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+# Polkadot Analytics Platform: Stage 2
+
+- **Team Name:** MOBR Systems
+
+- **Payment Address:** 0x10F40DE77B7D2cDEe35De46B521e0c93bA9C3b36 (USDC ERC20)
+
+- **[Level](https://github.com/w3f/Grants-Program/tree/master#level_slider-levels):** 2
+
+## Project Overview :page_facing_up:
+
+
+This proposal is aligned to the [RFP: Data Analysis Tools for Substrate-based Blockchains](https://github.com/w3f/Grants-Program/blob/master/docs/RFPs/Under%20Development/data_analysis_tools.md). 
+
+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](https://github.com/w3f/Grants-Program/pull/1420). 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](https://github.com/w3f/Grants-Program/pull/1420) and the project [A Polkadot Analytics Platform: Stage 1](https://github.com/w3f/Grants-Program/pull/1883) 
+
+
+### Overview
+
+**Tagline:** 
+
+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. 
+In its current stage, the platform is composed of a knowledge base (KB) and its initial representation with the POnto ontology [4]. The current grant proposal focuses on populating this KB through the creation of semantic ETL pipelines, i.e. information extraction workflows that will extract, reuse and integrate data from different sources, aligning and structuring domain knowledge in the KB. We will create data extraction mechanisms to gather data from various Polkadot-related sources using Substrate interfaces (e.g. using Substrate-ETL), as well as offchain (e.g., using Github API).
+
+
+[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] Polkadot Analytics Platform source code: https://github.com/mobr-ai/PolkadotAnalytics 
+
+
+**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.
+
+![PAP_overview](https://github.com/rbrandao/Grants-Program/assets/779451/f50051f7-4dbe-474d-96b7-8418cd438e7b)
+
+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) follows a modular and scalable design to accommodate future expansions and improvements. The orange boxes highlight the components that we will develop in the current application.
+
+
+![architecture-v2](https://github.com/rbrandao/Grants-Program/assets/779451/03d15de1-a87a-456a-99ac-bed34c2fcfd6)
+
+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 is key to the proposed data analysis platform. The team has also concluded the first stage of this platform [5], building the foundation of the analytics platform with its initial knowledge base. 
+
+[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
+
+[5] Polkadot Analytics Platform source code: https://github.com/mobr-ai/PolkadotAnalytics 
+
+
+**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 the second stage in the roadmap to build the Polkadot Analytics Platform, which comprises a subset of its components. Specifically: 
+
+- On Milestone 1:
+  
+| Number | Deliverable | Specification |
+| -----: | ----------- | ------------- |
+| **1** | Substrate-ETL extraction scripts | Extraction scripts to gather data from all the SQL tables specified in the Substrate-ETL. |
+| **2** | Polkadot Data extraction scripts | Extraction scripts to gather polkadot related data not available on Substrate-ETL. We will explore APIs like the ones used in the [Polkassembly](https://github.com/polkassembly/polkassembly/tree/main) to extract OpenGov data to answer the queries specified in the RFP |
+| **3** | GitHub Data extraction scripts | Extraction scripts to gather data from GitHub, which will be used to support queries relating to network engagement and innovation. We will use github APIs to extract data from the repositories related to the Polkadot Ecosystem, aiming at answering questions regarding dev-community engagement. E.g.: which parachain had more PR in a specific time interval. |
+
+- On Milestone 2:
+
+| Number | Deliverable | Specification |
+| -----: | ----------- | ------------- |
+| **1** | Concepts and data sources mapping | Extension of POnto, mapping relevant entities and corresponding datasources. This mapping will be used later on to automatize the proposed Semantic ETL Pipelines to continuously sync the KB with the latest ecosystem state. |
+| **2.** | Ontology Alignment | Scripts to perform ontology alignment with the extracted data as entities in the POnto ontology. |
+| **3.** | Knowledge Injection | Scripts to inject knowledge aligned with the POnto ontology. |
+| **4.** | ETL pipelines | The workflows to extract, transform and load will be implemented using Apache Airflow, and will handle data |
+
+
+The next stages in our roadmap, which are not in the scope of this application grant are:
+
+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](https://github.com/w3f/Grants-Program/pull/1716/), [PR 1768](https://github.com/w3f/Grants-Program/pull/1768/), [PR 1837](https://github.com/w3f/Grants-Program/pull/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 :busts_in_silhouette:
+
+### 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 [1, 2, 3, 4] and the upcoming Polkadot Analytics Platform [5]. Their previously funded W3F grants were 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 [6]) 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 [7]) 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. 
+
+[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] Polkadot Analytics Platform source code: https://github.com/mobr-ai/PolkadotAnalytics 
+
+[5] Grant Application - A Knowledge-Oriented Approach to Enhance Integration and Communicability in the Polkadot Ecosystem:  https://github.com/w3f/Grants-Program/pull/1420
+
+[6] Dr. Moreno LinkedIn profile: https://linkedin.com/in/marcio-moreno-phd-598a459a/ 
+
+[7] Dr. Brandao LinkedIn profile: https://linkedin.com/in/rafaelrmb/ 
+
+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:
+
+- https://github.com/mfmoreno
+- https://github.com/rbrandao
+
+
+### Team LinkedIn Profiles (if available)
+
+- https://linkedin.com/in/marcio-moreno-phd-598a459a/
+- https://linkedin.com/in/rafaelrmb/
+
+
+## Development Status :open_book:
+
+**links to improvement proposals or [RFPs](https://github.com/w3f/Grants-Program/tree/master/docs/RFPs) (requests for proposal),**
+
+This proposal is aligned to the [RFP: Data Analysis Tools for Substrate-based Blockchains](https://github.com/w3f/Grants-Program/blob/master/docs/RFPs/Under%20Development/data_analysis_tools.md). 
+
+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](https://github.com/w3f/Grants-Program/pull/1420) and the first stage of the [Polkadot Analytics Platform](https://github.com/w3f/Grants-Program/pull/1883). 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. 
+
+
+**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 addressed all of the mentioned queries in our previous grant (stage 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 :nut_and_bolt:
+
+### Overview
+
+- **Total Estimated Duration:** 3 months
+
+We expect to conclude the proposed project in 3 months
+
+- **Full-Time Equivalent (FTE):** 0.91 FTE
+
+- **Total Costs:** 30,000 USD
+
+The total budget requested is US$ 30,000 for two PhDs dedicating about 30 days of work each during a 3 months project. Considering the average PhD **daily** rate of US$500 to design and develop a project.
+
+Considering 22 working days on average per month, in three months we have approximately 3 * 22 days = 66 workdays. Each PhD working 30 days gives a total of 60 worked days. The FTE then is 60 worked days / 66 workdays, which is approximately 0.91 FTE.
+
+
+### Milestone 1 — Data extraction mechanisms
+
+The main goal of this milestone is to structure and develop mechanisms to gather raw data from the Polkadot Ecosystem, which is key to creating the individuals in the knowledge base in milestone 2.
+
+- **Estimated Duration:** 1 month
+- **FTE:**  0.91
+- **Costs:** 10,000 USD
+
+| Number | Deliverable | Specification |
+| -----: | ----------- | ------------- |
+| **0a.** | Copyright and Licenses | Apache 2.0 |
+| **0b.** | Documentation/Tutorial | Documentation of the Python code using pdoc3. The tutorial will be a jupyter notebook explaining the use of endpoints and the extraction mechanisms |
+| **0c.** | Testing and Testing Guide | Tests and a guide on how to verify the correctness of data extraction mechanisms. |
+| **0d.** | Docker | Docker images for the data extraction mechanisms. |
+| **1** | Substrate-ETL extraction scripts | Extraction scripts to gather data from all the SQL tables specified in the Substrate-ETL. |
+| **2** | Polkadot Data extraction scripts | Extraction scripts to gather polkadot related data not available on Substrate-ETL. We will explore APIs like the ones used in the [Polkassembly](https://github.com/polkassembly/polkassembly/tree/main) to extract OpenGov data to answer the queries specified in the RFP |
+| **3** | GitHub Data extraction scripts | Extraction scripts to gather data from GitHub, which will be used to support queries relating to network engagement and innovation. We will use github APIs to extract data from the repositories related to the Polkadot Ecosystem, aiming at answering questions regarding dev-community engagement. E.g.: which parachain had more PR in a specific time interval. |
+
+
+### Milestone 2 — Semantic ETL pipelines
+
+The primary objective of this milestone is to structure extracted data and develop processing workflows to dynamically inject and maintain a knowledge base with a formal representation of the extracted data aligned with the POnto ontology.
+
+- **Estimated Duration:** 2 months
+- **FTE:**  0.91
+- **Costs:** 20,000 USD
+
+| Number | Deliverable | Specification |
+| -----: | ----------- | ------------- |
+| **0a.** | Copyright and Licenses | Apache 2.0 |
+| **0b.** | Documentation/Tutorial | Documentation of the Python code using pdoc3. The tutorial will be a jupyter notebook explaining the use of the implemented semantic ETL workflows. In addition, this tutorial will demo how the results of this grant will be used in the future. |
+| **0c.** | Testing and Testing Guide | Testing the information ETL process. A guide on how to verify the correctness of injected knowledge. |
+| **0d.** | Docker | Docker images for the ETL workflows. |
+| **0e.** | Article | We will write a medium post with an overview of semantic ETL workflows and their tasks. |
+| **1** | Concepts and data sources mapping | Extension of POnto, mapping relevant entities and corresponding datasources. This mapping will be used later on to automatize the proposed Semantic ETL Pipelines to continuously sync the KB with the latest ecosystem state. |
+| **2.** | Ontology Alignment | Scripts to perform ontology alignment with the extracted data as entities in the POnto ontology. |
+| **3.** | Knowledge Injection | Scripts to inject knowledge aligned with the POnto ontology. |
+| **4.** | ETL pipelines | The workflows to extract, transform and load will be implemented using Apache Airflow, and will handle data |
+
+## Future Plans
+
+The next stages in our roadmap, which are not in the scope of this application grant are:
+
+**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.
+
+- Current Estimated Duration: 2 months
+
+- FTE: 0.91
+
+- Costs: 20,000 USD
+
+**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.
+
+- Current Estimated Duration: 6 weeks
+
+- FTE: 0.91
+
+- Costs: 15,000 USD
+
+
+**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.
+
+- Current Estimated Duration: 2 months
+
+- FTE: 0.91
+
+- Costs: 20,000 USD
+
+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.
+
+## Additional Information :heavy_plus_sign:
+
+**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. The team has also concluded the first stage of this platform [5], building the foundation of the analytics platform with its initial knowledge base. 
+
+In addition, we have WiP assets being developed in our recently founded [startup](https://mobr.ai).
+
+We also previously developed a technology that became widely adopted within IBM Research [6][7]. It is a toolset to deal with knowledge engineering and ontology construction. 
+
+
+[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
+
+[5] https://github.com/w3f/Grants-Program/pull/1883
+
+[6] https://github.com/ibm-hyperknowledge 
+
+[7] https://ibm-hyperknowledge.github.io/possibility-link-demo-iswc2022/ 
+
+**Previous grants you may have applied for.**
+
+https://github.com/w3f/Grants-Program/pull/1420
+https://github.com/w3f/Grants-Program/pull/1883