Methods and Challenges for Metaverse Analytics.pdf

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

Who we are
Methods and Challenges for Metaverse Analytics | SS 22
Safaa Alnabulsi
Fundamentals
Wiebke Peters
Analytical Methods
Neha Shrestha
Challenges

Agenda
1. Motivation
2. Research Questions
3. Foundations
4. Metaverse Analytics
5. Challenges for Metaverse Analytics
6. Summary
7. Outlook
8. References
Page 3

Motivation
● Technology on the rise
● New data streams generate new
possibilities for analytics
● Lack of quantivate measurement
studies
Page 4
https://medium.com/predict/the-metaverse-hype-cycle-58c9f690b534

Agenda
1. Motivation
3. Foundations
6. Summary
7. Outlook
8. References
Page 5

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?
Page 6

Agenda
1. Motivation
3. Foundations
6. Summary
7. Outlook
8. References
Page 7

Foundations: What is the Metaverse?
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/

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/

Foundations: Gears in the Metaverse
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

Foundations: Interactions
● Social interactions: cafes, discussions
● Entertainment: gaming, concerts, events
● Business: meetings, work, team events
● Shopping
Page 11
Horizon Workrooms - Remote Collaboration Reimagined
https://youtu.be/lgj50IxRrKQ

Foundations: Events in the Metaverse
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/

Foundations: Centralized vs. Decentralized
[23]
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

Agenda
1. Motivation
3. Foundations
6. Summary
7. Outlook
8. References
Page 14

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
metaverses?
Page 15

Metaverse Analytics: Location Data
Page 16
https://stackedhomes.com/editorial/the-future-of-digital-real-estate-and-the-metaverse-should-you-in
vest/

Metaverse Analytics: Location Data -
Virtual Real Estate
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

Metaverse Analytics: Location Data - Movement
Page 18
Position Heat Maps
● Tracking an avatars movement
● Create visual insight for understanding
movement patterns
https://vadr.io/demo/

Metaverse Analytics: Location Data
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

Metaverse Analytics: Interaction Data (1/3)
Page 20
https://medium.com/@freedomx/interactions-in-the-metaverse-382799ad985e

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
Page 21
https://injuredly.com/singaporeans-least-optimistic-about-southeast-asias-metaverse/

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]
Page 22
https://ieeexplore.ieee.org/document/5484741/

Metaverse Analytics: Sensor Data
Page 23
https://store.facebook.com/gb/en/quest/products/quest-2

Metaverse Analytics: Translating Sensor Signals
Page 24
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

Metaverse Analytics: Sensor Data - Eye-Tracking
Gaze Heat Maps
● Create visual insight for
understanding visual patterns
● Tracks eyesight through VR-Set
Page 25
https://vadr.io/demo/

Metaverse Analytics: Sensor Data
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

Metaverse Analytics
https://grow.google/certificates/data-analytics/#?modal_active=none

Agenda
1. Motivation
3. Foundations
6. Summary
7. Outlook
8. References
Page 28

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]
Page 29
https://www.business2community.com/tech-gadgets/how-to-make-sure-your-company-data-i
s-accessible-0427094

Challenges: Computation
● Transforming sensor data to new data types in
tabular format [10]
● Huge data volume => high demand for
computational resources [22]
Page 30
https://towardsdatascience.com/how-to-generate-synthetic-tabular-data-bcde7c28038a
Tabular Data
Hardware Software
Interaction
PHP-
Script
Tabular data

Challenges: Data Accessibility
=> Data access in centralized metaverse limited
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

Agenda
1. Motivation
3. Foundations
6. Summary
7. Outlook
8. References
Page 32

Research Questions
metaverse?
● Which challenges are associated with the quantitative investigation of metaverse and the
application of those methods?
Page 33

Summary
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

Agenda
1. Motivation
3. Foundations
6. Summary
7. Outlook
8. References
Page 35

What we learned
Page 36

Page 37
Questions?

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.
Page 38

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
Page 38

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).
Page 39

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
Page 40

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
Page 41

Motivation: History
Page 42
“Snow Crash” Novel 1992 [7] Meta announcement in 2021 [9]
https://www.youtube.com/watch?v=Uvufun6xer8
“Ready Player One” Novel 2011

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 ?
Page 43

Fundamentals - Possible Question
● What is the difference between virtual reality and
Metaverse?
Page 44

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
Page 45

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
Page 46

Biometric data
Page 47

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
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