustainability Management/Assessment Issues & Challenges & Solutions
-ESG DX Forum
V. Sustainable Smart City Development
-Metaverse for Sustainable Smart City
-Smart City Components
-Smart City Design and Development
-Smart City Management
-Smart City Financing and Business Development
Metaverse Marketing: Games and Virtual Worlds in Product PromotionSebastian Küpers
Analysts tell us that the market for in-game and virtual world advertising is expected to grow by a factor of ten in the next five years. But this is still a new frontier and marketers are confused about what's required to reach audiences in these worlds and what they can expect from investing in this area.We'll look at the most common and the most creative approaches to reaching these cyber-citizens, highlight common pitfalls, and discuss how to measure the effectiveness of these programs.
Have you ever visualized anything which has an amalgamation of many technological verticals? Have you ever experienced a reality, which arrives with a twist? No Right.
Bringing to you Metaverse, a concept of virtual and augmented reality. It is a highly immersive virtually encrypted world where people from different societies and background gather, socialize, have fun, and grow together.
Want to know more about this? Get this highly informative cryptocurrency-based info-graphic into your tab and know more MetaVerse.
Want to know more about it. Scroll this through.
The metaverse is a concept of a persistent, online, 3D universe that combines multiple different virtual spaces. You can think of it as a future iteration of the internet. The metaverse will allow users to work, meet, game, and socialize together in these 3D spaces.
Metaverse has become ae buzzword in the tech industry. Not a single day goes by without a mention of it
in the media, especially around investments, startups building components, new platforms being
announced and large companies entering this world of digital engagement. There is undeniably a huge momentum of an almost real 3D virtual world, and the clarion call was perhaps Facebook rebranding itself
as Meta which will perhaps be remembered as a red letter moment in the evolution of the Metaverse.
Contents
I. Metaverse Ecosystem
-Metaverse Taxonomy
-Metaverse Examples
-Facebook Metaverse Vision
-Microsoft Metaverse Solution
-Nissan Metaverse Use Case
-Metaverse Industry Ecosystem
-Metaverse Next
II. ESG Sustainability
-ESG Sustainability Imperative
-UN Sustainable Development Goals (SDGs)
-ESG Digital Transformation (DX)
-The Fourth Wave of Environmentalism
-Microsoft AI for Earth Project
-UPCO2 Blockchain based Carbon Credits Token Project
-BlocPower: Fighting Climate Change with IoT & Data
-AI Blockchain IoT for ESG DX AT A Glance
-ESG DX Innovation Insights from Patents
-ESG DX for Sustainable Business Innovation & Growth
-ESG DX Forum
III. Metaverse for ESG Sustainability
-Dassault Systemes Digital Twins for Sustainability
-Microsoft Metaverse for Sustainability Use Cases
-Metaverse for Sustainable Smart City (ESG City) Development
The metaverse is not new! The technology behind the latest immersive experiences has been building for years. Find out more about the history of the metaverse.
Metaverse - The Future of Marketing and Web 3.0.pdfthetechnologynews
The global metaverse market was valued at USD 107,100.67 Million in 2020, and it is expected to reach a value of USD 758,600.86 Million by 2027, at a CAGR of 37.1% over the forecast period (2020 - 2027).
Get To Know More : https://skyquestt.com/report/metaverse-market
The Metaverse is a virtual interactive self-sufficient ecosystem comprising mobile networks, augmented reality, social media, gaming, virtual reality, e-commerce, cryptocurrency, and workplace. This universe is envisioned as the internet's future, bringing together augmented reality (AR), virtual reality (VR), and physical worlds in a common digital arena. NFTs and online events are exploding, opening up a world of possibilities for the metaverse and associated technologies.
The transition to the Metaverse is fast approaching. Several components and features of this open-source platform have progressed to the point where they may be smoothly merged to investigate the idea of building a parallel virtual reality. NFTs and online events are exploding, opening up a world of possibilities for the metaverse.
Global Metaverse Market Segmental Analysis
The Global Metaverse Market is segmented based on Type, Technology, and Application. Based on Type it is categorized into: Mobile and Desktop. Based on Technology it is categorized into: Blockchain, VR & AR, Mixed Reality, and Others. Based on Application it is categorized into: Gaming, Online Shopping, Content Creation, Social Media, and Others. Based on region it is categorized into: North America, Europe, Asia-Pacific, South America, and MEA.
Analysis by Application
The gaming segment is expected to be the largest segment in the Metaverse market throughout the forecast period (2020-2027).Due to major ongoing innovations and advances by developers, as well as a rising focus on improving immersion and making games more realistic, the gaming segment will have the leading revenue share of more than 25% in 2021. Furthermore, corporations' growing emphasis on using games to enhance their corporate image is expected to drive revenue growth.
China, the world's second-largest economy, is expected to reach a market size of USD 103,100.26 million in 2026, with a CAGR of 38.1 % throughout the forecast period. Other notable global markets include Japan and Canada, which are expected to increase at 31.3% and 29.6%, respectively, throughout the forecast period. Germany is expected to develop at a 36.8% CAGR within Europe, while the rest of the European market would reach USD 59,500.67 Million by the conclusion of the forecast period.
The 5 Biggest Virtual, Augmented, And Mixed Reality Trends In 2022Bernard Marr
With the hype around the metaverse, there are a lot of things happing in the extended reality (XR) world. Here we explore the five biggest trends in VR, AR, and MR for 2022 including new headsets, 5G, as well as applications in retail and education.
AR101 Lecture - Introduction to Augmented Reality. Lecture providing an introduction to AR, the history of AR and some example applications. Presented by Mark Billinghurst at the AR101 summer school at the ISMAR 2016 conference, September 18th 2016.
Quest 2 and the future of metaverse v2.0 210908Michael Lesniak
Brief overview of the impact of the Quest 2 launch in S. Korea on the development of the metaverse here, and the near future of the metaverse worldwide.
Note:
Michael's Metaverse for Dummies by Michael A. Lesniak is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at http://www.malesniak.com/2020/09/blog-post.html.
The metaverse is a synthesized world which is composed of user-controlled avatars, digital things, virtual environments, and other computer-generated elements, where humans (represented by avatars) can use their virtual identity through any smart device to communicate, collaborate, and socialize with each other. Physical Infrastructures: The physical world offers supporting infrastructures (including sensing/control, communication, computation, and storage infrastructures) to the metaverse to support multi-sensory data perception, transmission, processing, and caching, as well as physical control, thereby enabling efficient interactions with both the digital and human worlds.Metaverse Engine: The metaverse engine uses the big data from the real world as inputs to generate, maintain, and update the virtual world via the interactivity, AI, digital twin, and blockchain technologies. Particularly, with the assistance of XR and HCI techniques, users situated in physical environments are able to immersively control their digital avatars in the metaverse via their senses and bodies for diverse collective and social activities such as car racing, dating, and virtual item trading.
Building your own metaverse, prepare your own contract with solidity and perform your first transaction in Web 3.0
#metaverse #vr #ar #virtualreality #augmentedreality #solidity #blockchain #nft #web3.0 #3D #threejs
The metaverse is a concept of a persistent, online, 3D universe that combines multiple different virtual spaces. You can think of it as a future iteration of the internet. The metaverse will allow users to work, meet, game, and socialize together in these 3D spaces.
Metaverse has become ae buzzword in the tech industry. Not a single day goes by without a mention of it
in the media, especially around investments, startups building components, new platforms being
announced and large companies entering this world of digital engagement. There is undeniably a huge momentum of an almost real 3D virtual world, and the clarion call was perhaps Facebook rebranding itself
as Meta which will perhaps be remembered as a red letter moment in the evolution of the Metaverse.
Contents
I. Metaverse Ecosystem
-Metaverse Taxonomy
-Metaverse Examples
-Facebook Metaverse Vision
-Microsoft Metaverse Solution
-Nissan Metaverse Use Case
-Metaverse Industry Ecosystem
-Metaverse Next
II. ESG Sustainability
-ESG Sustainability Imperative
-UN Sustainable Development Goals (SDGs)
-ESG Digital Transformation (DX)
-The Fourth Wave of Environmentalism
-Microsoft AI for Earth Project
-UPCO2 Blockchain based Carbon Credits Token Project
-BlocPower: Fighting Climate Change with IoT & Data
-AI Blockchain IoT for ESG DX AT A Glance
-ESG DX Innovation Insights from Patents
-ESG DX for Sustainable Business Innovation & Growth
-ESG DX Forum
III. Metaverse for ESG Sustainability
-Dassault Systemes Digital Twins for Sustainability
-Microsoft Metaverse for Sustainability Use Cases
-Metaverse for Sustainable Smart City (ESG City) Development
The metaverse is not new! The technology behind the latest immersive experiences has been building for years. Find out more about the history of the metaverse.
Metaverse - The Future of Marketing and Web 3.0.pdfthetechnologynews
The global metaverse market was valued at USD 107,100.67 Million in 2020, and it is expected to reach a value of USD 758,600.86 Million by 2027, at a CAGR of 37.1% over the forecast period (2020 - 2027).
Get To Know More : https://skyquestt.com/report/metaverse-market
The Metaverse is a virtual interactive self-sufficient ecosystem comprising mobile networks, augmented reality, social media, gaming, virtual reality, e-commerce, cryptocurrency, and workplace. This universe is envisioned as the internet's future, bringing together augmented reality (AR), virtual reality (VR), and physical worlds in a common digital arena. NFTs and online events are exploding, opening up a world of possibilities for the metaverse and associated technologies.
The transition to the Metaverse is fast approaching. Several components and features of this open-source platform have progressed to the point where they may be smoothly merged to investigate the idea of building a parallel virtual reality. NFTs and online events are exploding, opening up a world of possibilities for the metaverse.
Global Metaverse Market Segmental Analysis
The Global Metaverse Market is segmented based on Type, Technology, and Application. Based on Type it is categorized into: Mobile and Desktop. Based on Technology it is categorized into: Blockchain, VR & AR, Mixed Reality, and Others. Based on Application it is categorized into: Gaming, Online Shopping, Content Creation, Social Media, and Others. Based on region it is categorized into: North America, Europe, Asia-Pacific, South America, and MEA.
Analysis by Application
The gaming segment is expected to be the largest segment in the Metaverse market throughout the forecast period (2020-2027).Due to major ongoing innovations and advances by developers, as well as a rising focus on improving immersion and making games more realistic, the gaming segment will have the leading revenue share of more than 25% in 2021. Furthermore, corporations' growing emphasis on using games to enhance their corporate image is expected to drive revenue growth.
China, the world's second-largest economy, is expected to reach a market size of USD 103,100.26 million in 2026, with a CAGR of 38.1 % throughout the forecast period. Other notable global markets include Japan and Canada, which are expected to increase at 31.3% and 29.6%, respectively, throughout the forecast period. Germany is expected to develop at a 36.8% CAGR within Europe, while the rest of the European market would reach USD 59,500.67 Million by the conclusion of the forecast period.
The 5 Biggest Virtual, Augmented, And Mixed Reality Trends In 2022Bernard Marr
With the hype around the metaverse, there are a lot of things happing in the extended reality (XR) world. Here we explore the five biggest trends in VR, AR, and MR for 2022 including new headsets, 5G, as well as applications in retail and education.
AR101 Lecture - Introduction to Augmented Reality. Lecture providing an introduction to AR, the history of AR and some example applications. Presented by Mark Billinghurst at the AR101 summer school at the ISMAR 2016 conference, September 18th 2016.
Quest 2 and the future of metaverse v2.0 210908Michael Lesniak
Brief overview of the impact of the Quest 2 launch in S. Korea on the development of the metaverse here, and the near future of the metaverse worldwide.
Note:
Michael's Metaverse for Dummies by Michael A. Lesniak is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at http://www.malesniak.com/2020/09/blog-post.html.
The metaverse is a synthesized world which is composed of user-controlled avatars, digital things, virtual environments, and other computer-generated elements, where humans (represented by avatars) can use their virtual identity through any smart device to communicate, collaborate, and socialize with each other. Physical Infrastructures: The physical world offers supporting infrastructures (including sensing/control, communication, computation, and storage infrastructures) to the metaverse to support multi-sensory data perception, transmission, processing, and caching, as well as physical control, thereby enabling efficient interactions with both the digital and human worlds.Metaverse Engine: The metaverse engine uses the big data from the real world as inputs to generate, maintain, and update the virtual world via the interactivity, AI, digital twin, and blockchain technologies. Particularly, with the assistance of XR and HCI techniques, users situated in physical environments are able to immersively control their digital avatars in the metaverse via their senses and bodies for diverse collective and social activities such as car racing, dating, and virtual item trading.
Building your own metaverse, prepare your own contract with solidity and perform your first transaction in Web 3.0
#metaverse #vr #ar #virtualreality #augmentedreality #solidity #blockchain #nft #web3.0 #3D #threejs
MSR 2022 Foundational Contribution Award Talk: Software Analytics: Reflection...Tao Xie
MSR 2022 Foundational Contribution Award Talk on "Software Analytics: Reflection and Path Forward" by Dongmei Zhang and Tao Xie
https://conf.researchr.org/info/msr-2022/awards
SC6 Workshop 1: Big Data Europe platform requirements and draft architecture:...BigData_Europe
Presentation by Martin Kaltenböck, Semantic Web Company, at the first workshop of Societal Challlenge 6 in the BigDataEurope project, taking place in Luxembourg on 18 November 2015.
http://www.big-data-europe.eu/social-sciences/
Abstract: In many fields, such as industry, commerce, government, and education, knowledge discovery and data
mining can be immensely valuable to the subject of Artificial Intelligence. Because of the recent increase in
demand for KDD techniques, such as those used in machine learning, databases, statistics, knowledge acquisition,
data visualisation, and high performance computing, knowledge discovery and data mining have grown in
importance. By employing standard formulas for computational correlations, we hope to create an integrated
technique that can be used to filter web world social information and find parallels between similar tastes of
diverse user information in a variety of settings
I have been working on a new breed of estimation methodologies called "Open estimation methodologies". They can be called "Deliverable based estimation methodologies" also. This presentation is about this family of methodologies.
Building a robust machine learning model is not an easy task. After all, most POCs don't make it into production. And even if they make it into production, you still need to monitor its performance.
How can you build performant, tolerant, stable, predictive models that have known and fair biases? How can you make sure your models yield their value over time and stay performant after your team has deployed them? What are the current practices of model validation (or lack of), how are they flawed, and how could we improve them?
Simon Dagenais from Snitch AI will go through the reasons behind using an efficient validation framework that goes beyond the common metrics used by ML practitioners and why these tests matter when building high-quality models.
Agenda:
-----------
3:45pm - 4:00pm: Arrival & Networking
4:00pm - 4:15pm: News & Intro
4:15pm - 5:15pm: How to QA your ML models
5:15pm - 5:30pm: Virtual Snack & Networking
About the main speaker:
---------------------------------
Simon Dagenais is the Lead Data Scientist at Snitch AI, a machine learning validation tool. Before working on Snitch AI, Simon was a data scientist consultant at Moov AI, the parent company of Snitch AI. During his time as a consultant, he built and deployed custom ML solutions to solve business needs at companies like DRW, Société de Transport de Montréal and Cogeco. He now aspires to solve problems that data science teams will encounter during the course of a ML project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.
The talk at Twente University on 28 July 2014 Julia Kiseleva
Predictive Web Analytics is aimed at understanding behavioural patterns of users of various web-based applications: e-commerce, ubiquitous and mobile computing, and computational advertising. Within these applications business decisions often rely on two types of predictions: an overall or particular user segment demand predictions and individualised recommendations for visitors. Visitor behaviour is inherently sensitive to the context, which can be de ned as a collection of external factors. Context-awareness allows integrating external explanatory information into the learning process and adapting user behaviour accordingly. The importance of context-awareness has been recognised by researchers and practitioners in many disciplines, including recommendation systems, information retrieval, personalization, data mining, and marketing. We focus on studying ways of context discovery and its integration into predictive analytics.
Model design to develop online web based questionnaireTELKOMNIKA JOURNAL
This research aims to create a web-based application for sharing questionnaires.
The developed features are creating questionnaires, sharing questionnaire in the
dashboard, filter the questionnaire as the requested criterias, exchange the rewarded
coins for gifts, export questionnaire data to a document, set a limit for
the questionnaire for each device. The development will be using data collecting
using questionnaire and literature study. Then, software development life cycle
(SDLC) waterfall research methodology will be used for the website system development.
Result of this research will be a website application that will be used
for questionnaire maker so that they can reach the respondent count target, have
a suitable respondent (minimize respondent who does not meet the criteria), and
also can collect more validated data.
Similar to Methods and Challenges for Metaverse Analytics.pdf (20)
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
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Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
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4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Methods and Challenges for Metaverse Analytics.pdf
1. Methods and Challenges for Metaverse Analytics
Safaa Alnabulsi, Wiebke Peters, Neha Shrestha
Fachgebiet Service-centric Networking | TU Berlin & Telekom Innovation Laboratories
IoSL Seminar Summer Term 2022
2. Who we are
Methods and Challenges for Metaverse Analytics | SS 22
Safaa Alnabulsi
Fundamentals
Wiebke Peters
Analytical Methods
Neha Shrestha
Challenges
3. Agenda
1. Motivation
2. Research Questions
3. Foundations
4. Metaverse Analytics
5. Challenges for Metaverse Analytics
6. Summary
7. Outlook
8. References
Methods and Challenges for Metaverse Analytics | SS 22
Page 3
4. Motivation
● Technology on the rise
● New data streams generate new
possibilities for analytics
● Lack of quantivate measurement
studies
Methods and Challenges for Metaverse Analytics | SS 22
Page 4
https://medium.com/predict/the-metaverse-hype-cycle-58c9f690b534
5. Agenda
1. Motivation
2. Research Questions
3. Foundations
4. Metaverse Analytics
5. Challenges for Metaverse Analytics
6. Summary
7. Outlook
8. References
Methods and Challenges for Metaverse Analytics | SS 22
Page 5
6. Research Questions
● Which existing methods and analytical approaches can be applied to quantitatively study
metaverse?
● Which challenges are associated with the quantitative investigation of metaverse and the
application of those methods?
Methods and Challenges for Metaverse Analytics | SS 22
Page 6
7. Agenda
1. Motivation
2. Research Questions
3. Foundations
4. Metaverse Analytics
5. Challenges for Metaverse Analytics
6. Summary
7. Outlook
8. References
Methods and Challenges for Metaverse Analytics | SS 22
Page 7
8. Foundations: What is the Metaverse?
Methods and Challenges for Metaverse Analytics | SS 22
Page 8
https://expatguideturkey.com/a-new-invesment-idea-in-the-metaverse-universe/ https://stealthoptional.com/how-to/what-is-metaverse-land-how-to-buy/
9. Foundations: Avatars is the Metaverse
Challenges and Methods for Metaverse Analytics | SS 22
Page 9
https://www.youtube.com/watch?v=Uvufun6xer8
https://beyondphilosophy.com/is-this-the-future-great-practical-examples-of-the-begin
ning-of-the-metaverse-2/
10. Foundations: Gears in the Metaverse
Methods and Challenges for Metaverse Analytics | SS 22
Page 10
Oculus Quest VR VR Gloves from Meta Reality Labs
https://www.protocol.com/meta-haptic-gloves
https://store.facebook.com/gb/en/quest/products/quest-2
11. Foundations: Interactions
● Social interactions: cafes, discussions
● Entertainment: gaming, concerts, events
● Business: meetings, work, team events
● Shopping
Methods and Challenges for Metaverse Analytics | SS 22
Page 11
Horizon Workrooms - Remote Collaboration Reimagined
https://youtu.be/lgj50IxRrKQ
12. Foundations: Events in the Metaverse
Methods and Challenges for Metaverse Analytics | SS 22
Page 12
Travis scott's concert on Fortnite
Dolce & Gabbana sends Avatars with cats heads to the catwalk
https://www.forbes.com/sites/paultassi/2020/04/23/fortnites-travis-scott-concert-was-a-stu
nning-spectacle-and-a-glimpse-at-the-metaverse/?sh=d0075802e1f5
https://world.dolcegabbana.com/discover/dolcegabbana-enters-the-metaverse/
13. Foundations: Centralized vs. Decentralized
[23]
Methods and Challenges for Metaverse Analytics | SS 22
Page 13
Centralized Decentralized
Control Platform choice User choice
Governance Single entity Multiple users
Data
Collected, stored and owned by
the corporate who owns the
platform
Accessible online to all users, stored,
encrypted & owned by the
community
Examples Fortnite and Roblox Axie Infinity (AXS) and Decentraland
14. Agenda
1. Motivation
2. Research Questions
3. Foundations
4. Metaverse Analytics
5. Challenges for Metaverse Analytics
6. Summary
7. Outlook
8. References
Methods and Challenges for Metaverse Analytics | SS 22
Page 14
15. Metaverse Analytics
● Analytics can be defined as information gained from the computational analysis of data
● Variety of data groups emerging from the metaverse, such as
○ Location data
○ Interaction data
○ Sensor data
● Which existing methods and analytical approaches can be applied to quantitatively study
metaverses?
Methods and Challenges for Metaverse Analytics | SS 22
Page 15
16. Metaverse Analytics: Location Data
Methods and Challenges for Metaverse Analytics | SS 22
Page 16
https://stackedhomes.com/editorial/the-future-of-digital-real-estate-and-the-metaverse-should-you-in
vest/
17. Metaverse Analytics: Location Data -
Virtual Real Estate
Methods and Challenges for Metaverse Analytics | SS 22
Page 17
Time Series Analysis and Forecasting
● Based on historical data
● Incorporate real and virtual world
aspects
● Extract meaningful knowledge of
influences on the price
https://cryptonews.com/news/metaverse-land-prices-fall-but-still-outperform-ethereum.ht
m
18. Metaverse Analytics: Location Data - Movement
Methods and Challenges for Metaverse Analytics | SS 22
Page 18
Position Heat Maps
● Tracking an avatars movement
● Create visual insight for understanding
movement patterns
https://vadr.io/demo/
19. Metaverse Analytics: Location Data
Methods and Challenges for Metaverse Analytics | SS 22
Page 19
Findings:
Virtual Real Estate:
● Lack of studies for virtual real estate
● Common for offline real estate, as seen in [7, 11]
Movement Analysis:
● Traffic analysis performed in Second Life [13], by
crawling position update messages between client
and server
https://theconversation.com/the-metaverse-is-money-and-crypto-is-king-why-youll-be-
on-a-blockchain-when-youre-virtual-world-hopping-171659
20. Metaverse Analytics: Interaction Data (1/3)
Methods and Challenges for Metaverse Analytics | SS 22
Page 20
https://medium.com/@freedomx/interactions-in-the-metaverse-382799ad985e
21. Metaverse Analytics: Interaction Data (2/3)
Social Network Analysis [14]
● Connections between avatars
visualized by lines (edges)
● Edges display intensity of
relationship
● Comparable metrics for their
evaluation, such as connectedness
and reach
Methods and Challenges for Metaverse Analytics | SS 22
Page 21
https://injuredly.com/singaporeans-least-optimistic-about-southeast-asias-metaverse/
22. Metaverse Analytics: Interaction Data (3/3)
Related research:
● Social network analysis based on groups in
metaverses found[8]:
○ Similar structure to real world
○ Comparable social behavior
● Automated sensing proposed as opportunity
[10]
Methods and Challenges for Metaverse Analytics | SS 22
Page 22
https://ieeexplore.ieee.org/document/5484741/
23. Metaverse Analytics: Sensor Data
Methods and Challenges for Metaverse Analytics | SS 22
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https://store.facebook.com/gb/en/quest/products/quest-2
24. Metaverse Analytics: Translating Sensor Signals
Methods and Challenges for Metaverse Analytics | SS 22
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Automated Sensing
● Autonomously translate sensor signals into
structured data
● Use this data as basis for analytics
https://www.mdpi.com/1424-8220/20/21/6045
25. Metaverse Analytics: Sensor Data - Eye-Tracking
Gaze Heat Maps
● Create visual insight for
understanding visual patterns
● Tracks eyesight through VR-Set
Methods and Challenges for Metaverse Analytics | SS 22
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https://vadr.io/demo/
26. Metaverse Analytics: Sensor Data
Methods and Challenges for Metaverse Analytics | SS 22
Page 26
Related research:
● Web-crawling, similar to study in Second Life [10]
● Examples in other fields
○ Eye-tracking for virtual learning
environments, as seen in [12]
○ Movement and position tracking, as
proposed by [13]
○ Voice recognition, proposed by Baumann
in 1993 [1]
https://towardsdatascience.com/how-to-generate-synthetic-tabular-data-bcde7c28038a
based on [10]
Hardware Software
Interaction
PHP-
Script
Tabular Data
27. Page 27
Metaverse Analytics
Methods and Challenges for Metaverse Analytics | SS 22
https://grow.google/certificates/data-analytics/#?modal_active=none
28. Agenda
1. Motivation
2. Research Questions
3. Foundations
4. Metaverse Analytics
5. Challenges for Metaverse Analytics
6. Summary
7. Outlook
8. References
Methods and Challenges for Metaverse Analytics | SS 22
Page 28
29. Challenges: Data Privacy
● Data are collected from VR devices [5]
● Sensitive information such as locations,
shopping preferences and financial details
[4,5]
● Can track people to a higher degree than
real world [4,5]
● Recent studies has shown that anonymous
data can be identifiable [4]
Methods and Challenges for Metaverse Analytics | SS 22
Page 29
https://www.business2community.com/tech-gadgets/how-to-make-sure-your-company-data-i
s-accessible-0427094
30. Challenges: Computation
● Transforming sensor data to new data types in
tabular format [10]
● Huge data volume => high demand for
computational resources [22]
Methods and Challenges for Metaverse Analytics | SS 22
Page 30
https://towardsdatascience.com/how-to-generate-synthetic-tabular-data-bcde7c28038a
Tabular Data
Hardware Software
Interaction
PHP-
Script
Tabular data
31. Challenges: Data Accessibility
=> Data access in centralized metaverse limited
Methods and Challenges for Metaverse Analytics | SS 22
Page 31
https://www.business2community.com/tech-gadgets/how-to-make-sure-your-company-data-is-access
ible-0427094
Centralized Decentralized
Data
Collected, stored and
owned by the
corporate who owns
the platform
Accessible online to all
users, stored, encrypted
& owned by the
community
32. Agenda
1. Motivation
2. Research Questions
3. Foundations
4. Metaverse Analytics
5. Challenges for Metaverse Analytics
6. Summary
7. Outlook
8. References
Methods and Challenges for Metaverse Analytics | SS 22
Page 32
33. Research Questions
● Which existing methods and analytical approaches can be applied to quantitatively study
metaverse?
● Which challenges are associated with the quantitative investigation of metaverse and the
application of those methods?
Methods and Challenges for Metaverse Analytics | SS 22
Page 33
34. Summary
Methods and Challenges for Metaverse Analytics | SS 22
Page 34
Data Type Location Interaction Sensor
Proposed Methods Time Series Analysis,
Position Heat Maps
Social Network Analysis
Automated Sensing, Gaze Heat
Maps
Research Interest Investment, movement-related
insight, effect of lacking physical
restrictions, marketing
Social studies and human
behavior, marketing
Human behavior, psychology,
marketing
Existing Research
[10] [8], [10] [12],
Privacy Challenge High degree of tracking possible, access to sensible information, invasion of privacy
Computational
Challenge
High data volumes, sensor data translation
Data Access Centralized metaverses have full control of sensitive data
35. Agenda
1. Motivation
2. Research Questions
3. Foundations
4. Metaverse Analytics
5. Challenges for Metaverse Analytics
6. Summary
7. Outlook
8. References
Methods and Challenges for Metaverse Analytics | SS 22
Page 35
38. References(1/5)
[1] Baumann, J. (1993). Voice recognition. Human Interface Technology Laboratory.
[2] Cline, Ernest. Ready Player One. New York: Crown Publishers, 2011. Print.
[3] Darlin Boonparn Chalat Yawised, Kritcha Apasrawirote. 2022. From traditional business shifted towards
transformation: The emerging business opportunities and challenges in ’Metaverse’ era. (2022).
[4] Egliston, B. & Carter, M. (2021). Critical questions for Facebook’s virtual reality: data, power and the metaverse .
Internet Policy Review, 10(4). https://doi.org/10.14763/2021.4.1610
[5] Ellysse Work. 2021. Balancing User Privacy and Innovation in Augmented and Virtual Reality-KEY TAKEAWAYS.
(2021).
[6] Gadekallu, Thippa & Huynh-The, Thien & Wang, Weizheng & Yenduri, Gokul & Ranaweera, Pasika & Pham,
Quoc-Viet & Costa, D.B. & Liyanage, Madhusanka. (2022). Blockchain for the Metaverse: A Review.
Methods and Challenges for Metaverse Analytics | SS 22
Page 38
39. References(2/5)
[7] Ghysels, E., Plazzi, A., Valkanov, R., & Torous, W. (2013). Forecasting real estate prices. Handbook of economic
forecasting, 2, 509-580
[8] Gregory Thomas Stafford. 2013. Analysis of social networks in a virtual world. (2013)
Kurka, David Burth, Alan Godoy, and Fernando J. Von Zuben. "Online social network analysis: A survey of research
applications in computer science." arXiv preprint arXiv:1504.05655 (2015).
[9] Fernandes, S., Antonello, R., Moreira, J., Sadok, D., & Kamienski, C. (2007, June). Traffic analysis beyond this
world: the case of Second Life. In 17th International workshop on Network and operating systems support for digital
audio and video, University of Illinois, Urbana-Champaign (pp. 4-5).
[10] L. A. Overbey, G. McKoy, J. Gordon and S. McKitrick, "Automated sensing and social network analysis in virtual
worlds," 2010 IEEE International Conference on Intelligence and Security Informatics, 2010, pp. 179-184
Methods and Challenges for Metaverse Analytics | SS 22
Page 38
40. References(3/5)
[11] Meen, G. (2002). The time-series behavior of house prices: a transatlantic divide?. Journal of housing
economics, 11(1), 1-23.
[12] Mika Calbureanu-Popescu Calin Markopoulos Panagiotis Ranttila Pertti Laukkanen Sami Laivuori Niko Ravyse
Werner Saarinen Juha Nghia Tran Markopoulos, Evangelos Luimula. 2022. Neural Network Driven Eye Tracking
Metrics and Data Visualization in Metaverse and Virtual Reality Maritime Safety Training. (2022)
[13] Niehorster, D. C., Li, L., & Lappe, M. (2017). The accuracy and precision of position and orientation tracking in
the HTC vive virtual reality system for scientific research. i-Perception, 8(3), 2041669517708205.
[14] Ning, Huansheng, et al. "A Survey on Metaverse: the State-of-the-art, Technologies, Applications, and
Challenges." arXiv preprint arXiv:2111.09673 (2021).
Methods and Challenges for Metaverse Analytics | SS 22
Page 39
41. References(4/5)
[15] Rui Wang Xiao Hu Bin Wang, Fei-Yue Qin. 2022. MetaSocieties in Metaverse: MetaEconomics and
MetaManagement for MetaEnterprises and MetaCities. IEEE Transactions on Computational Social Systems 9, 1
(2022), 2–7.
[16] Scott, John. "Social network analysis." Sociology 22, no. 1 (1988): 109-127.
[17] Stephenson, Neal. Snow Crash. New York: Bantam Books, 1993. Print.
[18] Su, Yanhui, Per Backlund, and Henrik Engström. "Comprehensive review and classification of game analytics."
Service Oriented Computing and Applications 15.2 (2021).
[19] The Metaverse and How We'll Build It Together -- Connect 2021
Methods and Challenges for Metaverse Analytics | SS 22
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42. References(5/5)
[20] Wei, William WS. "Time series analysis." The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2.
2006.
[21] Yongwoog Andy Jeon. 2022. Reading Social Media Marketing Messages as Simulated Self Within a Metaverse:
An Analysis of Gaze and Social Media Engagement Behaviors within a Metaverse Platform. In 2022 IEEE
Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). IEEE, 301–303.
[22] Zhao, Y., Jiang, J., Chen, Y., Liu, R., Yang, Y., Xue, X., & Chen, S. (2022). Metaverse: Perspectives from
graphics, interactions and visualization. Visual Informatics, 6(1), 56-67. https://doi.org/10.1016/j.visinf.2022.03.002
[23] The Foundation Of The Metaverse: Centralization Versus Decentralization
Methods and Challenges for Metaverse Analytics | SS 22
Page 41
43. Motivation: History
Methods and Challenges for Metaverse Analytics | SS 22
Page 42
“Snow Crash” Novel 1992 [7] Meta announcement in 2021 [9]
https://www.youtube.com/watch?v=Uvufun6xer8
“Ready Player One” Novel 2011
44. Fundamentals - Possible Question
● Will Metaverse hype continue to drop off?
● What’s the relationship between impressive experience and used gears?
● Do you think events happening in the metaverse would replace the real world ones?
● Can we do analytics on centralized metaverse ?
● In your opinion, which is better centralized or decentralized metaverse ?
Methods and Challenges for Metaverse Analytics | SS 22
Page 43
45. Fundamentals - Possible Question
● What is the difference between virtual reality and
Metaverse?
Methods and Challenges for Metaverse Analytics | SS 22
Page 44
46. Biometric data
● Malicious users can monitor and collect metaverse users behaviour (eg. interaction with other users, purchase
actions) and biometrics (eg. facial expressions, vocal inflections) in real time, which could be used to
recognize the user.
● Use eye tracking sensors
● Recordings of people’s faces and emotional states, and possibly bodily movements might be required to
create virtual avatars.
● Incorporate facial recognition technology and use it to identify random passers by and collect and transmit
their location data back to the developers.
● Users creates various data such as intimate information (eg. messages, voice, and video), corporate secrets
used for work, and the personal information needed for service to continue.
● Its not clear yet how companies would use such data, but history shows its unlikely that all of this new data is
going to be handled in a way that is good for user privacy
Methods and Challenges for Metaverse Analytics | SS 22
Page 45
47. High data volume
● Every person who enters the metaverse creates a data file, data continues to grow as a result of social
interaction
● Data labelling and data organization will be a challenge with huge amounts of data produced by metaverse
application
● Data produced will be huge, unstructured and real time
● Current computation is not yet powerful enough to host thousands, or millions, of people in a live, shared,
persistent, synchronous space, highlighting another hurdle for the metaverse to overcome
Methods and Challenges for Metaverse Analytics | SS 22
Page 46
49. Source
Number
Topic Author Method Findings
Metaverse
or
Technology
8
Social Network
Analysis
Stafford, G
SNA by crawling group names and
members
Comparable relationships and structures as n real world SecondLife
9 Traffic Analysis
Fernandes et
al.
Understanding traffic profile by
and implications for traffic
management, We visited different
places at different hours and days,
summarizing more than 100 hours
of experiments using SL
How developers, designers and researchers in both networking and virtual
environments fields can improve the performance of their systems or
networks, no comparison between metaverses, similart traffic structure to
other games, profiling the traffic generated by its servers and clients
SecondLife
10
SNA Automated
Sensing
Overbey et
al
Automated sensing, behaviors and
communications that occur
between and among individuals
that are not persistent for SNA on
terrorist groups
Difficulty in web crawling info from sensors, easy to establish sub groups, n
undirected, unweighted social network, virtual location effects connectedness
SecondLife
12
Eye-tracking for
tracking learning
progress
Markopoulo
s et al
NN for eye-tracking learning
progress
Visualizing eyesight to identify learning progress feasible just VR
13
Movement and
Position tracking
Niehorster
et al.
Accuracy and precision of VR
position and motion tracking with
HTC vive
Tracking is subjectively fast and supports good presence, the system end-to-end
latency is low
just VR
Metaverse Analytics: Overview