att_abstract={{Tweet streams provide a variety of real-life and real-time information on social events that dynamically change over time.
Although social event detection has been actively studied, how to efficiently monitor evolving events from continuous tweet streams
remains open and challenging. One common approach for event detection from text streams is to use single-pass incremental
clustering. However, this approach does not track the evolution of events, nor does it address the issue of efficient monitoring in the
presence of a large number of events. In this paper, we capture the dynamics of events using four event operations (create, absorb,
split and merge), which can be effectively used to monitor evolving events. Moreover, we propose a novel event indexing structure,
called Multi-layer Inverted List (MIL), to manage dynamic event databases for the acceleration of large-scale event search and update.
We thoroughly study the problem of nearest neighbor search using MIL based on upper bound pruning, along with incremental index
maintenance. Extensive experiments have been conducted on a large-scale real-life tweet dataset. The results demonstrate the
promising performance of our event indexing and monitoring methods on both efficiency and effectiveness.}},
	att_categories={C_BB.1, C_IIS.1},
	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 2015. {{, Volume 27}}{{, Issue 11}}{{, 2015-08-31}}
	author={Divesh Srivastava and Hongyun Cai and Zi Huang and Qing Zhang},
	institution={{IEEE Transactions on Knowledge and Data Engineering}},
	title={{Indexing Evolving Events from Tweet Streams}},