=Paper=
{{Paper
|id=Vol-3741/paper86
|storemode=property
|title=Identifying key factors in designing data spaces for Urban Digital Twin Platforms: a data driven approach
|pdfUrl=https://ceur-ws.org/Vol-3741/paper86.pdf
|volume=Vol-3741
|authors=Cristian Martella,Angelo Martella,Amro Issam Hamed Attia Ramadan,Antonella Longo
|dblpUrl=https://dblp.org/rec/conf/sebd/MartellaMRL24
}}
==Identifying key factors in designing data spaces for Urban Digital Twin Platforms: a data driven approach==
https://ceur-ws.org/Vol-3741/paper86.pdf
[DISCUSSION PAPER] Identifying key factors in designing
data spaces for Urban Digital Twin Platforms: a data
driven approach⋆
Cristian Martella1,∗,† , Angelo Martella1,† , Amro Issam Hamed Attia Ramadan1,† and
Antonella Longo1
1
University of Salento, Lecce, Italy
Abstract
This study explores the design of data spaces for Urban Digital Twin Platforms using a data-driven approach. It
interviews professionals from academia, urban design, engineering, research, software development, and other
fields to identify 12 key factors organized in 4 categories: Data Support, Data Interoperability, Data Sovereignty,
and Data Economy. The research aligns with European data space pillars and aims to guide future developments.
The results emphasize the importance of managing diverse data formats for platform success and suggest that
data interoperability and sovereignty will become more crucial as technologies mature. Future research should
validate and expand on these characteristics to meet evolving data space requirements in smart cities.
Keywords
Urban Digital Twin, Data space, Urban data space, Data Interoperability, Data Sovereignty
1. Introduction
The rise of digital technology in smart cities is transforming hybrid spaces, requiring innovative data
management approaches. Cities are evolving towards complex cyber-physical models, impacting digital
systems and data supporting processes. Planning and designing public urban services are crucial for
modeling and implementing the city’s Digital Twin (DT), which is a crucial aspect of the integration
of digital technologies in urban environments. Urban Digital Twins (UDTs) are being developed using
various market solutions, but data management remains a significant challenge. Data spaces [1, 2],
a common solution, offer efficient data sharing and exchange between different instances. These
spaces aim to encompass the city-level to individual-level data ecosystem, facilitating the creation and
development of UDTs in smart cities. The design and implementation of a data space within an Urban
Data Twin (UDT) platform requires enabling key factors for efficient data sharing, ensuring it supports
urban planning and decision-making capabilities. A survey was conducted to identify relevant factors
for the design of a data space that must support and be integrated with a DT, including Urban DT.
This research paper aims to match key factors proposed in the European context for data space design
with the results of a survey conducted in collaboration with the Digital Twin Cities Centre (DTCC)
research group at Chalmers University. The main European bodies involved in drafting data spaces
development specifications include BDVA [3], IDSA [4], and Fiware [5]. The research focuses on key
factors specialized professionals consider when designing a data space that supports and integrates with
a Data Transport System (DT), including UDT. It is specific to academic, industrial, and research sectors
and focuses on data management aspects within the design process. The study uses the Grounded Theory
Methodology (GTM) to identify relevant characteristics in designing a UDT platform. The resulting
data characteristics are organized into merit tiers, highlighting their influence on the decision-making
SEBD 2024: 32nd Symposium on Advanced Database Systems, June 23-26, 2024, Villasimius, Sardinia, Italy
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open cristian.martella@unisalento.it (C. Martella); angelo.martella@unisalento.it (A. Martella);
amroissam.ramadan@unisalento.it (A. I. H. A. Ramadan); antonella.longo@unisalento.it (A. Longo)
Orcid 0000-0001-9751-9367 (C. Martella); 0000-0002-1082-7293 (A. Martella); 0000-0003-0042-5095 (A. I. H. A. Ramadan);
0000-0002-6902-0160 (A. Longo)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Figure 1: Building blocks of a Data Space [6].
process related to future UDT platform design. Four merit tiers have been identified, with essentials and
supporting characteristics reported to ensure successful creation of UDTs that can natively integrate with
data space instances. Data spaces require governance, which involves participants adhering to business,
operational, and organizational agreements [6]. Business agreements regulate data exchange and legal
constraints, operational agreements cover data spaces’ policies, and organizational agreements ensure
compliance with product specifications (as shown in Figure 1 [6]). This paper uses the technological
pillars considered fundamental by European sector bodies as key concepts for identifying and organizing
questionnaire sections for survey professionals and analyzing results.
The paper is structured into several sections, including background, methodology, results, discussion,
and conclusions. Section 2 discusses the background of the study, whereas section 3 delves into the
methodology used, and the definition of figures involved in the GTM-based survey. Section 4 provides a
detailed description of the survey’s results, while section 5 analyzes the survey results and provides
final considerations. Section 6 presents potential future developments and conclusions.
2. Background and related work
This section introduces the fundamental concepts of data spaces, models, standards, and specifications
in the paper dissertation. Several approaches exist, particularly in the European context. Basic concepts
include DTs [7, 8, 9] and UDTs [10, 11, 12], which are well-documented in literature. A specific contri-
bution is proposed in [13], providing a hypothesis of UDT architecture and a comparative analysis of
license-free UDT platforms. This paper can be considered a sequel to the current research work.
2.1. Data Space
Data space is a technology that enables data management by creating a data ecosystem where stake-
holders can exchange and share data. This seamless data sharing can provide value, especially when
combined with data analytics. In a smart city, public transportation companies and local businesses
can collaborate in a data space, benefiting from greater retail demand forecasts and optimizing traffic
management. This data economy contributes to the creation of a smart city, as the Internet of Things
has already addressed the concept within the context of smart cities [14].
2.2. Data Spaces in Europe
Gaia-X [15] and the International Data Spaces Association (IDSA) [16] are European initiatives focused
on studying models, specifications, and standards for data spaces. Their synergies [17] have sparked
interest in Europe [18, 19] and beyond [20]. Open ecosystems based on standards, like Fiware [21], can
provide building blocks for data platforms, such as data brokering via standardized data models [22].
Data spaces involve data suppliers, consumers, and service providers, allowing them to exchange data
and accommodate a wide range of data sources with varying models. Service providers can use the
Role Professionals
Academics P1, P3, P8, P11
Urban designer P2, P6, P11
Engineers and architects P2, P5, P6, P11, P12
Researcher P2, P4, P5, P6, P12
Software architects P3, P4, P7, P9, P10
CEO P7, P8
CTO P9
Data scientist P13
Software developer P6, P13
Table 1
Professional contacts and their occupation
shared data space for data analytics services, while data consumers can share analytics results. IDSA,
Gaia-X, and Fiware are developing data connectivity building blocks. The Big Data Value Association
(BDVA) is another significant European initiative [2, 23], focusing on data management principles and
techniques, including data life cycle management, usage of data lakes and spaces, underlying data
storage services, and connection to data sharing platforms.
2.3. Data Space and Digital Twin Integration
The literature discusses the integration of Digital Twins (DTs) and data spaces. A prototype [24]
demonstrates that product carbon footprints can be stored in a DT’s data space instance and shared
with participants. Smart cities are shifting from technical to socio-technical perspectives [25], aiming to
involve citizens in urban planning strategy decision-making. However, these DTs face challenges due to
their limited variables and processes. A Living Lab is proposed in [26] to demonstrate the interaction
between UDTs under the UN’s Sustainable Development Goals of sustainable cities, health, well-being,
industry, innovation, and infrastructure. The system processes real-time data using onboard sensing
devices, edge computing paradigms, and Machine Learning algorithms. The authors highlight the
constant conflict between what is real and ideal in urban-scale DTs.
3. Methodology
The study employs an exploratory methodology based on the principles of GTM [27], which involves two
iterative steps: data collection and analysis. Data collection involves in-depth interviews, transcripts,
questionnaires, and recordings to provide a comprehensive observation of a phenomenon. Data analysis
extracts meaningful insights and shapes the theoretical framework of the phenomenon. In [28], the
GTM is used to evaluate existing digital twins (DTs) within different application domains of Cyber-
Physical Systems. The authors in [29] propose an analytical approach that compares DT instances and
universally define and structure digital twins using a feature-based digital twin framework (FDTF). In
this work, the GTM methodology is used to organize qualitative interviews with professionals involved
in multidisciplinary projects related to DTs. The interviews involve professionals from various industries,
including enterprises, startups, engineers, architects, technicians, and universities across Europe, as listed
in Table 1. The questionnaire comprises sections about the degree of knowledge about DT development
platforms and their support evaluation in terms of data management. Some interviews were conducted
in presence, while the major parts were systematically recorded and subjected to Natural Language
Processing-based analysis techniques. Interviews are organized according to the professional’s role, with
professionals with multiple roles participating in each round. Cross-references are made by matching
the results obtained from the survey with models, standards, and specifications currently being studied
at the European level. The methodology develops distinct and homogeneous characteristics that align
with the existing body of literature related to fundamental pillars for DS design.
Academics
Urban Designers
Engineers and Architects
Researchers
Software Architects
CEO
CTO
Data Scientists
Software Developers
Subcategory score
Category score
Input Data 4 3 5 5 5 2 1 1 2 28
Output Data 4 3 5 5 5 2 1 1 2 28
Data Support Object Representation 2 3 3 2 2 1 1 0 1 15 116
Simulation Output Data 4 2 4 5 4 2 1 1 2 25
Metadata Support 4 1 1 4 4 2 1 1 2 20
Standard and Open Data For- 4 1 1 4 4 2 1 1 2 20
Data mats
65
Interoperability Data Provenance and Traceabil- 4 2 3 3 2 2 1 1 0 18
ity
Data Exchange APIs 3 0 0 3 3 0 1 0 2 12
Data Integration 4 1 0 3 5 0 1 0 1 15
Data Trusted Data Exchange 3 0 0 4 4 2 1 0 1 15
37
Sovereignty Control/Mechanisms on Data 3 2 4 3 4 2 1 1 2 22
Access and Usage
Data Economy 4 0 0 4 1 2 0 0 0 11 11
Table 2
Data types characteristics used for the creation of UDT
4. Results
The study identifies 12 key factors for designing data spaces for Urban Digital Twin Platforms based
on professional interviews. These factors are organized into four categories: Data Support, Data
Interoperability, Data Sovereignty, and Data Economy. These categories are based on European data
space pillars (discussed in section 2) and will be further explored in future developments. The Data
Support category includes input and output data, object representation, simulation output data, and
metadata support subcategories. Data Interoperability includes standard and open data formats, data
provenance and traceability, data exchange APIs, and data integration subcategories. Data Sovereignty
includes trusted data exchange and control mechanisms on data access and usage. The study provides
a detailed description of these 12 key factors and proposes further discussion into the four categories
introduced.
4.1. Data Support
The Data Support category comprises key subcategories related to the requirements and features a
UDT platform must offer. These include Input Data, Output Data, Object Representation, Simulation
Output Data, and Metadata Support. Each subcategory is detailed, with a focus on data sources and
sinks, identifying their main characteristics and importance in the context of UDT platforms.
Input Data Urban Digital Twin (UDT) platforms use aerial photography, LIDAR point clouds, and
cadastral data to create accurate 3D models of urban environments [13]. Additional input data, such as
live IoT sensor streams, historical collections of samples, and external web APIs, may also be required.
IoT streams are encoded based on communication protocol requirements, with lightweight data formats
like JSON and Ultralight 2.0 being popular [30, 31]. Historical collections provide timed behavioral
representations and machine learning models for simulation. Popular storage options include tabular
files like CSV and collections of JSON documents [32]. Web APIs are valuable sources of information for
real-time simulations and decision-making [33, 34]. Standard formats are necessary to organize input
data, fostering interoperability between systems [34], allowing policy application, and ensuring trusted
data sharing between participants. This ensures interoperability, shared lingua, and trusted data sharing
in the supporting data space for an UDT [6].
Output Data The assessment of a generic UDT platform involves considering various outputs related
to various contexts, such as the 3D City Model, Solar irradiance, Solar shadow, citizen interaction, future
city development, digital infrastructure for IoT services, and energy demand. These outputs are derived
from commonly used scenarios in research. For example, solar irradiance and solar shadow can be
traced back to simulation and analysis outputs, while the 3D city model and digital infrastructure for
IoT services and energy demand are crucial outputs for planning a smart city [13].
Object Representation and Level of Details Object representation is crucial in choosing a Universal
Design Tool (UDT) platform as it affects the accuracy and complexity of displayed models, but also re-
quires higher rendering performance. The Level of Detail (LOD) metric helps identify the representation
complexity of building geometries, from footprints to complex 3D geometries and textures. Other UDT
platform characteristics include natural elements representation, building components representation,
and representation of city infrastructures and service networks on surface and underground [13].
Simulation Output Data An UDT platform uses simulation output data to integrate tools and display
phenomena using dashboards and interactive visualizations. It uses high-performance computing
and AI-driven predictive tools to analyze what-if scenarios. The platform offers various solutions for
analyzing geotechnical behavior, urban development, solar shadowing, sea level rise effects, weather
conditions, air quality, and noise models, enabling accurate analyses based on what-if scenarios [13].
Metadata Support Digital twins rely on consistent data and information [35], and metadata can
enhance the quality of data and simplify interoperability between systems [36]. Attribute metadata can
provide a uniform shared context for data exchanges, reducing data mismatch and fostering accurate
analyses. Attribute metadata integration can play a key role in selecting the most suitable UDT platform,
enabling effective system integration and collaboration using a common lingua [34] (more on it in
section 4.2). Additionally, metadata can include data structure information like keys, indexes, and
columns, allowing search capabilities and interoperability among open data platforms [37].
4.2. Data Interoperability
In the data economy era, organizations often operate as isolated data silos, referred to by a few specific
software systems or platforms. This presents a challenge in supporting data sharing and exchange. Data
interoperability is a key challenge, and organizations must be prepared to comply with new technologies
like data spaces that aim to break data silos and foster interoperability. To achieve interoperability,
organizations must align their underlying data and data models. Standardization is crucial for achieving
full data interoperability [38], but the heterogeneity of data sources and data is a significant challenge.
Data access and models are key factors for data interoperability, as they must be agreed upon and shared
for efficient and transparent data exchange. Initiatives like IDSA [39] and Gaia-X [40] aim to define
technical specifications of data spaces, enabling concepts of data sovereignty and trust.
Standard and Open Data Formats Standard and open data formats are crucial for data sharing
and exchanging in data spaces [6], as they enable a common lingua definition for seamless integration.
However, the main challenge lies in mechanisms of data sharing/exchange that are independent of
corresponding protocols and data formats. LinkedScales is a multiscale-based data space architecture
that uses a graph-based integration process over a graph database [41], implementing an integration and
enrichment pipeline to incrementally obtain ontology-like data structures from raw data representations.
Linked Open Data provides high-quality retrievals in Exploratory Search Systems (ESSs) [42], while
JSON-LD is a JSON-based format for serializing Linked Data [43], designed to be easily integrated into
deployment environments that already use JSON as the reference data format. JSON-LD is a reference
format for urban data management using emerging technologies [44], while Fiware enhances data
quality [36] through metadata integration. CityGML is an international open standard for represent-
ing, exchanging, and storing 3D city models, supporting various applications like urban planning,
environmental simulation, disaster management, and navigation in UDTs [45].
Data Provenance and Traceability Data provenance refers to the origin and chronology of data,
including its inception, ownership, and utilization. Cryptographic measures are used to ensure data
integrity and security, while data creation and storage procedures establish connections between current
events and their preceding occurrences. This results in transparent and tamper-resistant documentation
of data origins [46]. Data traceability involves monitoring and tracing data provenance throughout its
life cycle, including preservation, processing, and access stages. Blockchain infrastructure can be used
to achieve comprehensive data traceability through meticulous documentation and auditing of all data
stages. This process ensures the reliability and credibility of produced data by capturing and documenting
essential information at every stage of data generation [47]. Integrating data provenance and traceability
within a digital twin framework enhances accountability, transparency, and credibility. Blockchain
technology is used to establish a verified record of asset transactions, ensuring data authenticity and
traceability [48].
Data Exchange APIs The data economy’s limited expansion is due to heterogeneity in data access,
which hinders the implementation of solutions relying on diverse sources. To ensure data interoperability
within data spaces, consensus on technological interfacing and data modeling must be established.
Current initiatives focused on establishing technical soft infrastructure for data spaces do not address data
modeling, as their specifications only focus on metadata exchange rather than actual data. Additionally,
there is a lack of standardization and harmonization in data distribution specifications across various
data-providing platforms. The Next Generation Service Interfaces Linked Data (NGSI-LD) standard [49],
developed by the European Telecommunications Standards Institute (ETSI), offers a comprehensive
specification for managing context data. It enhances the accessibility of contextual information by
establishing Application Programming Interfaces (API) and data models that stakeholders can use within
a given data environment. Fiware offers a suite of open-source components that can be effectively used
in building data platforms, including the Context Broker, which incorporates the NGSI-LD API [50].
The NGSI-LD API is based on an abstract information model centered around entities with various
characteristics, types, properties, and relationships. Several ongoing initiatives are focused on developing
a corpus of NGSI-LD-compatible data models, providing a standardized reference for semantically
modeling data that will be exchanged within future data spaces. One notable program is the Smart
Data Models program [51], which supports semantic interoperability of context information within data
spaces.
Data Integration The rapid growth of data generation, processing, and storage is due to the recogni-
tion of data as a crucial resource for organizations, fostering innovation and value creation [52]. The
integration of information technologies facilitates data utilization for economic purposes [53], and
organizations engage in collaborative efforts within value-oriented socio-technical networks [54, 55]
to share and transfer data [56, 57]. However, the establishment and sustainability of these ecosys-
tems face challenges [57] such as digitization [58], diverse data sources integration [59], external data
incorporation [60], and addressing organizations’ hesitancy to share data [61]. The concept of data
spaces offers a potential solution to these challenges, particularly technical complexities associated with
integrating diverse datasets [1]. Information systems research has shown increasing attention towards
data ecosystems [62, 63, 64, 65], with political bodies like the European Union supporting entities to
promote data sovereignty, innovation, and organizational competitiveness [66]. However, the exact
nature of the connection between data spaces and data ecosystems remains uncertain.
Figure 2: Digital twins connections and hierarchy levels
4.3. Data Sovereignty
Urban digital twins (DTs) are a family of digital assets that range from building assets to smart buildings
and cities. UDT platforms aim to coordinate them in a federated fashion using schemas like the layered
hierarchy proposed in [67] and also discussed in [68] (see Figure 2 [67]). Each DT is designed for
high-performance bidirectional interconnections with multiple data sources, including other digital
twins. However, data security and privacy issues are growing, and data sovereignty addresses these
concerns. To achieve these goals, trusted data exchange paradigms and data access and usage control
mechanisms are implemented. Both are discussed in the following paragraphs.
Trusted Data Exchange Trusted data security measures are crucial for the confidentiality, integrity,
and availability of sensitive data exchanged between nodes in the smart city ecosystem [69]. These
measures include sensitive data handling, data privileges, user activity logging, pseudonymization,
and privacy-aware data interlinking services [70]. In UDTs, the data space layer should manage data
exchanges effectively while providing Fair Access (FAIR) [68] policies and ensuring the authenticity and
trustworthiness of data sources in the UDT’s data space ecosystem [71].
Control Mechanisms on Data Access and Usage Data sovereignty is essential in data spaces, as it
safeguards sensitive data from unauthorized access and malicious exploitation. Verifiable credentials,
based on data ownership and selective disclosure, ensure transparency, provenance, and reliability. This
allows secure data sharing between functional units in smart cities while maintaining control over access
and purpose [71]. Implementing effective data governance and control mechanisms can be challenging,
especially in inter-organizational data-sharing networks [72]. Decentralized access control, which turns
a blockchain into an automated access manager, allows users to control their data without relying on
third parties [73]. Knowledge-control regimes for exchanging samples and sequences can also control
data access and use [74].
4.4. Data Economy
The data economy is the value generated by data collection, storage, and analysis within a data space [6].
International initiatives like Fiware, part of the Gaia-X European Association for Data and Cloud
alliance, the Big Data Value Association (BDVA), and the International Data Spaces Association (IDSA)
aim to accelerate business transformation in the data economy, enabling efficient near real-time data
exchange [75]. Data markets and data exchange services are being addressed to enhance the data
value chain, bridging the gap with traditional data value chains for consumable goods [76]. Emerging
data-driven technologies, such as data spaces, are fostering interest in making data a new economic
value by identifying and assigning new data properties, such as STREAM (Sovereign, Trusted, Reusable,
Exchangeable, Actionable, and Measurable) data properties [76].
5. Discussion
The survey results discussed in section 4 and illustrated in Table 2 have identified four merit-based
ranking tiers for data types characteristics (DT) in a data management solution. The core aspects
(25-28 rank) include essential aspects such as input and output data, fundamental aspects (20-24 rank)
include metadata support, standard and open data formats, and control mechanisms on data access and
usage, relevant aspects (15-19 rank) include data provenance and traceability, data integration, trusted
data exchange, and object representation, and slightly relevant aspects (11-14 rank) include data
exchange APIs and data economy. These tiers are based on the scores achieved in the survey and are
crucial for ensuring the development of DT solutions. The survey’s findings provide valuable insights
into the data management needs of DT developers. The evaluation framework for selecting an UDT
platform focuses on data management subcategories. Data Support is the most important factor, with
four out of six merit tiers belonging to this category. Data Interoperability and Data Sovereignty are
considered on average significant, while the Data Economy category appears to be the least relevant.
The analysis considers the requirements and maturity of reference technologies for UDT development
platforms, such as managing and generating a wide range of data formats, supporting adequate data
exchange and sharing mechanisms, and supporting the data value chain. The lack of precise protocols
and standards in data spaces reduces the need for guaranteeing aspects related to Data Interoperability
and Data Sovereignty, while the reluctance of companies to share their data and the perception of data
as a source of value reduces the need for support for the Data Economy. The results underline that
Data Support is the most important factor to consider when selecting an UDT development solution.
Despite the limited availability of mature technologies, the Data Interoperability and Data Sovereignty
categories still highlight professionals’ strong perception of their potential relevance.
6. Conclusion
The study analyzed four main characteristics of data used for urban digital twin (UDT) creation and their
11 subcategories. 13 professionals rated each characteristic and subcategory’s impact on choosing a UDT
platform using a GTM methodology. The results showed that platform ability to manage diverse data
formats is crucial for UDT platform success. Data interoperability and sovereignty were less influential,
but their full potential will be unleashed as technologies mature. Future research should validate and
refine identified characteristics, as data spaces and UDTs evolve with professionals’ needs.
Acknowledgements This work is partially supported by ICSC – Centro Nazionale di Ricerca in
High Performance Computing, Big Data and Quantum Computing, funded by European Union –
NextGenerationEU (PNRR-HPC, CUP:C83C22000560007). We are grateful to the Digital Twins Cities
Centre for their invaluable collaboration and support, that made this survey a success.
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