Towards Scalable Information Sensemaking
The amount of data available due to the rapid spread of advanced information technology is exploding. It is expected that this data will be efficiently utilized for data-driven decision making, which is crucial, in particular, for interdisciplinary research where a comprehensive picture of the subject requires large amounts of data from disparate data sources. Despite its increasing availability, managing big research data is often beyond capabilities of individual groups and institutions.
In this talk I will elaborate on challenges in developing an infrastructure that engages large communities to share their data; collectively resolve the data discrepancies; and harmonize their efforts in reliable data fusion. I will consider how concepts of data fusion and crowdsourcing complement each other and accelerate novel research directions in scalable information sensemaking. I will explore each of those concepts and their synergy under prominent scenarios of large-scale historical data integration and situation assessment in multi-robot search and rescue missions.