2015 16th IEEE International Conference on Mobile Data Management最新文献_第3页

Mining Risk Factors in RFID Baggage Tracking Data 挖掘RFID行李追踪数据中的风险因素

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.31

Tanvir Ahmed, T. Calders, T. Pedersen

{"title":"Mining Risk Factors in RFID Baggage Tracking Data","authors":"Tanvir Ahmed, T. Calders, T. Pedersen","doi":"10.1109/MDM.2015.31","DOIUrl":"https://doi.org/10.1109/MDM.2015.31","url":null,"abstract":"Airport baggage management is a significant part of the aviation industry. However, for several reasons every year a vast number of bags are mishandled (e.g., Left behind, send to wrong flights, gets lost, etc.,) which costs a lot of money to the aviation industry as well as creates inconvenience and frustration to the passengers. To remedy these problems we propose a detailed methodology for mining risk factors from Radio Frequency Identification (RFID) baggage tracking data. The factors should identify potential issues in the baggage management. However, the baggage tracking data are low level and not directly accessible for finding such factors. Moreover, baggage tracking data are highly imbalanced, for example, our experimental data, which is a large real-world data set from the Scandinavian countries, contains only 0.8% mishandled bags. This imbalance presents difficulties to most data mining techniques. The paper presents detailed steps for pre-processing the unprocessed raw tracking data for higher-level analysis and handling the imbalance problem. We fragment the data set based on a number of relevant factors and find the best classifier for each of them. The paper reports on a comprehensive experimental study with real RFID baggage tracking data and it shows that the proposed methodology results in a strong classifier, and can find interesting concrete patterns and reveal useful insights of the data.","PeriodicalId":187331,"journal":{"name":"2015 16th IEEE International Conference on Mobile Data Management","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129879579","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

An Analysis of the Walking Environmental Factors Affecting the Stress of Pedestrians for Route Recommendation 影响行人压力的步行环境因素分析及路径推荐

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.27

Hiroki Kitabayashi, Xinpeng Zhang, Yasuhito Asano, Masatoshi Yoshikawa

{"title":"An Analysis of the Walking Environmental Factors Affecting the Stress of Pedestrians for Route Recommendation","authors":"Hiroki Kitabayashi, Xinpeng Zhang, Yasuhito Asano, Masatoshi Yoshikawa","doi":"10.1109/MDM.2015.27","DOIUrl":"https://doi.org/10.1109/MDM.2015.27","url":null,"abstract":"Walking is an easy exercise for maintaining health, although there are several environmental factors, such as road congestion or slopes, which are stressful for pedestrians, especially elders. Therefore, a pedestrian navigation system is required to recommend a less stressful route for a user by estimating such environmental factors. Towards realizing such a system, we firstly collect vital sign, spatiotemporal and environmental data from pedestrians, and secondly analyze the relation between them, and then propose models for estimating the personal stress of a pedestrian. As our vital sign data, we calculate the stress of each pedestrian from the R-R interval data obtained by a heart rate sensor. Our spatiotemporal data includes the walking speed of pedestrians and road grade calculated from GPS sensor data. We measure the degree of road congestion, as our environmental data, using a laser range finder. Our analysis reveals that the road congestion is an important factor affecting stress in walking. The proposed models based on neural networks employ the road congestion in addition to the vital sign and spatiotemporal data. Experimental results indicate that the road congestion degree is effective and the stress of pedestrians estimated by our models is similar to the real stress.","PeriodicalId":187331,"journal":{"name":"2015 16th IEEE International Conference on Mobile Data Management","volume":"1968 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129940834","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

The PersonaCheck System for Personalizing M-Commerce Checkout Processes PersonaCheck系统用于个性化移动商务结账流程

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.58

Argyris Constantinides, Marios Belk, Panagiotis Germanakos, G. Samaras

引用次数: 3

Efficient Computation of Group Optimal Sequenced Routes in Road Networks 道路网络中群体最优顺序路线的高效计算

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.68

Samiha Samrose, T. Hashem, Sukarna Barua, Mohammed Eunus Ali, Mohammad Hafiz Uddin, Md. Iftekhar Mahmud

引用次数: 22

Scalable Mockup Experiments on Smartphones Using Smart Lab 使用Smart Lab在智能手机上进行可扩展的模型实验

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.21

Georgios Larkou, Marios Mintzis, P. Andreou, Andreas Konstantinidis, D. Zeinalipour-Yazti

引用次数: 0

Making Sense of Trajectory Data in Indoor Spaces 利用室内空间的轨迹数据

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.44

Thor S. Prentow, Andreas Thom, H. Blunck, J. Vahrenhold

{"title":"Making Sense of Trajectory Data in Indoor Spaces","authors":"Thor S. Prentow, Andreas Thom, H. Blunck, J. Vahrenhold","doi":"10.1109/MDM.2015.44","DOIUrl":"https://doi.org/10.1109/MDM.2015.44","url":null,"abstract":"The increasing prevalence of positioning and tracking systems has helped simplify tracking large amounts of, e.g., People moving through buildings or cars traveling on roads, over long periods of time. However, technical limitations of positioning algorithms and traditional sensing infrastructures are likely, especially indoors, to induce errors and biases in the resulting data. In particular, the resulting motion trajectories often do not conform perfectly to the underlying route network. As a consequence, analyses of trajectory sets are impeded by these phenomena, as it becomes hard to identify which route was taken in a particular travel instance or whether two travel instances followed the same route. In this paper, we present a bootstrapping approach and several algorithms to mitigate error biases and related phenomena, focusing on indoor scenarios. In particular, we are able to estimate and iteratively refine an underlying route network from a set of motion trajectories. Secondly, we represent sub trajectories, i.e., Movements on individual elements of the route network, by their median sub trajectory. The resulting aggregated and cleaned-up data set facilitates using further, domain-specific analysis tools. Additionally, it allows to predict the locally occurring expected positioning error biases. This in turn allows improved positioning, e.g., For real-time navigation assistance scenarios. We evaluate the proposed methods using trajectory data from employees at a large hospital complex. In particular, we show that we can reconstruct the hospital's route network accurately, and that we can furthermore extract median sub trajectories for almost all individual corridors. Finally, we illustrate that median trajectories deliver useful deviation maps to learn, and correct for, the expected local biases in positioning.","PeriodicalId":187331,"journal":{"name":"2015 16th IEEE International Conference on Mobile Data Management","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125720472","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: 14

Resource Allocation for Heterogeneous Traffic in LTE Virtual Networks LTE虚拟网络中异构业务的资源分配

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.34

Ayman Abdelhamid, P. Krishnamurthy, D. Tipper

{"title":"Resource Allocation for Heterogeneous Traffic in LTE Virtual Networks","authors":"Ayman Abdelhamid, P. Krishnamurthy, D. Tipper","doi":"10.1109/MDM.2015.34","DOIUrl":"https://doi.org/10.1109/MDM.2015.34","url":null,"abstract":"Cellular network virtualization is being considered as a key trend in future mobile networks towards improved resource utilization. However, virtualization scenarios need investigation to understand the considerations which should be taken into account when deploying virtualized wireless networks in practice. Towards this, we address the performance of a virtualized network in the presence of heterogeneous classes of traffic. In previous cellular network virtualization literature, both Real time (RT) and Non-Real time (NRT) traffic requests have been included without distinction. Both types are provisioned using the same algorithm for allocation of resources specified by the Network Scheduler [1]. However, different types of traffic have different characteristics [2], e.g., RT requests are delay sensitive but may need fixed bandwidth, and hence should be treated differently, especially when wireless channel conditions are factored into the scheduling. We recognize this difference and in this paper, we propose a new approach to improve scheduling of resources for RT and NRT traffic. In particular, we prioritize the traffic belonging to different virtual slices from all service providers (SP/VEs) at the Network Scheduler before allocating resources to different SP/VEs, i.e., We form a Virtual Prioritized Slice (VPS). The virtual prioritized slice is forwarded to the VPS scheduler to serve all RT requests first. Only after the RT traffic is scheduled, the NRT traffic is provisioned using proportional fairness (PF) scheduling. We show by simulation results that this new VPS approach helps outperform recently proposed resource allocation schemes.","PeriodicalId":187331,"journal":{"name":"2015 16th IEEE International Conference on Mobile Data Management","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131360756","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

Internet of Things: Challenges and State-of-the-Art Solutions in Internet-Scale Sensor Information Management and Mobile Analytics 物联网:互联网级传感器信息管理和移动分析的挑战和最新解决方案

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.72

A. Zaslavsky, Dimitrios Georgakopoulos

引用次数: 42

Continuous Median Queries in Wireless Sensor Networks 无线传感器网络中的连续中值查询

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.48

Khaled Ammar, M. Nascimento

引用次数: 1

RECATHON: A Middleware for Context-Aware Recommendation in Database Systems 数据库系统中上下文感知推荐的中间件

2015 16th IEEE International Conference on Mobile Data Management Pub Date : 2015-06-15 DOI: 10.1109/MDM.2015.63

Mohamed Sarwat, James L. Avery, M. Mokbel

{"title":"RECATHON: A Middleware for Context-Aware Recommendation in Database Systems","authors":"Mohamed Sarwat, James L. Avery, M. Mokbel","doi":"10.1109/MDM.2015.63","DOIUrl":"https://doi.org/10.1109/MDM.2015.63","url":null,"abstract":"This paper presents RECATHON, a context-aware recommender system built entirely inside a database system. Unlike traditional recommender systems that are context-free where they support the general query of Recommend movies for a certain user, RECATHON users can request recommendations based on their age, location, gender, or any other contextual/ demographical/preferential user attribute. A main challenge of supporting such kind of recommenders is the difficulty of deciding what attributes to build recommenders on. RECATHON addresses this challenge as it supports building recommenders in database systems in an analogous way to building index structures. Users can decide to create recommenders on selected attributes, e.g., Age and/or gender, and then entertain efficient support of multidimensional recommenders on the selected attributes. RECATHON employs a multi-dimensional index structure for each built recommender that can be accessed using novel query execution algorithms to support efficient retrieval for recommender queries. Experimental results based on an actual prototype of RECATHON, built inside Postgre SQL, using real Movie Lens and Foursquare data show that RECATHON exhibits real time performance for large-scale multidimensional recommendation.","PeriodicalId":187331,"journal":{"name":"2015 16th IEEE International Conference on Mobile Data Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132611068","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