Querying streaming data is becoming a dominant problem in big data analytics. A practical approach to querying streaming data is through traditional databases that have been modified to support streams, such as MySQL. However, conditional selection for querying data streams is currently an open challenge. We present a new visual framework that provides a more intuitive querying interaction for streaming data by combining visual selections on patterns with image processing techniques in order to better identify regions of interest. The main contribution of this paper is a novel method for matching patterns among normalized frames via feature vector clustering.
['Chenhui Li', 'George Baciu', 'Yunzhe Wang']
VisQuery: Visual querying of streaming data via pattern matching