We describe a Visual Analytics (VA) infrastructure, rooted on techniques in machine learning and logic-based deductive reasoning that will assist analysts to make sense of large, complex data sets by facilitating the generation and validation of models representing relationships in the data. We use Logic Programming (LP) as the underlying computing machinery to encode the relations as rules and facts and compute with them. A unique aspect of our approach is that the LP rules are automatically learned, using Inductive Logic Programming, from examples of data that the analyst deems interesting when viewing the data in the highdimensional visualization interface. Using this system, analysts will be able to construct models of arbitrary relationships in the data, explore the data for scenarios that fit the model, refine the model if necessary, and query the model to automatically analyze incoming (future) data exhibiting the encoded relationships. In other words it will support both model-driven data exploration, as well as data-driven model evolution. More importantly, by basing the construction of models on techniques from machine learning and logic-based deduction, the VA process will be both flexible in terms of modeling arbitrary, user-driven relationships in the data as well as readily scale across different data domains.

Abstract—This paper describes a novel method for browsing a large video collection. It links various forms of related video fragments together as threads. These threads are based on query results, the timeline as well as visual and semantic similarity. We design two interfaces which use threads as the basis for browsing. One interface shows a minimal set of threads, and the other as many as fit on the screen. To evaluate both interfaces we perform a regular user study, a study based on user simulation, and we participated in the interactive video retrieval task of the TRECVID benchmark. The results indicate that the use of threads in interactive video retrieval is beneficial. Furthermore, we found that in general the query result and the timeline are the most important threads, but having several additional threads improves the performance as it encourages people to explore new dimensions. Index Terms—Conceptual similarity, information visualization, interactive search, multidimensional browsing, semantic threads.