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Trường DC | Giá trị | Ngôn ngữ |
---|---|---|
dc.contributor.author | Ding, Yichen | vi |
dc.contributor.other | Zhou, Xun | vi |
dc.contributor.other | Wu, Guojun | vi |
dc.contributor.other | Li, Yanhua | vi |
dc.contributor.other | Bao, Jie | vi |
dc.contributor.other | Zheng, Yu | vi |
dc.contributor.other | Luo, Jun | vi |
dc.date.accessioned | 2021-03-31T07:54:48Z | - |
dc.date.available | 2021-03-31T07:54:48Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://tailieuso.tlu.edu.vn/handle/DHTL/10618 | - |
dc.description.abstract | Given a set of user-specified locations and a massive trajectory dataset, the task of mining spatio-temporal reachable regions aims at finding which road segments are reachable from these locations within a given temporal period based on the historical trajectories. Determining such spatio-temporal reachable regions with high accuracy is vital for many urban applications, such as location-based recommendations and advertising. Traditional approaches to answering such queries essentially perform a distance-based range query over the given road network, which does not consider dynamic travel time at different time of day. By contrast, we propose a data-driven approach to formulate the problem as mining actual reachable regions based on a real historical trajectory dataset. Efficient algorithms for the Single-location spatio-temporal reachability Query (S-Query) and the Union-of-multi-location spatio-temporal reachability Query (U-Query) were presented in our recent work. In this paper, we extend the previous ideas by introducing a new type of reachability query with multiple sources, namely, the Intersection-of-multi-location spatio-temporal reachability Query (I-Query). As we demonstrate, answering I-Queries efficiently is generally more computationally challenging than answering either S-Queries or U-Queries because I-Queries involve complicated intersect conditions. We propose two new algorithms called the Intersection-of-Multi-location Query Maximum Bounding region search (I-MQMB) algorithm and the I-Query Trace Back Search (I-TBS) algorithm to efficiently answer I-Queries, which utilize an indexing schema composed of a spatio-temporal index and a connection index. We evaluate our system extensively by using a large-scale real taxi trajectory dataset that records taxi rides in Shenzhen, China. Our results demonstrate that the proposed approach reduces the running time of I-Queries by 50% on average compared to the baseline method | vi |
dc.description.uri | https://doi.org/10.1109/TKDE.2019.2959531 | vi |
dc.language | en | vi |
dc.publisher | IEEE Xplore | vi |
dc.relation.ispartofseries | IEEE Transactions on Knowledge and Data Engineering | vi |
dc.subject | Roads | vi |
dc.subject | Trajectory | vi |
dc.subject | Business | vi |
dc.subject | Indexes | vi |
dc.subject | Query processing | vi |
dc.subject | Public transportation | vi |
dc.subject | Data mining | vi |
dc.title | Mining Spatio-temporal Reachable Regions With Multiple Sources over Massive Trajectory Data | vi |
dc.type | BB | vi |
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