2019 20th IEEE International Conference on Mobile Data Management (MDM)最新文献_第5页

Building a Large-Scale Microscopic Road Network Traffic Simulator in Apache Spark 在Apache Spark中构建大型微观路网交通模拟器

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-42

Zishan Fu, Jia Yu, Mohamed Sarwat

{"title":"Building a Large-Scale Microscopic Road Network Traffic Simulator in Apache Spark","authors":"Zishan Fu, Jia Yu, Mohamed Sarwat","doi":"10.1109/MDM.2019.00-42","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-42","url":null,"abstract":"Road network traffic data has been widely studied by researchers and practitioners in different areas such as urban planning, traffic prediction, and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install GPS receivers and administrators must continuously monitor these devices. There has been a number of urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) scalability: most of them only offer single-machine solution which is not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. In the paper, we propose GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize largescale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 200 thousand vehicles over a very large road network (250 thousand road junctions and 300 thousand road segments).","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"86 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":"115799918","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: 8

Message from the Transport & Urban Analytics Track Chairs 来自交通与城市分析轨道主席的信息

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/mdm.2019.00-95

引用次数: 0

Mining Prevalent Co-Location Patterns Based on Global Topological Relations 基于全局拓扑关系的普遍同位模式挖掘

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-55

Jialong Wang, Lizhen Wang, Xiaoxuan Wang

{"title":"Mining Prevalent Co-Location Patterns Based on Global Topological Relations","authors":"Jialong Wang, Lizhen Wang, Xiaoxuan Wang","doi":"10.1109/MDM.2019.00-55","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-55","url":null,"abstract":"Spatial co-location pattern mining is an important branch in the spatial data mining area, which discovers subsets of spatial features whose instances are frequently located together in the geographic space. The proximity between instances is defined by a distance threshold given by the user in traditional spatial co-location pattern mining. However, the user doesn't know which distance threshold is appropriate in most cases, even for experts. Besides, different densities of instance distribution are not considered in a dataset when using a unified distance threshold to measure the proximity. Also, global topological relations of instances are ignored in mining. In this paper, we consider the global topological relations by constructing Delaunay triangulation of spatial instances and calculate a distance constraint for each instance based on the constructed Delaunay triangulation. We redefine the proximity of instances according to the distance constraint so that users don't have to worry about giving an appropriate distance threshold when mining prevalent co-location patterns. We propose a new algorithm PTB based on a proximity relationship tree P-tree which stores the proximity relationships between instances. The experimental evaluation of several real-world datasets shows that our algorithm can get better results. We also evaluate each parameter and the number of features and instances affecting the efficiency of the algorithm by using synthetic datasets.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"117 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":"127298132","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

Android App Update Timing: A Measurement Study Android应用更新时间:一项测量研究

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00118

Ed Novak, Chris Marchini

引用次数: 4

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00115

Lin Yao, Yuqi Wang, Qiufen Xia, Rui Xu

{"title":"Popularity Prediction Caching Using Hidden Markov Model for Vehicular Content Centric Networks","authors":"Lin Yao, Yuqi Wang, Qiufen Xia, Rui Xu","doi":"10.1109/MDM.2019.00115","DOIUrl":"https://doi.org/10.1109/MDM.2019.00115","url":null,"abstract":"Vehicular Content Centric Network (VCCN) is proposed to cope with mobility and intermittent connectivity issues of vehicular ad hoc networks by enabling the Content Centric Network (CCN) model in vehicular networks. The ubiquitous in-network caching of VCCN allows nodes to cache contents frequently accessed data items, improving the hit ratio of content retrieval and reducing the data access delay. Furthermore, it can significantly mitigate bandwidth pressure. Therefore, it is crucial to cache more popular contents at various caching nodes. In this paper, we propose a novel cache replacement scheme named Popularity-based Content Caching (PopCC), which incorporates the future popularity of contents into our decision making. We adopt Hidden Markov Model (HMM) to predict the content popularity based on the inherent characters of the received interests, request ratio, request frequency and content priority. To evaluate the performance of our proposed scheme PopCC, we compare it with some state-of-the-art schemes in terms of cache hit, average access delay, average hop count and average storage usage. Simulations demonstrate that the proposed scheme possesses a better performance.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"60 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":"132383446","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: 10

Clustering Noisy Trajectories via Robust Deep Attention Auto-Encoders 基于鲁棒深度注意自编码器的噪声轨迹聚类

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-73

Rui Zhang, Peng Xie, Hongbo Jiang, Zhu Xiao, Chen Wang, Ling Liu

{"title":"Clustering Noisy Trajectories via Robust Deep Attention Auto-Encoders","authors":"Rui Zhang, Peng Xie, Hongbo Jiang, Zhu Xiao, Chen Wang, Ling Liu","doi":"10.1109/MDM.2019.00-73","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-73","url":null,"abstract":"Trajectory clustering aims at grouping similar trajectories into one cluster. It is an efficient way of finding the representative path or common trend shared by different moving objects, and also provides a foundation for movement pattern mining, anomaly detection and other applications. Existing trajectory clustering studies mainly rely on feature selection and similarity measurement based on their geographical and spatial properties. However, one obstacle hindering their wide usage is the problem of clustering accuracy in the presence of noisy or incomplete sensing data, due to limited sensory device quantity, communication errors, sensor failures, and sensor vacancy. This paper proposes an error-tolerant trajectory clustering approach by incorporating denoising methods.We propose the Robust Deep Attention Auto-encoders model (called Robust DAA) to learn the representations of low-dimensional denoising trajectories with three novel features. First, we present the deep attention auto-encoders by integrating the attention mechanism into the classical deep auto-encoder, which is capable of enhancing feature propagation and feature selection. Second, we train the deep attention auto-encoder by applying proximal method, back propagation and the Alternating Direction of Method of Multipliers (ADMM). As a result, our Robust DAA can reduce the negative influence of the noise on trajectory data. Finally, we perform clustering over the low-dimensional denoising representations using traditional clustering algorithms and demonstrates the quality of the clustering results by comparing our approach with existing representative methods. Extensive experiments are conducted on both synthetic datasets and real datasets. The results show that our approach outperforms the existing models in terms of accuracy, precision, recall and f1-score.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"33 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":"130235078","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: 4

LaCAVR: Load and Constraints Aware Vehicle Rerouting LaCAVR:负载和约束感知车辆重新路由

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-32

D. Bis, Noah Bix, Benjamin Gruman, S. Guenette, Adam Hauge, Hannah Moser, Jimmy Paul, Goce Trajcevski

引用次数: 0

Offline Worker Selection for Real-Time Spatial Crowdsourcing Multi-Worker Tasks 实时空间众包多工作者任务的离线工作者选择

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00117

Yongjian Zhao, Qi Han

引用次数: 0

Time-Dependent Reachability Analysis: A Data-Driven Approach 随时间变化的可达性分析：数据驱动法

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-64

Chrysovalantis Anastasiou, Chao Huang, S. H. Kim, C. Shahabi

引用次数: 0

Trajectory Prediction from a Mass of Sparse and Missing External Sensor Data 基于大量稀疏且缺失的外部传感器数据的轨迹预测

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-43

L. A. Cruz, K. Zeitouni, J. Macêdo

{"title":"Trajectory Prediction from a Mass of Sparse and Missing External Sensor Data","authors":"L. A. Cruz, K. Zeitouni, J. Macêdo","doi":"10.1109/MDM.2019.00-43","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-43","url":null,"abstract":"In this paper, we predict the movement of objects under the circumstance where external sensors placed on the road-sides (e.g., traffic surveillance cameras) capture their trajectories. This type of trajectories may have very different mobility patterns since they are not restricted to a fleet or a community of users. However, their reported positions are sparse due to the sparsity of the sensor distribution, and incomplete, since the sensors may fail to register the passage of objects. In this paper, we first analyze such external sensor trajectories based on a real dataset, which evidenced the problems of their sparsity and their incompleteness, and hinders the location prediction. In this context, we proposed an approach for coping with the missing data problem. We discussed how to apply this approach in conjunction with the predictors based on Recurrent Neural Networks. In particular, we adjusted the accuracy metrics to account for missing values in the test set, by introducing the distance between the predicted location and the registered next location. We evaluate our approach compared to the baselines, showing an improvement of about 23% in the prediction accuracy while reducing the overall distances. In spite of the contribution of many works in location prediction, at the best of our knowledge, none of those works have studied location prediction for trajectories based on external (road-side) sensors data.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"143 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":"116050313","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: 7