Mirabito CS Pt.2 - Dogukan Atakur; Emre Demir; Mert Vural; Samet Genc;

Week 1 02/14/2025

Attendance: Emre DEMIR, Dogukan ATAKUR, Mert Can VURAL, Samet GENC

Summary:

This week, we focused on implementing middleware for error handling and request-response logging to enhance API robustness. We refined API integration between Conexxus and RLM, ensuring alignment with openapi.json specifications. Additionally, we structured the JSON-to-XML translator for seamless data conversion and prepared responses for the senior meeting, covering Conexxus-RLM differences and the translator's implementation strategy.

Accomplishments:

• Created middleware for global exception handling and request-response logging.
• `FluentValidator` integration in applicatoin layer.
• Proposed an implementation plan for the `JsonToXmlTranslator` to handle Conexxus JSON to RLM XML conversions.
• Implemented schema validation and transformation logic in the translator engine.
• Designed and documented the component diagram for the translator engine.
• Initiated `TranslatorEngineTests.cs` for unit testing.

To-Do:

• Create a SQL database for further testing and data persistence
• Set scope/plan to complete implementation of Json-to-XML translator

Week 2 02/19/2025

Attendance: Emre DEMIR, Dogukan ATAKUR, Mert Can VURAL, Samet GENC

Summary:

This week, we focused on constructing the scope document for a JSON-XML translator that will reside at the presentation layer of our .NET API as dedicated middleware. This translator is designed to bridge our legacy XML system with the new domain objects seamlessly. In parallel, we conducted detailed research on recommendation systems, exploring best practices and evaluating various algorithms and approaches for the training phase.

Accomplishments:

• Conducted in-depth research on recommendation systems, covering collaborative filtering, content-based approaches, and sequential modeling.
• Documented key insights and potential pipeline structures for the recommendation engine.
• Prepared initial presentation materials detailing integration and recommendation engine strategies.

To-Do:

• Refine the integration details between the legacy XML system and new domain objects.
• Develop an initial prototype for the JSON-XML translator for provided RLM message (auth method).
• Begin proof-of-concept implementation for the selected recommendation algorithms.
• Create a MSSQL database for further testing and data persistence
• Schedule a review meeting to discuss recommendation research findings and next steps on translator middleware and persistence layer setup.