2014 IEEE 15th International Conference on Mobile Data Management最新文献

Differentially Private Location Recommendations in Geosocial Networks 地理社交网络中的差异化私密位置推荐

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.13

Jiadong Zhang, Gabriel Ghinita, Chi-Yin Chow

引用次数: 37

WhereToGo: Personalized Travel Recommendation for Individuals and Groups WhereToGo:为个人和团体提供个性化旅游推荐

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.12

Long Guo, Jie Shao, K. Tan, Yang Yang

{"title":"WhereToGo: Personalized Travel Recommendation for Individuals and Groups","authors":"Long Guo, Jie Shao, K. Tan, Yang Yang","doi":"10.1109/MDM.2014.12","DOIUrl":"https://doi.org/10.1109/MDM.2014.12","url":null,"abstract":"With the rapid development of GPS-enabled mobile devices, huge amounts of user-contributed data with location information can be collected from the Internet. With this kind of data, one promising application is travel recommendation, which has attracted a considerable number of researches recently. However, most of the previous studies only focus on one aspect of the relations among users and locations or make a coarse linear combination of the relations. Moreover, all the existing work on travel recommendation do not consider recommendation to groups, which is an important characteristic of travelers' behavior. In this paper, we present a personalized travel recommendation system named Where to Go. The novelty of the system is a 3R model which can unify user-location relation, user-user relation and location-location relation into a single framework and perform random walk with restart to analyze the model. We further extend our approach to provide recommendations for groups. To the best of our knowledge, this is the first work to use random walk with restart for group recommendation. We conduct a comprehensive performance evaluation using a real dataset collected from Flickr, which is one of the most popular online photo-sharing sites. Experimental results show that our approach provides significantly superior recommendation quality compared to other state-of-the-art travel recommendation approaches for both individuals and groups.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125391003","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: 16

E-VeT: Economic Reward/Penalty-Based System for Vehicular Traffic Management E-VeT:车辆交通管理经济奖罚制度

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.18

Nilesh Padhariya, O. Wolfson, Anirban Mondal, Varun Gandhi, S. Madria

引用次数: 5

Trajectory Event Cleaning for Mobile RFID Objects 移动RFID对象的轨迹事件清洗

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.23

Guoqiong Liao, Philip S. Yu, Qianhui Zhong, Sihong Xie, Zhen Shen, Changxuan Wan, Dexi Liu

{"title":"Trajectory Event Cleaning for Mobile RFID Objects","authors":"Guoqiong Liao, Philip S. Yu, Qianhui Zhong, Sihong Xie, Zhen Shen, Changxuan Wan, Dexi Liu","doi":"10.1109/MDM.2014.23","DOIUrl":"https://doi.org/10.1109/MDM.2014.23","url":null,"abstract":"With the rapid development of Radio Frequency Identification (RFID), sensor and wireless technologies, a large amount of trajectory data of moving objects are emerging, and trajectory data mining has received more and more attentions recently. However, since the data collected by sensors and RFID readers are usually noisy, it is necessary and meaningful to clean up the noise, including missing detection events and cross detection events, so as to provide high quality data for various applications using trajectory data. Cleaning up the trajectory events should take into account of uncertainty of location and unreliability of event detection at the same time. In the paper, we first discuss the rules to distinguish between normal detection events and false detection events in the trajectories, using constraints on continuous motion between adjacent detection regions and direct moving time between neighboring physical regions. Then, as a unified cleaning framework, we establish a probabilistic region connection graph to represent region detection features, region connection relationships, and region transition probabilities of neighboring physical regions. Focusing on interpolating missing events, we suggest two path-based probabilistic interpolating strategies, namely, the Most Likely Path (MLP) strategy and the Highest Weighting Probability Path (HWPP) strategy. Also, we discuss pruning rules of candidate paths for reducing computational cost. Finally, we conduct experiments over simulation data to demonstrate the effectiveness and efficiency of the proposed methods.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070801","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

Supporting Location-Based Services in a Main-Memory Database 在主存数据库中支持基于位置的服务

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.7

S. Ray, Rolando Blanco, Anil K. Goel

{"title":"Supporting Location-Based Services in a Main-Memory Database","authors":"S. Ray, Rolando Blanco, Anil K. Goel","doi":"10.1109/MDM.2014.7","DOIUrl":"https://doi.org/10.1109/MDM.2014.7","url":null,"abstract":"With the proliferation of mobile devices and explosive growth of spatio-temporal data, Location-Based Services (LBS) have become an indispensable technology in our daily lives. The key characteristics of the LBS applications include a high rate of time-stamped location updates, and many concurrent historical, present and predictive queries. The commercial providers of LBS must support all three kinds of queries and address the high update rates. While they employ relational databases for this purpose, traditional databases are unable to cope with the growing demands of many LBS systems. Support for spatio-temporal indexes within these databases are limited to R-tree based approaches. Although a number of advanced spatio-temporal indexes have been proposed by the research community, only a few of them support historical queries. These indexing techniques, with support for historical queries, are unable to sustain high update and query throughput typical in LBS. Technological trends involving increasingly large main memory and core footprints offer opportunities to address some of these issues. We present several key ideas to support high performance commercial LBS by exploiting in-memory database techniques. Taking advantage of very large memory available in modern machines, our system maintains the location data and index for the past N days in memory. Older data and index are kept in disk. We propose an in-memory storage organization for high insert performance. We also introduce a novel spatio-temporal index that maintains partial temporal indexes in a versioned-grid structure. The partial temporal indexes are organized as compressed bitmaps. With extensive evaluation, we demonstrate that our system supports high insert and query throughputs and it outperforms the leading LBS system by a significant margin.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"283 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121362423","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: 19

Challenges in Crowdsourcing Real-Time Information for Public Transportation 众包公共交通实时信息的挑战

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.70

Naveen Nandan, A. Pursche, Xing Zhe

引用次数: 25

Efficient Retrieval of Top-K Most Similar Users from Travel Smart Card Data 从旅游智能卡数据中高效检索Top-K最相似用户

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.38

Bolong Zheng, Kai Zheng, M. Sharaf, Xiaofang Zhou, S. Sadiq

{"title":"Efficient Retrieval of Top-K Most Similar Users from Travel Smart Card Data","authors":"Bolong Zheng, Kai Zheng, M. Sharaf, Xiaofang Zhou, S. Sadiq","doi":"10.1109/MDM.2014.38","DOIUrl":"https://doi.org/10.1109/MDM.2014.38","url":null,"abstract":"Understanding the dynamics of human daily mobility patterns is essential for the management and planning of urban facilities and services. Travel smart cards, which record users' public transporting histories, capture rich information of users' mobility pattern. This provides the opportunity to discover valuable knowledge from these transaction records. In recent years, research on measuring user similarity for behavior analysis has attracted a lot of attention in applications such as recommendation systems, crowd behavior analysis applications, and numerous data mining tasks. In this paper, our goal is to estimate the similarity between users' travel patterns according to their travel smart card data. The core of our proposal is a novel user similarity measurement, namely, Travel Spatial-Temporal Similarity (TST), which measures the spatial range and temporal similarity between users. Moreover, we also propose a hybrid index structure, which integrates inverted files and cluster-based partitioning, to allow for efficient retrieval of the top-K most similar users. Through experimental evaluation, our proposed approach is shown to deliver scalable performance.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116964807","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

RoadEye: A System for Personalized Retrieval of Dynamic Road Conditions RoadEye:动态路况个性化检索系统

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.42

Anirban Mondal, Avinash Sharma, Kuldeep Yadav, Abhishek Tripathi, Atul Singh, N. Piratla

引用次数: 3

Dynamic Content and Route Management in Wireless Networks 无线网络中的动态内容和路由管理

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.58

S. Madria, Anirban Mondal, Tridib Mukherjee

引用次数: 1

Skyline Travel Routes: Exploring Skyline for Trip Planning 天际线旅行路线:探索天际线旅行计划

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.64

Wan Ting Hsu, Y. Wen, Ling-Yin Wei, Wen-Chih Peng

{"title":"Skyline Travel Routes: Exploring Skyline for Trip Planning","authors":"Wan Ting Hsu, Y. Wen, Ling-Yin Wei, Wen-Chih Peng","doi":"10.1109/MDM.2014.64","DOIUrl":"https://doi.org/10.1109/MDM.2014.64","url":null,"abstract":"In this paper, given a spatial range Q and a set of query points specified by users, the goal of this paper is to return the travel routes that fulfill two requirements: 1.) travel routes should contain all those query points specified, and 2.) travel routes should be within the spatial range Q. Furthermore, we claim that each query point may have its proper visiting time. As such, the travel routes should go through these query points at their corresponding proper visiting time. To avoid some redundant information in the travel routes, we utilize the skyline concept to retrieve travel routes with more diversity. Specifically, in our paper, we consider some factors, such as the visiting time information of POIs and the set of query points, in retrieving travel routes. These factors could be mapped into dimensional spaces. Then, each travel route is viewed as a data point in the dimensional space. Thus, skyline data points (referred to as skyline travel routes) are returned as the query result. Skyline travel routes could provide more diversity in the query result of trip route recommendations. To evaluate our proposed methods, we conducted extensive experiments on real datasets. The experimental results show that skyline travel routes indeed provide more diversity in the query result. In addition, we evaluate the efficiency of retrieving skyline travel routes.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130584406","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: 30