Hongbo Yin, Donghua Yang, Kaiqi Zhang, Hong Gao, Jianzhong Li
{"title":"利用室内轨迹搜索客流量最大的房间","authors":"Hongbo Yin, Donghua Yang, Kaiqi Zhang, Hong Gao, Jianzhong Li","doi":"10.1007/s10791-024-09457-2","DOIUrl":null,"url":null,"abstract":"<p>In a wide variety of applications, such as indoor position selection for advertising and setting rents of different shops in a shopping mall, it is better to get the passenger flow of each room. In the indoor space, the positions of users are commonly captured by the indoor positioning system consisting of static positioning devices. And the sequence of all tracking events with the same user ordered by the corresponding time is the indoor trajectory of this user. Thus, in this paper, we define and study two essential queries named Rooms with top-<i>k</i> passenger flows at a Timestamp query (R<i>k</i>T for short) and Rooms with top-<i>k</i> passenger flows within a time Interval query (R<i>k</i>I for short), i.e., how to search rooms with top-<i>k</i> passenger flows at a timestamp and within a time interval in the past using indoor trajectories, respectively. For the indoor positioning system, there are only limited static positioning devices deployed in the indoor space on account of the cost. And the detection ranges of these static positioning devices only cover a small part of the indoor space. When a user is in the undetected state, there is uncertainty in its position combined with the quite complex indoor topology. Such uncertainty brings great challenges to determining the passenger flow in each room. Considering the distribution of static positioning devices, we propose a new method about how to reasonably infer where a user is in the undetected state and the corresponding probability based on its indoor trajectory and the complex indoor topology. In order to quickly retrieve the set of indoor trajectories, we propose a full Binary tree indexing indoor trajectories divided by Time intervals (BiT for short), which is built on the given set of indoor trajectories. Based on the index BiT, we propose PAT Algorithm and PAI Algorithm to efficiently process R<i>k</i>T and R<i>k</i>I queries, respectively. Extensive experiment results demonstrate superior performance of PAT Algorithm and PAI Algorithm.</p>","PeriodicalId":54352,"journal":{"name":"Information Retrieval Journal","volume":"31 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Searching rooms with top-k passenger flows using indoor trajectories\",\"authors\":\"Hongbo Yin, Donghua Yang, Kaiqi Zhang, Hong Gao, Jianzhong Li\",\"doi\":\"10.1007/s10791-024-09457-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In a wide variety of applications, such as indoor position selection for advertising and setting rents of different shops in a shopping mall, it is better to get the passenger flow of each room. In the indoor space, the positions of users are commonly captured by the indoor positioning system consisting of static positioning devices. And the sequence of all tracking events with the same user ordered by the corresponding time is the indoor trajectory of this user. Thus, in this paper, we define and study two essential queries named Rooms with top-<i>k</i> passenger flows at a Timestamp query (R<i>k</i>T for short) and Rooms with top-<i>k</i> passenger flows within a time Interval query (R<i>k</i>I for short), i.e., how to search rooms with top-<i>k</i> passenger flows at a timestamp and within a time interval in the past using indoor trajectories, respectively. For the indoor positioning system, there are only limited static positioning devices deployed in the indoor space on account of the cost. And the detection ranges of these static positioning devices only cover a small part of the indoor space. When a user is in the undetected state, there is uncertainty in its position combined with the quite complex indoor topology. Such uncertainty brings great challenges to determining the passenger flow in each room. Considering the distribution of static positioning devices, we propose a new method about how to reasonably infer where a user is in the undetected state and the corresponding probability based on its indoor trajectory and the complex indoor topology. In order to quickly retrieve the set of indoor trajectories, we propose a full Binary tree indexing indoor trajectories divided by Time intervals (BiT for short), which is built on the given set of indoor trajectories. Based on the index BiT, we propose PAT Algorithm and PAI Algorithm to efficiently process R<i>k</i>T and R<i>k</i>I queries, respectively. Extensive experiment results demonstrate superior performance of PAT Algorithm and PAI Algorithm.</p>\",\"PeriodicalId\":54352,\"journal\":{\"name\":\"Information Retrieval Journal\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Retrieval Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10791-024-09457-2\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Retrieval Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10791-024-09457-2","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
在各种应用中,如广告的室内位置选择和商场内不同商铺的租金设定,最好能获得每个房间的客流量。在室内空间,用户的位置通常由静态定位设备组成的室内定位系统捕捉。而同一用户的所有跟踪事件按相应时间排序的序列就是该用户的室内轨迹。因此,在本文中,我们定义并研究了两个基本查询,分别名为 "时间戳上客流量前 k 位的房间 "查询(简称 RkT)和 "时间间隔内客流量前 k 位的房间 "查询(简称 RkI),即如何利用室内轨迹搜索过去某个时间戳上客流量前 k 位的房间和某个时间间隔内客流量前 k 位的房间。对于室内定位系统来说,由于成本原因,在室内空间部署的静态定位设备有限。而这些静态定位设备的探测范围只能覆盖室内空间的一小部分。当用户处于未检测状态时,其位置存在不确定性,再加上室内拓扑结构相当复杂。这种不确定性给确定每个房间的客流量带来了巨大挑战。考虑到静态定位设备的分布,我们提出了一种新方法,即如何根据用户的室内轨迹和复杂的室内拓扑结构,合理推断出用户处于未检测状态的位置以及相应的概率。为了快速检索室内轨迹集,我们在给定的室内轨迹集的基础上,提出了以时间间隔划分的室内轨迹全二叉树索引(简称 BiT)。基于 BiT 索引,我们提出了 PAT 算法和 PAI 算法,分别用于高效处理 RkT 和 RkI 查询。广泛的实验结果证明了 PAT 算法和 PAI 算法的卓越性能。
Searching rooms with top-k passenger flows using indoor trajectories
In a wide variety of applications, such as indoor position selection for advertising and setting rents of different shops in a shopping mall, it is better to get the passenger flow of each room. In the indoor space, the positions of users are commonly captured by the indoor positioning system consisting of static positioning devices. And the sequence of all tracking events with the same user ordered by the corresponding time is the indoor trajectory of this user. Thus, in this paper, we define and study two essential queries named Rooms with top-k passenger flows at a Timestamp query (RkT for short) and Rooms with top-k passenger flows within a time Interval query (RkI for short), i.e., how to search rooms with top-k passenger flows at a timestamp and within a time interval in the past using indoor trajectories, respectively. For the indoor positioning system, there are only limited static positioning devices deployed in the indoor space on account of the cost. And the detection ranges of these static positioning devices only cover a small part of the indoor space. When a user is in the undetected state, there is uncertainty in its position combined with the quite complex indoor topology. Such uncertainty brings great challenges to determining the passenger flow in each room. Considering the distribution of static positioning devices, we propose a new method about how to reasonably infer where a user is in the undetected state and the corresponding probability based on its indoor trajectory and the complex indoor topology. In order to quickly retrieve the set of indoor trajectories, we propose a full Binary tree indexing indoor trajectories divided by Time intervals (BiT for short), which is built on the given set of indoor trajectories. Based on the index BiT, we propose PAT Algorithm and PAI Algorithm to efficiently process RkT and RkI queries, respectively. Extensive experiment results demonstrate superior performance of PAT Algorithm and PAI Algorithm.
期刊介绍:
The journal provides an international forum for the publication of theory, algorithms, analysis and experiments across the broad area of information retrieval. Topics of interest include search, indexing, analysis, and evaluation for applications such as the web, social and streaming media, recommender systems, and text archives. This includes research on human factors in search, bridging artificial intelligence and information retrieval, and domain-specific search applications.