Polkadot Analytics Platform: Stage 1

Team Name: MOBR Systems
Payment Address: 0x10F40DE77B7D2cDEe35De46B521e0c93bA9C3b36 (USDC ERC20)
Level: 1

Project Overview 📄

This proposal is aligned to the RFP: Data Analysis Tools for Substrate-based Blockchains.

Our R&D roadmap was conceived and kick-started before the creation of this RFP, which was presented to us during the course of our previous research grant. Nevertheless, we believe the proposed application (Polkadot Analytics Platform) is aligned with the RFP's requirements and main goals. Both aim at providing data analysis in a way that advanced non-technical users can consume, without requiring programming skills.

This is a follow-up grant application for the project A Knowledge-Oriented Approach to Enhance Integration and Communicability in the Polkadot Ecosystem

Overview

tag line Empowering the Polkadot community with a comprehensive analytics platform that provides natural language querying, supported by formal knowledge representations, and customizable dashboards for holistic data analyses.

A brief description of your project. The Polkadot Analytics Platform aims at building a comprehensive data analysis and visualization tool for the Polkadot ecosystem. The platform will allow users to retrieve and analyze data from various Polkadot-related sources (e.g., different parachains and components such as browser wallets), aligned with the POnto ontology [1, 2, 3]. Users will be able to specify their queries using a controlled natural language (CNL), and the platform will provide a query engine to process these queries. Additionally, the platform will provide a UI to support constructing queries and visualizing informative artifacts that represent query results. As well as support for composing customizable dashboards using these artifacts.

An indication of how your project relates to / integrates into Substrate / Polkadot / Kusama. The Polkadot Analytics Platform will use the Substrate blockchain framework and Polkadot ecosystem components to extract, store, structure and process data in order to present it in a user-friendly manner. It will leverage the POnto ontology to enable efficient knowledge organization and retrieval. A CNL querying engine will facilitate natural language queries on the ecosystem's data, making it accessible to a broader target audience.

An indication of why your team is interested in creating this project. Our team is committed to research and develop Web3 technologies, supporting the growth of the Polkadot community. By building the Polkadot Analytics Platform, we aim to provide valuable insights, enhance user experience, and enable seamless data exploration for developers, users, and stakeholders. We are enthusiastic about creating user-centric solutions and pushing the boundaries of knowledge-oriented approaches in the blockchain space.

Project Details

Mockups/designs of any UI components

Figure 1 presents an overview of how the Polkadot Analytics Platform main components are integrated with its UI and how users can interact with it.

The user interaction begins with building a query in a controlled natural language format in the platform. For example, the user may enter a query like "How many transactions happened between July 4th and 8th in the Moonbeam parachain?".

The platform utilizes NLP mechanisms to parse the query and extract key entities, relationships, and actions mentioned. This step involves understanding the user's intent and mapping it to the corresponding concepts in the POnto ontology. This mapping helps establish a semantic understanding of the user query and allows for effective retrieval.

The results are structured as informative artifacts, enriched with charts to enhance the user's understanding. The summarized query results are presented to the user through a dashboard UI where they can analyze the queried data, refine their queries, and further explore the insights provided.

Users have the opportunity to provide feedback or refine their queries based on the presented results. The platform may incorporate user feedback to the knowledge base, enhancing future query processing and improving the system's performance and relevance.

The platform comprises information extraction processes capable of constructing the knowledge base with Polkadot’s ecosystem data, aligned with the POnto ontology. The platform architecture and its components are detailed in the next sections.

Figure 1 - Mockup of how the Polkadot Analytics Platform main components are integrated with its UI

Data models / API specifications of the core functionality Data model will reflect the POnto structure. Extracted data will be transformed and injected in a triplestore database.

The API specifications will provide a standardized way for users to interact with the Polkadot Analytics Platform, allowing them to query specific data, create visualizations, and manage their dashboards effectively. The API will be designed to interact with the knowledge base (i.e. underlying triplestore database), allowing users to access and query the data based on the POnto ontology. The endpoints will provide information about ontology entities (e.g., blocks, extrinsics, etc.), querying support, dashboard setup, informative artifacts, and others.

An overview of the technology stack to be used Data processing: Python and Apache Airflow Triplestore database: Apache Jena and Fuseki Knowledge representation and querying: OWL, RDF and SPARQL Information extraction, NLP, and other backend pipelines: Python, NLTK UI and client-side integration: Node.js, JavaScript, and Angular/React Interaction with relay chains and parachains: Substrate interfaces like Polkadot{.js}

Documentation of core components, protocols, architecture, etc. to be deployed

The platform's architecture (see Figure 2) will follow a modular and scalable design to accommodate future expansions and improvements.

Figure 2 - Polkadot Analytics Platform architecture.

The architecture includes the following core components:

==== Client layer

API: This component structures an API for external applications to interact with the platform.

Frontend: This component offers a user-friendly interface for users to create and execute queries, visualize data, and manage their dashboards.

==== Engine layer

Controlled Natural Language (CNL) Engine: This component will define the controlled natural language grammar and syntax that users can use to query the data based on the POnto ontology. CNL Engine parses natural language queries and assists query completion.

Query Engine: The query engine uses the CNL engine to get a structured query to retrieve the corresponding data from the triplestore database.

Informative Artifacts Engine: Informative artifacts will be created based on query results or on query specification, representing visualizations such as strings, tables, line charts, bar graphs, or other data visualizations.

Dashboard Engine: This component will allow users to create, customize, and manage their dashboards by integrating and composing multiple informative artifacts.

==== Data layer

Data Extraction: This component foresees data extraction workflows to deal with the dynamicity of various data sources within the ecosystem. The component will be responsible for extracting data from the Polkadot ecosystem, according to the entities defined in the POnto ontology, including blocks, transactions, and other relevant information.

Knowledge Management: This component comprises the mechanisms to build and maintain the knowledge representation according to the data extracted and aligned with the POnto ontology, ensuring structured representation and organization. It also comprises the triplestore database where the knowledge representation will be stored.

==== Ecosystem layer

The Polkadot ecosystem has in its core a decentralized network of interconnected blockchains (aka Parachains), and it consists of multiple components. Each component generates data related to blocks, transactions, events, and other relevant information. The proposed platform will interact with the available tools, libraries and SDK to access and manage ecosystem data.

PoC/MVP or other relevant prior work or research on the topic Prior to this application, the team has conducted scientific research to create an ontology [1, 2, 3, 4] that represents the main concepts and relationships of the Polkadot Ecosystem. This ontology will support the building of the proposed data analysis platform.

[1] POnto source code: https://github.com/mobr-ai/POnto [2] POnto documentation: https://www.mobr.ai/ponto [3] POnto scientific paper: https://github.com/mobr-ai/POnto/raw/main/deliverables/milestone3/article.pdf [4] Grant Application - A Knowledge-Oriented Approach to Enhance Integration and Communicability in the Polkadot Ecosystem: https://github.com/w3f/Grants-Program/pull/1420

What your project is not or will not provide or implement This project is not focused on tokenomics, marketing, or business-oriented activities. It will not involve creating tokens or launching a separate business venture.

This is a place for you to manage expectations and to clarify any limitations that might not be obvious This is only the first stage in the roadmap to build the platform, which comprises a subset of the platform components. Specifically:

| 1. | POnto Evolution | New version of POnto ontology, addressing aspects from RFP and Substrate-ETL. | | 2. | KB Bootstrap | Python code with KB endpoints that interact with Jena triplestore database through fuseki-server. Implementation of an initial API to handle knowledge representation. Along with, scripts to inject the knowledge representation required to bootstrap the KB. |

The next stages in our roadmap, which are not in the scope of this application grant are: Stage 2: Semantic ETL workflows The primary objective of this stage is to structure and develop processing workflows to gather raw data from the Polkadot Ecosystem. The knowledge base will dynamically maintain a formal representation of extracted data aligned with the POnto ontology.

Stage 3: Controlled Natural Language (CNL) Engine In this stage, the roadmap foresees a controlled natural language (CNL) specification to leverage the POnto ontology. CNL is a simplified form of natural language designed to be easily understood by both humans and machines. The CNL specification will allow users to interact with the Polkadot Analytics Platform using natural language queries, making the platform more user-friendly and accessible.

Stage 4: Query Engine This stage will focus on the development of a query engine to support the execution of CNL-based queries on the knowledge base. The query engine will be responsible for processing user queries, retrieving relevant data from the knowledge base, and providing users with query results.

Stage 5: Visualization The final stage focuses on delivering a UI where users will be able to integrate multiple informative artifacts to compose their custom dashboards, facilitating personalized data exploration and analysis.

The Polkadot Analytics Platform will be an analytics tool. While it will provide valuable data insights, it is not designed to be used as a trading tool and will not offer financial advice or recommend investment strategies.

Ecosystem Fit

Where and how does your project fit into the ecosystem? The Polkadot Analytics Platform will fill a critical gap in the ecosystem by providing users with a comprehensive analytics solution. It integrates with Substrate to access blockchain data and aligns it with the POnto ontology. The CNL querying engine enhances user experience by enabling natural language-based queries. The provision of customizable dashboards and informative visualization artifacts enhances data communication, making it easier for users to track key metrics, monitor trends, and construe valuable insights.

Who is your target audience (parachain/dapp/wallet/UI developers, designers, your own user base, some dapp's userbase, yourself)? The proposed platform has a broad target audience, including developers, designers, analysts, researchers, and users within the Polkadot ecosystem. We aim to support both technical and non-technical users, providing easy access to meaningful data insights.

What need(s) does your project meet? The Polkadot Analytics Platform addresses the need for comprehensive and accessible data within the ecosystem. It enhances data integration, querying, and visualization, enabling users to gain deeper insights into the Polkadot ecosystem.

Are there any other projects similar to yours in the Substrate / Polkadot / Kusama ecosystem?

To the best of our knowledge, there are no projects in the ecosystem that provide a holistic and human-centered approach to ease data access and analysis similar to our proposed platform. We are aware of three projects tackling the RFP referred to in this proposal (PR 1716, PR 1768, PR 1837). However, none of them combines in a broad knowledge-oriented approach, a CNL querying engine, and composable informative artifacts as an integrated analytics platform for Polkadot.

If so, how is your project different? The Polkadot Analytics Platform stands out by integrating knowledge-oriented approaches, formal ontology, and natural language querying to provide an intuitive and efficient analytics experience. Its composable informative artifacts enable users to create customized dashboards tailored to their specific analysis needs. The proposed platform enables both technical and non-technical users to access data and perform deep analysis.

Team 👥

Team members

Name of team leader Dr. Marcio Moreno, marcio@mobr.ai, Co-Founder, Research Scientist and CEO at MOBR Systems (https://www.mobr.ai).

Names of team members Dr. Rafael Brandao, rafael@mobr.ai, Co-Founder, Research Scientist and COO at MOBR Systems (https://www.mobr.ai).

Contact

Contact Name: Rafael Brandao
Contact Email: rafael@mobr.ai
Website: https://www.mobr.ai

Legal Structure

Registered Address: Av Paulista, 1106 Sala 01 / ANDAR 16 - Bairro: Bela Vista, Sao Paulo - SP CEP 01310914
Registered Legal Entity: MOBR SYSTEMS LTDA

Team's experience

The applicants were co-authors of the POnto ontology previously funded by the W3F [1, 2, 3]. The previous research grant [4] was delivered by MOBR, their co-founded tech startup. Before founding their company, both applicants worked as Research Scientists at IBM for 7 years. Earlier, Dr. Moreno (LinkedIn on [5]) was a postdoctoral researcher at CWI (Centrum Wiskunde & Informatica) in the Netherlands, and worked at the Pontifical Catholic University of Rio (PUC-Rio) in Brazil. Dr. Brandão (LinkedIn on [6]) worked at the TecGraf Institute, from PUC-Rio, during his doctoral studies. The team has published multiple research papers and patents, their background includes Web3, AI, Knowledge Engineering, Distributed and Decentralized Systems, Multimedia and Hypermedia Systems, Human-Centered Computing, among other research topics.

For a complete list of peer-reviewed published papers and granted patents, please visit the following google scholar links

Marcio Moreno, PhD: https://scholar.google.com/citations?user=PfdmrPUAAAAJ
Rafael Brandao, PhD: https://scholar.google.com/citations?user=3ta0InEAAAAJ

Team Code Repos

MOBR Systems:

https://github.com/mobr-ai

Our personal repos:

Team LinkedIn Profiles (if available)

Development Status 📖

links to improvement proposals or RFPs (requests for proposal),

This proposal is aligned to the RFP: Data Analysis Tools for Substrate-based Blockchains.

academic publications relevant to the problem,

https://github.com/mobr-ai/POnto/raw/main/deliverables/milestone3/article.pdf

references to conversations you might have had related to this project with anyone from the Web3 Foundation,

Medium publications about the Polkadot Analytics Platform initiative: https://medium.com/@mobrsys/unlocking-the-future-of-distributed-ledger-data-analysis-a-step-forward-with-polkadot-4ba21e56bc34

We had the following conversations:

Feedback from diogo@web3.foundation during his evaluation [1] led us to the Polkadot forum, where we found interesting discussions [2,3] regarding analytic tools and similar desired features. Feedback from sebastian@web3.foundation regarding our competency questions and the SQL queries already existent in the Substrate-ETL project. We will investigate these queries, to address as many as possible as defined in a deliverable in Milestone 1. As part of our roadmap in our previous research grant, we had a collab session with participants from the W3F grants team. We created a set of activities in Mural, engaging with domain experts to inspire a discussion around POnto modeling, as well as to gather their view around a prospective data analysis tool and its requirements.

[1] https://github.com/w3f/Grant-Milestone-Delivery/blob/master/evaluations/MOBR-Knowledge-Oriented-Framework_2_dsm-w3f.md [2] https://forum.polkadot.network/t/dune-analytics-style-data-service-for-polkadot-kusama/271 [3] https://forum.polkadot.network/t/select-from-polkadot/2593/7 [4] https://github.com/w3f/Grant-Milestone-Delivery/pull/931

Development Roadmap 🔩

Overview

Total Estimated Duration: 1 month

We expect to conclude the proposed project in 1 month

Full-Time Equivalent (FTE): 0.85 FTE
Total Costs: 10,000 USD

The total budget requested is US $10000 for two PhDs dedicating about 10 days of work each during a 1 month project. Considering the average PhD **daily** rate of US$ 500 to design and develop a project.

Milestone 1 — Knowledge-Base bootstrap

In this milestone, the focus is on constructing the foundation of the analytics platform by creating a knowledge base. The goal is to define and implement the base components for the dev environment that will be the basis for the proposed platform. This includes evolving with POnto to address queries from the Data Analysis Tools for Substrate-based Blockchains RFP and the Substrate-ETL project. An initial KB component (including triplestore database and basic API endpoint) will be developed to handle operations over POnto entities.

Estimated Duration: 1 month
FTE: 0,85
Costs: 10,000 USD

Number	Deliverable	Specification
0a.	Copyright and Licenses	Apache 2.0
0b.	Documentation/Tutorial	Documentation of the new version of the POnto Ontology, as well as the KB API.
0c.	Testing and Testing Guide	Testing the KB API. A guide on how to query the KB.
0d.	Docker	Docker image for the KB Bootstrap.
0e.	Article	Medium post about the proposed platform roadmap and what we achieved so far.
1.	POnto Evolution	New version of POnto ontology, addressing aspects from RFP and Substrate-ETL.
2.	KB Bootstrap	Python code with KB endpoints that interact with Jena triplestore database through fuseki-server. Implementation of an initial API to handle knowledge representation. Along with, scripts to inject the knowledge representation required to bootstrap the KB.

Future Plans

how you intend to use, enhance, promote and support your project in the short term, and

Additional Information ➕

Work you have already done.

Prior to this application, the team has conducted scientific research to create an ontology [1, 2, 3, 4] that represents the main concepts and relationships of the Polkadot Ecosystem. This ontology will support the building of the proposed data analysis platform.

In addition, we have WiP assets being developed in our recently founded startup.

We also previously developed a technology that became widely adopted within IBM Research. It is a toolset to deal with knowledge engineering and ontology construction.

Previous grants you may have applied for.

https://github.com/w3f/Grants-Program/pull/1420

Project Overview 📄​

Overview​

Project Details​

Ecosystem Fit​

Team 👥​

Team members​

Contact​

Legal Structure​

Team's experience​

Team Code Repos​

Team LinkedIn Profiles (if available)​

Development Status 📖​

Development Roadmap 🔩​

Overview​

Milestone 1 — Knowledge-Base bootstrap​

Future Plans​

Additional Information ➕​

Project Overview 📄

Overview

Project Details

Ecosystem Fit

Team 👥

Team members

Contact

Legal Structure

Team's experience

Team Code Repos

Team LinkedIn Profiles (if available)

Development Status 📖

Development Roadmap 🔩

Overview

Milestone 1 — Knowledge-Base bootstrap

Future Plans

Additional Information ➕