  Thousands of documents are made available to the users via the web on a daily
basis. One of the most extensively studied problems in the context of such
document streams is burst identification. Given a term t, a burst is generally
exhibited when an unusually high frequency is observed for t. While spatial and
temporal burstiness have been studied individually in the past, our work is the
first to simultaneously track and measure spatiotemporal term burstiness. In
addition, we use the mined burstiness information toward an efficient
document-search engine: given a user's query of terms, our engine returns a
ranked list of documents discussing influential events with a strong
spatiotemporal impact. We demonstrate the efficiency of our methods with an
extensive experimental evaluation on real and synthetic datasets.
