Our goal is to create a project knowledge-base that can be easily and unobtrusively queried by using a built-in search or using voice commands.
This way the team can retrieve any relevant project artifacts quickly without having to know it's exact location in a deep folder hierarchy. Search queries can be filtered by the type of artifact, the date of creation as well as the respective process stage in which the artifact was created, involved users or by any relevant keyword describing this artifact.
To enable the user to intuitively search for artifacts he might only partially remember, we use machine learning to identify fitting tags and categories for each artifact.
Moreover, we will test the efficiency of relying on the intuition of a large group of people (crowdsourcing) to come up with data categories.
Our system proposes to use an interface that accepts voice commands and translates them into database queries by utilizing Google’s voice recognition API.