This study proposes an approach, Shape Query,to mine for related patterns (or events). We assume that two patterns might be related with each other if one pattern history are similar to another. A pattern is defined as one or more consecutive words; the history of a pattern is the frequency distribution of that pattern appearing in the consecutive equal-size time intervals among timestamps texts. To take advantage of the characteristics of Haar wavelet that can be able to keep the skeleton of one shape under control precisely, first of all, all of pattern histories are transformed into Haar Wavelet series using the Discrete Haar Wavelet Transformation (HWT), and stored these series in database. Given a pattern by user, we transform that pattern history into Haar Wavelet series and then search for shape similar patterns based on that transformed series. The database for the experiments of pattern shape query derives from the histories of significant patterns extracted from the abstract of 14,438,209 articles within PubMed from 1990 to 2013. Experimental results show that we can find related patterns according to shape query. Given a pattern ”Wilson’s Disease” for shape query, for example, there are two shape similar patterns,”acute basal ganglia” and ”changes in activity of antioxidant”, that are proved by domain experts related to pattern ”Wilson’s Disease”.
