Detection and Interpretation of Low-Level and High-Level Surprising and Important Events in Large-Scale Data Streams

Final Report: Detection and Interpretation of Low-Level and High-Level Surprising and Important Events in LargeScale Data Streams Report Title This project explored how to mathematically formalize the computations of surprise and relevance of events in large data streams, including video, audio and text. We have developed new mathematical theories to define surprise in terms of how new data observations may or not affect an observer’s set of beliefs. This is computed in terms of the Kullback-Leibler divergence between posterior and prior beliefs of the observer, and quantified in a new unit of “wows”. Likewise, we have developed a new general theory of relevance that quantifies how new data observations may or not affect an observer’s beliefs about how she/he/it will achieve its goals. Data observations which suggest that some previously possible solutions to a problem are now invalid will be measured as more relevant, in a new unit of “rels”. Both theories have been extensively tested using large video (~3000 hours) and text (twitter feeds) datasets. (a) Papers published in peer-reviewed journals (N/A for none) Enter List of papers submitted or published that acknowledge ARO support from the start of the project to the date of this printing. List the papers, including journal references, in the following categories: