Information Retrieval is a key component in and analysis approaches conducted at GRIS for over 10 years. In addition to textual documents that pose the most established data type in retrieval approaches, application examples at GRIS include the Visual Search , retrieval of 3D objects , or graph structures in large network data. time series data
and Information Visualization are two key competences at GRIS that support the visual search and interactive Information Retrieval process. Visual Analytics
Exploratory search combines the two principles of search (in data) and exploration (of data). Data mining, machine learning, information visualization and visual analytics techniques can be employed to facilitate the design of exploratory search systems.
is the principle to search for similarity measures that can be used, e.g., to conduct information retrieval tasks. In many cases, the Interactive Similarity Search principle is applied at GRIS to find measures of similarity that reflect the notion of similarity of involved user groups. User-Centered Design
Information Retrieval explained
Information Retrieval refers to as the computer-supported search for complex content in large data collections as well as the presentation of search results to the user. For users with a clearly definable information need (the query term), Information Retrieval can improve the effectiveness of the information seeking process considerably. Research in Information Retrieval can be conducted in several ways. First, supporting users in the formulation of meaningful queries is one way – the use of visual interactive interfaces is one way to achieve this. Second, retrieval algorithms are subject to ongoing research. Similarity measures, data mining, or database technologies are related aspects in this connection. Finally, the presentation of search results to the user requires novel solutions. Information visualization and human computer interaction are two associated research fields.