文章基本信息

标题：Addendum to “Mining Similar Traces of Entities on Web” (published in Vol. 15, No 6)
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作者：Xinyan Huang ; Xinjun Wang ; Hui Li 等
期刊名称：Cybernetics and Information Technologies
印刷版ISSN：1311-9702
电子版ISSN：1314-4081
出版年度：2016
卷号：16
期号：1
页码：188
DOI：10.1515/cait-2016-0015
出版社：Bulgarian Academy of Science
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.