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However, applications such as future movement prediction need real-time processing over streaming trajectories. Thus, we investigate real-time distributed co-movement pattern detection over streaming trajectories.<\/jats:p>\n          <jats:p>Existing off-line methods assume that all data is available when the processing starts. Nevertheless, in a streaming setting, unbounded data arrives in real time, making pattern detection challenging. To this end, we propose a framework based on Apache Flink, which is designed for efficient distributed streaming data processing. The framework encompasses two phases: clustering and pattern enumeration. To accelerate the clustering, we use a range join based on two-layer indexing, and provide techniques that eliminate unnecessary verifications. To perform pattern enumeration efficiently, we present two methods FBA and VBA that utilize id-based partitioning. When coupled with bit compression and candidate-based enumeration techniques, we reduce the enumeration cost from exponential to linear. Extensive experiments offer insight into the efficiency of the proposed framework and its constituent techniques compared with existing methods.<\/jats:p>","DOI":"10.14778\/3339490.3339502","type":"journal-article","created":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T12:50:07Z","timestamp":1565182207000},"page":"1208-1220","source":"Crossref","is-referenced-by-count":58,"title":["Real-time distributed co-movement pattern detection on streaming trajectories"],"prefix":"10.14778","volume":"12","author":[{"given":"Lu","family":"Chen","sequence":"first","affiliation":[{"name":"Aalborg University, Aalborg, Denmark"}]},{"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China and Alibaba-Zhejiang University, Hangzhou, China"}]},{"given":"Ziquan","family":"Fang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China and Alibaba-Zhejiang University, Hangzhou, China"}]},{"given":"Xiaoye","family":"Miao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"given":"Christian S.","family":"Jensen","sequence":"additional","affiliation":[{"name":"Aalborg University, Aalborg, Denmark"}]},{"given":"Chenjuan","family":"Guo","sequence":"additional","affiliation":[{"name":"Aalborg University, Aalborg, Denmark"}]}],"member":"320","published-online":{"date-parts":[[2019,6]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"81","article-title":"A framework for clustering evolving data streams","volume":"29","author":"Aggarwal C. 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