@inproceedings{1b4be1337149477c9d50528c6bcd29ca,
title = "Discovering all-Chain set in streaming time series",
abstract = "Time series chains discovery is an increasingly popular research area in time series mining. Previous studies on this topic process fixed-length time series. In this work, we focus on the issue of all-chain set mining over the streaming time series, where the all-chain set is a very important kind of the time series chains. We propose a novel all-chain set mining algorithm about streaming time series (ASMSTS) to solve this problem. The main idea behind the ASMSTS is to obtain the mining results at current time-tick based on the ones at the last one. This makes the method more efficiency in time and space than the Na{\"i}ve. Our experiments illustrate that ASMSTS does indeed detect the all-chain set correctly and can offer dramatic improvements in speed and space cost over the Naive method.",
keywords = "All-chain set, Streaming time series, Time series chains",
author = "Shaopeng Wang and Ye Yuan and Hua Li",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019 ; Conference date: 14-04-2019 Through 17-04-2019",
year = "2019",
doi = "10.1007/978-3-030-16148-4_24",
language = "English",
isbn = "9783030161477",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "306--318",
editor = "Min-Ling Zhang and Zhiguo Gong and Zhi-Hua Zhou and Qiang Yang and Sheng-Jun Huang",
booktitle = "Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings",
address = "Germany",
}