Proposal - RIAN (Independent Publisher)

independent-publisher-connectors/Querent RIAN/intro.md

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+## RIAN Power Platform Connector
+The Querent’s Real-Time Information Aggregation Network (RIAN) solution transforms vast volumes of data into a Data Fabric - a compact, graph based representation. This connector ingests heterogenous data from data source(s) to enable knowledge discovery by creating linkages between data.
+
+## Publisher: Querent AI LLC
+
+## Prerequisites
+
+- A self-hosted RIAN instance running on a Docker container or a Kubernetes cluster on the user’s infrastructure. 
+- Refer to the [Installation Guide](https://docs.querent.xyz/docs/get-started/installation/) for detailed instructions.  
+
+## Obtaining Credentials  
+  Contact [contact@querent.xyz](mailto:contact@querent.xyz) to request a license key for enterprise production use-cases. For research and development purposes a license key can be obtained from [here](https://www.querent.xyz/rian/).
+
+## Supported Operations  
+
+1. **Configure Data Collection**:  
+   Set up collectors to ingest heterogenous data from various sources such as local folders, email, real-time data streams, and cloud storage.  
+
+2. **Data Handling**:  
+   Process data from different file formats, including txt, json, xml, html, ppt, doc, pptx, and more. Apply data transformation techniques, including normalization, tokenization, and feature extraction, to prepare the data for downstream processing.
+
+3. **Data Fabric Modeling**:
+   Generates a data fabric that models contextually enriched relationships between data entities using attention scores from locally saved transformer models, allowing for advanced natural language understanding and insight discovery.
+
+4. **Data Fabric Traversal for Knowledge Discovery**:  
+   Users can perform graph traversal queries on the data fabric. This includes identifying top connections, exploring second-order relationships, and discovering latent trends within their datasets.
+
+5. **Anomaly Detection and Predictive Analytics**:
+   Use locally saved and trained transformer models to extract attention scores for detecting anomalies and predicting outcomes in time-series or structured data.
+
+6. **Interactive Graph Representations**:
+   Visualize data fabrics with nodes and edges, highlighting connections and contextual relationships for interpretation and validation.
+
+7. **Extensible Integration**:
+   Export data fabrics and insights seamlessly to third-party analytics platforms or integrate with existing workflows. Built-in APIs make it easy to customize operations for domain-specific use cases.
+
+8. **Cross-Domain Applicability**:
+   Applicable to any domain, enabling users to tackle domain-specific challenges while maintaining consistent performance and accuracy.
+
+9. **Data Warehousing, Knowledge Management, and Versioning**:
+   Enable scalable data warehousing, robust knowledge management, and seamless versioning of insights. The connector ensures that processed data and generated insights are stored efficiently, allowing for easy retrieval, comparison, and iterative refinement over time.
+
+## Known Issues and Limitations  
+- No known issues. 
+
+## Frequently Asked Questions  
+- Please refer to the official [faqs documentation](https://docs.querent.xyz/docs/faqs/).