摘要：Different research objects and different meaning of a sliding time window. Our work presented in [1] is based on massive events data sets in a long history which have already been extracted, while the work presented in [2] is based on the context in real-time from the live public data stream of Twitter. So the scale of data to process in the case of [2] isn't in the same order of magnitude as ours, where the same approach has a different meaning for our work. Take a sliding time window model, for example, taking into consideration such a big scale of events. The main aim of applying time windows in our work is to facilitate parallel processing and achieve high computational efficiency. A sliding window model is applied in [2] to extract events.
