att_abstract={{The ever-increasing scale of streaming texts presents a fundamental challenge to analyzing, visualizing and discovering useful information among the endless rivers of text available in social media. 
In this paper, we present an online visual search engine that efficiently handles querying and retrieval of text streams of interest for understanding streaming tweet data. With regards to the user-specified s
earch query, the retrieved tweets are automatically categorized and summarized with semantic analysis and clustering methods. To visualize the search results, we present CompactMap, a dynamic visualization tha
t packs tweet clusters within the display space for efficient space utilization. It also achieves a temporal coherent layout by dynamically matching the remaining clusters across time, incrementally discarding
 outdated clusters and incorporating new-coming clusters, thus preserving the user's mental map. We show the effectiveness and usefulness of our system through case studies in event tracking and topic evolution monitoring in real-time.}},
	att_authors={yh573v, sn1789},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2013. {{, 2013-10-01}}
	author={Yifan Hu and Stephen North and Xiaotong Liu and Han-wei Shen},
	institution={{IEEE Symposium on Large Scale Data Analysis and Visualizatuion}},
	title={{An Online Visual Search Engine for Mining Streaming Text Data in Real-Time}},