Discovering partial periodic patterns on discrete spatial-temporal data

Speaker: CAO Huiping

Abstract

Partial period pattern discovered from spatial-temporal data refers to those location series that appear periodically and frequently. More spatial-temporal data are generated with the development of moving computing equipments. Most provided methods support queries on such kind of data efficiently by making use of index. We are trying to find some periodic patterns from the data to facilitate the query. Existing works on periodic pattern mining either assume that the periods are given in advance by the user or could not efficiently find the periods automatically. In this talk, I will introduce our method on how to find the possible periods automatically and discover the patterns efficiently. The first step of our method scans the discrete spatial-temporal data and constructs a memory based structure, abbreviated list, to find the potential periods. In the same time, we create disk-based inverted list for the typical data point in the sequence. In the second step, all the frequent patterns are found taking advantage of the disk-based inverted list gotten from the first step. In order to show the effectiveness of our method, some experiment results will be given.

Referred Papers