Evaluation of Maestro, an extensible general-purpose data gathering and data classification platform

Various tools are available to help navigate and manage the vast amount of data on the Web that is constantly growing. However, automatically searching, gathering, and classifying data from multiple sources can be challenging. This research presents Maestro, a multiuser and multi organization platform that enables users to create and manage specialized search contexts for streamlined this process. With Maestro, users can define and configure these search contexts to gather data from different web sources, process and filter the obtained data, classify it using machine learning models, and send the results to external services. Maestro platform is designed to be extensible, allowing new features to be easily and incrementally added without disrupting its core framework. The paper presents and examines two simple but illustrative examples showcasing the capabilities of Maestro: the Bird-species dataset and the Sound-to-Text conversion dataset. The article also evaluates Maestro's usability and extensibility through two user assessments. The first assessment focused on evaluating the user perception, including its usability and relevance, and the second on the easiness of adding new components to extend its functionality by developers. The results were positive and encouraging, with users praising the platform's ease of use and versatility.

Automated summary

The research introduces Maestro, a platform that allows users to create specialized search contexts to automatically search, gather, and classify data from various web sources. Maestro can process and filter the obtained data, classify it using machine learning models, and send the results to external services. The usability and extensibility of Maestro were evaluated through user assessments, with positive feedback on its ease of use and versatility.