CHAINTEL: On-Chain Analytics and Blockchain Intelligence Suite

Short Summary

The On-Chain Analytics and Blockchain Intelligence Suite provides a dedicated environment for ingesting, processing, and analysing large-scale public blockchain data, with a primary focus on Bitcoin and Ethereum networks. Built on NVIDIA RTX 6000 ADA and H100 NVL GPUs combined with high-capacity storage infrastructure, CHAINTEL is configured to handle the volume and complexity of on-chain transaction data, supporting graph-based analytics, machine learning-driven pattern recognition, and temporal analysis of blockchain activity. Unlike general-purpose data analytics platforms, CHAINTEL is specifically oriented toward the structural and analytical characteristics of distributed ledger data, including UTXO and account-based transaction models, address clustering, and network topology analysis. The platform supports research and applied use cases spanning transaction flow analysis, entity identification, market behaviour modelling, and the study of emergent network phenomena on public chains.

Keywords: Blockchain Analytics; On-Chain Data; Graph Analysis; Transaction Networks; Machine Learning

Deeptech Area

Artificial Intelligence
Other

Hosting Institution and PI Info

Name of Host Organization	NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa
Department or Lab	MagIC (Information Management Research Center) - the NOVA IMS research and development center
Name of Building	Manuel Vilares Building
Physical Address	Campus de Campolide, 1070-312 Lisboa
Website Links	https://www.novaims.unl.pt/
Institutional contact name	Cristina Oliveira
Institutional contact email	magic@novaims.unl.pt
Principal Investigator Name	Professor Ian James Scott
Position / institutional role	Assistant Professor
ORCID	0000-0001-9699-4473
Email	iscott@novaims.unl.pt
TestBed Responsible Name (if different from PI)
Funding source(s) for TestBed’s acquisition	This testbed benefits from the resources of the NOVA Data & Analytics Hub (NOVA DAH), hosted at NOVA Information Management School (NOVA IMS) of Universidade NOVA de Lisboa. The work is supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project UID/04152/2025 (https://doi.org/10.54499/UID/04152/2025) (Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS); by the Plano de Recuperação e Resiliência (PRR) under projects UID/PRR/04152/2025 (https://doi.org/10.54499/UID/PRR/04152/2025) and EQUIPAR+2: UID/PRR2/04152/2025 (https://doi.org/10.54499/UID/PRR2/04152/2025); and by LISBOA2030 under project LISBOA2030-FEDER-01317500.

Application Domain

Manufacturing
Healthcare
Logistics
Agriculture
Maintenance & Inspection

Application Cases

Application case:	Short description:
Bitcoin Transaction Graph Analysis	Construct and analyse large-scale transaction graphs derived from the Bitcoin UTXO model, applying graph analytics and network science methods to study transaction flow patterns, address clustering, and the structural properties of the Bitcoin transaction network over time.
Ethereum On-Chain Behaviour Modelling	Process and analyse Ethereum account-based transaction data to study wallet behaviour, token transfer patterns, and interaction networks between addresses, including the identification of smart contract interactions and decentralised application usage patterns.
Anomaly and Suspicious Pattern Detection	Develop and evaluate machine learning models for detecting anomalous transaction patterns on public blockchains, exploring approaches applicable to the identification of structuring behaviour, mixing services, and other patterns of interest for compliance and research purposes.
Temporal Analysis of Blockchain Network Evolution	Analyse how Bitcoin and Ethereum network topology, transaction volumes, and participant behaviour have evolved over time, supporting longitudinal studies of adoption, market cycles, and the impact of protocol changes on network activity.
MSc and PhD Research in Blockchain Analytics	The platform supports graduate students conducting dissertation and research projects in blockchain data science, providing the storage capacity and compute required to work with full or partial chain datasets in a reproducible academic research environment.

Potencial Stakeholders

Non-academic stakeholders	Industrial partners, SMEs, Startups, Government bodies, Professional associations, Public agencies and municipalities
Academic stakeholders	MSc students, PhD students, Researchers, Visiting researchers, Seconded researchers
Other types of stakeholders	R&I support professionals, R&I infrastructure operators, Innovation intermediaries, Technology transfer actors

Possible TRL and Exploitation Scenarios

TRL application range	4
Internal academic research	Yes
Collaborative research with external academic partners	Yes
Contract research / Proof-of-Concept for industry	Yes
Pilot / DeepTech Deployment in operational environment	No
Training services (courses, workshops, certification)	Yes
Service provision (testing, benchmarking, validation)	Yes
Open access for walk-in users (e.g. open days / hackathons)	No
Other

Formal access conditions and prerequisites

Type of contractual relationship	Academic partner	Industrial partner
No contract (direct access)	No	No
Direct contract between parties (e.g., research agreement)	Yes (See Note 1)	Yes (See Note 1)
Indirect contract between parties (e.g., project framework)	Yes (See Note 1)	Yes (See Note 1)
Other (Secondments / sponsored access for visiting researchers under project-based or institutionally approved arrangements)	Yes (See Note 1)	Yes (See Note 1)

Note 1: All access is subject to terms and conditions.

Type of prerequisites	Description of prerequisites
Agreements	Confidentiality agreement for proprietary algorithms	In some cases (See Note 2)
	Data sharing agreement for datasets generated	In some cases (See Note 2)
	IP agreements	In some cases (See Note 2)
	Other	In some cases (See Note 3 and 4)
Insurance	Users must have appropriate liability coverage through their home institution	Yes

Note 2: Intellectual property, confidentiality, and exploitation conditions are governed by the applicable NOVA regulations, the CITADELS consortium framework, and any project- or service-specific agreements. Background IP remains with the original rightsholders. Foreground generated through collaborative or service activities will be managed according to the applicable contractual framework, including provisions on ownership, access rights, confidentiality, dissemination, and exploitation. Additional NDAs, data-processing agreements, or specific IP clauses may apply depending on the nature of the data, software, models, or other assets involved.

Note 3: Access is granted on a project-based or institutionally approved basis, subject to feasibility assessment, resource availability, compliance with data protection and security requirements, and acceptance of the applicable terms and conditions. Special arrangements may apply for CITADELS secondments and other approved visiting researcher schemes. Where sensitive, proprietary, or regulated assets are involved, additional safeguards may be required before access is enabled.

Note 4: Gurobi licenses can only be used for academic applications.

Training and Safety

Mandatory technical training	N/A
Recommended technical training	Recommended training on cluster operation, job submission, queuing in SLURM.
Mandatory safety requirements	No physical safety requirements apply. Users must follow institutional cybersecurity, data protection, GDPR and responsible AI rules where relevant.

Technical Components for the Testbed

Components:

Description:

Hardware

(physical equipment available in this TestBed)

1) NOVA DAH01 System Specifications:

a) CPU: 32-Core CPU - This processor provides a significant amount of processing power, enabling users to run multiple demanding tasks simultaneously, such as simulations, data processing, and other compute-intensive workloads.

b) GPU: 2 x Nvidia RTX 6000 ADA - These high-performance GPUs are designed to accelerate AI, HPC, and other GPU-accelerated workloads. With two RTX 6000 ADA GPUs, users can leverage massive parallel processing capabilities, handling large amounts of data and providing a substantial boost to performance.

c) Storage: 7TB NVMe Storage - This high-capacity storage solution provides rapid data access and transfer speeds, ideal for applications that require high-performance storage, such as data analytics, scientific simulations.

2) NOVA DAH02 System Specifications:

a) CPU: 112 CPU cores, providing a substantial amount of processing power for compute-intensive tasks. This will enable users to run multiple simulations, data processing, and other tasks concurrently.

b) GPU: 2 x Nvidia H100 NVL (Next-Generation High-Performance Computing) GPUs, which offer significant performance boosts for AI, HPC, and other GPU-accelerated workloads. The H100 NVL GPUs are designed to handle massive amounts of data and provide high-performance computing capabilities.

3) NOVA DAH WS includes:

a) 16 Lenovo Thinkstations P5 units, each equipped with an Intel(R) Xeon(R) W3-2423 processor, 32 GB DDRS-4800 MHz ECC memory, NVIDIA RTX(R) 2000 GPU with 16 GB GDDR6 (Ada Generation), and 1 TB PCIe Neg4 SSD.

b) Operating system Windows 11 Education,

c) Broad range of licensed and open-source software for data science, analytics, modelling, and visualisation, including but not limited to a broad range of licensed and open-source software for data science, analytics, modelling, and visualization, including but not limited to Python, R, Power BI, Tableau, SPSS, SAS, QGIS, ArcGIS, Docker, Anaconda, Visual Studio Code, and Zotero.

c) Storage: 500 TB of storage, providing ample space for storing large datasets, applications, and other data. This storage capacity will enable users to work with big data and store the results of their computations.

4) Others

Software

(needed to run
the TestBed)

1) SSH client

2) File transfer tools recommended

3) Apptainer runtime to test locally

Standards and regulations
(relevant for the safe and compliant operation of this TestBed)

N/A

Testbed Documentation

Type	Short description:
Tutorial	How to connect to NOVA DAH (Tutorial NOVA DAH01)
Tutorial	How to connect to NOVA DAH (Tutorial NOVA DAH02)

Access to these tutorials for external users is subject to a signed Non-Disclosure Agreement (NDA).

Ethical and Societal Aspects

Ethical and societal aspect:	Short description:
Privacy on Public Blockchains	Although Bitcoin and Ethereum transaction data is publicly available, the application of analytics techniques to de-anonymise addresses or link on-chain activity to real-world identities raises significant privacy considerations. Researchers using CHAINTEL are expected to reflect on the proportionality of their methods and comply with applicable data protection frameworks, including GDPR, where derived data may constitute personal data under EU law.
Supporting Financial Integrity and AML Research	Blockchain analytics plays an important role in supporting anti-money laundering (AML) and counter-terrorism financing (CTF) research, helping to develop tools and methods that can assist regulators, financial institutions, and law enforcement in understanding illicit financial flows. CHAINTEL provides a research environment to develop and validate such methods in an academic context.
Regulatory Alignment with MiCA and EU Digital Finance Policy	Research conducted on CHAINTEL is relevant to the evolving EU regulatory landscape for crypto-assets, including the Markets in Crypto-Assets Regulation (MiCA) and the EU Digital Finance Strategy. The testbed supports the development of analytical capabilities that can inform regulatory compliance, market surveillance, and policy design in the digital asset space.
Responsible Disclosure and Dual-Use Awareness	Blockchain analytics methods developed on the testbed may have dual-use potential, with legitimate research applications alongside possible misuse for surveillance or targeted financial profiling. Researchers are expected to consider responsible disclosure practices and the broader societal implications of their methods, particularly where outputs could affect individual privacy or be applied outside an academic context.
Transparency and Reproducibility in Blockchain Research	The open and immutable nature of public blockchain data provides a strong foundation for reproducible research. CHAINTEL encourages researchers to document their data extraction, processing, and analytical pipelines clearly, supporting open science principles and enabling independent verification of findings in a domain where methodological transparency is especially important.

Funding Source

This testbed benefits from the resources of the NOVA Data & Analytics Hub (NOVA DAH), hosted at NOVA Information Management School (NOVA IMS) of Universidade NOVA de Lisboa. The work is supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under project UID/04152/2025 (https://doi.org/10.54499/UID/04152/2025) (Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS); by the Plano de Recuperação e Resiliência (PRR) under projects UID/PRR/04152/2025 (https://doi.org/10.54499/UID/PRR/04152/2025) and EQUIPAR+2: UID/PRR2/04152/2025 (https://doi.org/10.54499/UID/PRR2/04152/2025); and by LISBOA2030 under project LISBOA2030-FEDER-01317500.

More info

(TBD)