2019 20th IEEE International Conference on Mobile Data Management (MDM)最新文献_第6页

Group Nearest Compact POI Set Queries in Road Networks 路网中的组最近紧凑POI集查询

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-68

Sen Zhao, Li Xiong

引用次数: 6

Message from the BlockApp'19 Workshop Chairs 来自BlockApp'19工作坊主席的信息

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/mdm.2019.00-91

Xiaowen Chu, Bin Xiao, Jiang Xiao

引用次数: 0

A Keyword-Aware Optimal Route Query Algorithm on Large-Scale Road Networks 基于关键字感知的大规模路网最优路径查询算法

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00124

Jinyao Hao, Baoning Niu, X. Qin

{"title":"A Keyword-Aware Optimal Route Query Algorithm on Large-Scale Road Networks","authors":"Jinyao Hao, Baoning Niu, X. Qin","doi":"10.1109/MDM.2019.00124","DOIUrl":"https://doi.org/10.1109/MDM.2019.00124","url":null,"abstract":"Mobile users prefer to choose personalized travel routes using their mobile terminals. Keyword-aware Optimal Route Query (KORQ) is proposed to meet users' need because it not only considering the length of the route, but also considering the cost of the route and the points of interest covered by the route. As the number of points of interest in road networks increases sharply, the time and space complexity for preprocessing and route expansion rises dramatically, and the state of the art algorithms are not scalable. To address these issues, we propose an algorithm called KORAL, short for Keyword-aware Optimal Route Query Algorithm on Large-scale Road Networks. To reduce the overhead of preprocessing, KORAL partitions the road network into subgraphs, and stores only the information about the routes between subgraphs in preprocessing stages. In the rout expansion stage, KORAL uses a strategy called minimum budget pruning to prune infeasible vertices, which greatly speed up the route expansion. Experiments against 3 datasets of New York road network in a server with 16G RAM show that KORAL is scalable and breaks the limitation that the road network cannot exceed 100K vertices.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121414190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Sextant: Grab's Scalable In-Memory Spatial Data Store for Real-Time K-Nearest Neighbour Search 六分仪:Grab的可扩展内存空间数据存储，用于实时k近邻搜索

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-51

Zhiyin Zhang, Xiaocheng Huang, Chaotang Sun, Shaolin Zheng, Bo Hu, Jagannadan Varadarajan, Yifang Yin, Roger Zimmermann, Guanfeng Wang

{"title":"Sextant: Grab's Scalable In-Memory Spatial Data Store for Real-Time K-Nearest Neighbour Search","authors":"Zhiyin Zhang, Xiaocheng Huang, Chaotang Sun, Shaolin Zheng, Bo Hu, Jagannadan Varadarajan, Yifang Yin, Roger Zimmermann, Guanfeng Wang","doi":"10.1109/MDM.2019.00-51","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-51","url":null,"abstract":"Locating nearest moving objects in real-time is a vital problem that the ride-hailing industry needs to address. For instance, when a passenger makes a booking, the service provider, such as Grab or Uber, needs to locate the K nearest drivers for the given pickup location in case the closest driver is not optimal for this booking request. This poses two main challenges: firstly, massive frequent write operations are needed to track the objects' current locations. As drivers can move as fast as 25 meters per second in developed countries like Singapore, it is therefore important to update drivers' locations at a second, if not millisecond, granularity. Secondly, a K-nearest neighbour (kNN) query poses tremendous challenges, compared to a simple Get query, in a key-value data store such as Redis. This paper presents Sextant, a scalable in-memory spatial data store tailored for kNN searches. Sextant is decentralized, scalable, reliable, efficient and highly available. It has been supporting Grab's daily flow with no downtime for more than one year, with write QPS (query per second) and kNN query QPS approaching millions.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125367100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

SSVisual: Intelligent Start-Stop System 智能启停系统

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-30

Cuizhu Bao, Chen Chen, H. Kui, Xiaoyang Wang

引用次数: 0

A Personal Location Prediction Method to Solve the Problem of Sparse Trajectory Data 一种解决轨迹数据稀疏问题的个人位置预测方法

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-41

Fan Li, Qingquan Li, Zhen Li, Zhao Huang, Xiaomeng Chang, J. Xia

{"title":"A Personal Location Prediction Method to Solve the Problem of Sparse Trajectory Data","authors":"Fan Li, Qingquan Li, Zhen Li, Zhao Huang, Xiaomeng Chang, J. Xia","doi":"10.1109/MDM.2019.00-41","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-41","url":null,"abstract":"The rapid development of information and communication technology and the popularization of mobile devices have generated a large number of spatiotemporal trajectory data. Trajectory data can be applied to location prediction, which is significant for urban traffic planning and location-based service. Although various methods for personal location prediction have been proposed, the historical trajectory data of some users is always sparse in practical applications, resulting in poor prediction precision of prediction models based on personal historical data for those sparse users. Targeting on this challenge, we propose an \"Individual trajectory-Group trajectory assist Individual trajectory\" location prediction model (ITGTAIT) by utilizing the group travel patterns to assist in predicting personal locations. First, the model conducts a spatial clustering algorithm on trajectory points to construct the clustering link. Second, the clustering link and Fano's inequality are used to estimate the predictability of the next location. Third, a Variable Order Markov Model that named Prediction by Partial Match (PPM) was adopted to predict the clustering link based on the individual trajectory for users with sufficient data. For users with sparse samples, the PPM utilizes the pattern of group travels, which using the group trajectory to assist individual trajectory. Finally, our method was evaluated by using 608,712 trajectory points from 5000 volunteers at Shenzhen city, China. The result shows that a) with the increase of training data, the precision of the ITGTAIT is gradually stable, b) for users with over four days of data, the highest precision is 87.11%, stable at about 82%, c) for users with only 1-3 days of data, the prediction precision is 55.15%, 67.04%, and 76.86% respectively, which introduces approximately 10.76%, 10.98% and 6.83% performance gains on location predictions respectively by utilizing the group characters.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115821790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Learned Index for Spatial Queries 空间查询的学习索引

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00121

Haixin Wang, Xiaoyi Fu, Jianliang Xu, Hua Lu

引用次数: 52

An Effective Approach on Mining Co-Location Patterns from Spatial Databases with Rare Features 从稀有特征空间数据库中挖掘同位模式的有效方法

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-74

Peizhong Yang, Lizhen Wang, Xiaoxuan Wang, Dianwu Fang

{"title":"An Effective Approach on Mining Co-Location Patterns from Spatial Databases with Rare Features","authors":"Peizhong Yang, Lizhen Wang, Xiaoxuan Wang, Dianwu Fang","doi":"10.1109/MDM.2019.00-74","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-74","url":null,"abstract":"A co-location pattern is a group of spatial features whose instances are frequently appearing together in geography. Co-location pattern mining is particularly valuable for discovering spatial dependencies. Lots of co-location pattern mining approaches have been proposed, but they often emphasize the equal participation of every spatial feature. As a result, the interesting pattern which involves spatial features with significantly different for the number of instances cannot be captured. In this paper, we are committed to address the problem of mining co-location patterns from the spatial database with rare features. Specifically, we first propose a new interest measure, namely the weighted participation index. This interest measure is related to the distribution of the number of instances for spatial features, and it has ability to capture the prevalent co-location patterns with or without rare features. Furthermore, we prove that the weighted participation index possesses the approximate monotonicity property, which can be utilized to improve the computational efficiency, and thereby an efficient algorithm is developed. As demonstrated by extensive experiments, our approach is effective, efficient and scalable for mining co-location patterns embedded in the spatial database with rare features.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121284603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Message from the MUST'19 Workshop Chairs 来自MUST'19研讨会主席的信息

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/mdm.2019.00-88

引用次数: 0

MR-Cubes: On-the-Fly Computation of Location Popularity from Check-in Data Streams MR-Cubes:从签到数据流中实时计算位置人气

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-77

G. Constantinou, Chrysovalantis Anastasiou, Dimitris Stripelis, C. Shahabi

{"title":"MR-Cubes: On-the-Fly Computation of Location Popularity from Check-in Data Streams","authors":"G. Constantinou, Chrysovalantis Anastasiou, Dimitris Stripelis, C. Shahabi","doi":"10.1109/MDM.2019.00-77","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-77","url":null,"abstract":"Several applications in urban planning, ride-sharing or marketing, require access to the location popularity of a geographical area (e.g., city block, city, county) in near real-time and at different resolutions. To conceptualize such an access, imagine a visualization tool to view a heatmap of location popularity of a region on-the-fly as a user interacts seamlessly by zooming in and out. The access method required to enable such a seamless visualization must support: 1) updating the heatmap cells frequently as the raw data (e.g., check-ins) arrives at a high rate in a streaming fashion, and 2) splitting and merging the adjacent cells quickly to support zooming in and out, respectively. This is challenging because the most useful metric for location popularity, location entropy, requires counting the number of unique visits per user, and hence: 1) a large data structure should be maintained and updated per cell, and 2) the adjacent cells must be aggregated/disaggregated quickly while the unique visits are not additive. Due to these challenges, the previous techniques for OLAP cubes, streaming sketches and index structures are not effective. In this paper, we propose a new index structure called MR-Cube that approximates the popularity by maintaining sketches of streamed data per cell, supports time-decay for older visits and aggregates the non-additive location popularity quickly and accurately at different resolutions. We evaluate the accuracy and efficiency of MR-Cube using real-world and synthetic datasets and show its utility for our application.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131722765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0