2014 IEEE 15th International Conference on Mobile Data Management最新文献_第3页

ToSS-it: A Cloud-Based Throwaway Spatial Index Structure for Dynamic Location Data ToSS-it:一种基于云的动态位置数据一次性空间索引结构

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.37

Afsin Akdogan, C. Shahabi, Ugur Demiryurek

{"title":"ToSS-it: A Cloud-Based Throwaway Spatial Index Structure for Dynamic Location Data","authors":"Afsin Akdogan, C. Shahabi, Ugur Demiryurek","doi":"10.1109/MDM.2014.37","DOIUrl":"https://doi.org/10.1109/MDM.2014.37","url":null,"abstract":"The widespread use of GPS-enabled devices have led to a number of emerging applications that require monitoring and querying a large number of moving objects, such as in location-based services, mobile phone social networking, UAV surveillance, and car navigation systems. In such applications, indexes for moving objects must support queries efficiently and also cope with frequent updates. In this paper, we propose a cloud-based throwaway index structure, dubbed ToSS-it, where we generate the index from scratch in a short period of time rather than updating it with every location change of the moving objects. ToSS-it employs inter-node and intra-node multi-core parallelism paradigm to rapidly construct a distributed Voronoi Diagram. ToSS-it scales out by using a voronoi partitioning technique that minimizes the network message exchanges between the nodes (i.e., The major overhead in parallel generation of Voronoi Diagrams), and scales up since it fully exploits the multi-core CPUs available on each server. As a comparison point, with the state-of-the-art cloud-based spatial index structure (RT-CAN), if at least 7% of the objects are moving and issue updates to the index, it is faster to recreate ToSS-it from scratch than updating RT-CAN.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"11 5 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":"130319265","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: 13

A Demonstration of the NITOS BikesNet Framework NITOS BikesNet框架演示

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.55

Giannis Kazdaridis, D. Stavropoulos, Stavros Ioannidis, T. Korakis, S. Lalis, L. Tassiulas

引用次数: 3

Enabling Location-Based Services 2.0: Challenges and Opportunities 启用基于位置的服务2.0:挑战与机遇

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.45

Saket K. Sathe, R. Melamed, Peter Bak, S. Kalyanaraman

引用次数: 2

Hybrid Queries over Symbolic and Spatial Trajectories: A Usage Scenario 符号和空间轨迹的混合查询:一个使用场景

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.49

M. Damiani, H. Issa, R. H. Güting, Fabio Valdés

引用次数: 11

Real-Time QoS Monitoring for Cloud-Based Big Data Analytics Applications in Mobile Environments 移动环境下基于云的大数据分析应用的实时QoS监控

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.74

Khalid Alhamazani, R. Ranjan, P. Jayaraman, Karan Mitra, Meisong Wang, Zhiqiang George Huang, Lizhe Wang, F. Rabhi

引用次数: 15

Estimating Common Pedestrian Routes through Indoor Path Networks Using Position Traces 利用位置轨迹估算室内路径网络中常见的行人路线

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.11

Thor S. Prentow, H. Blunck, Kaj Grønbæk, M. Kjærgaard

{"title":"Estimating Common Pedestrian Routes through Indoor Path Networks Using Position Traces","authors":"Thor S. Prentow, H. Blunck, Kaj Grønbæk, M. Kjærgaard","doi":"10.1109/MDM.2014.11","DOIUrl":"https://doi.org/10.1109/MDM.2014.11","url":null,"abstract":"Accurate information about how people commonly travel in a given large-scale building environment and which routes they take for given start and destination points is essential for applications such as indoor navigation, route prediction, and mobile work planning and logistics. In this paper, we propose methods for detecting commonly used routes by robust aggregation, clustering, and merging of indoor position traces. The developed methods overcome three specific challenges for detecting commonly used routes in an indoor setting based on position data: i) a high ratio between path-density and positioning-accuracy, ii) a flat path hierarchy, and iii) providing cost-effective scalability. Through an evaluation based on data collected by staff members at a hospital covering more than 10 hectare over three floors, we show that the proposed methods detect routes that are representative of the commonly used routes between locations. These methods are sufficiently efficient to provide common routes based on real-time data from thousands of devices simultaneously. Furthermore, we show that the methods operate robustly even on basis of noisy and coarse-grained position estimates as provided by large-scale deployable indoor Wi-Fi positioning systems, and with no prior information on building layout.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"44 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":"130554456","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: 11

Mobile Big Data Analytics: Research, Practice, and Opportunities 移动大数据分析:研究、实践和机遇

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.73

D. Zeinalipour-Yazti, S. Krishnaswamy

{"title":"Mobile Big Data Analytics: Research, Practice, and Opportunities","authors":"D. Zeinalipour-Yazti, S. Krishnaswamy","doi":"10.1109/MDM.2014.73","DOIUrl":"https://doi.org/10.1109/MDM.2014.73","url":null,"abstract":"The rapid expansion of broadband mobile networks by Telecom Operators, has introduced a versatile global infrastructure that internally generates vast amounts of spatio-temporal network-level data (e.g., User id, location, device type, etc.) At the same time, mobile app vendors have nowadays at their fingertips massive amounts of app-level data collected through implicit or explicit crowd sourcing schemes with multi-sensing smartphones that have become a commodity. Mobile big data analytics refers to the discovery of previously unknown meaningful patterns and knowledge from a few dozen terabytes to many petabytes of data collected from mobile users at the network-level or the app-level. Example analytics range from high-level metrics and summaries (e.g., Through clustering, classification and association rule mining) useful to executive managers to alert-based analytics (e.g., Anomaly detection) useful to front-line engineers and users. This panel will explore how the academia and industry are tackling mobile big data analytic challenges. It will also identify and debate the key challenges and opportunities, in terms of applications, queries, architectures, to which the mobile data management and mobile data mining communities should contribute to.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"33 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":"116276426","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: 28

Exploring Location-Related Data on Smart Phones for Activity Inference 探索智能手机上的位置相关数据以进行活动推断

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.71

Xiao Wen Ruan, Shou Chung Lee, Wen-Chih Peng

引用次数: 5

A Framework for Continuous Group Activity Recognition Using Mobile Devices: Concept and Experimentation 使用移动设备的连续群体活动识别框架:概念和实验

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.62

Amin Bakhshandehabkenar, S. Loke, J. Rahayu

{"title":"A Framework for Continuous Group Activity Recognition Using Mobile Devices: Concept and Experimentation","authors":"Amin Bakhshandehabkenar, S. Loke, J. Rahayu","doi":"10.1109/MDM.2014.62","DOIUrl":"https://doi.org/10.1109/MDM.2014.62","url":null,"abstract":"Group Activity Recognition (GAR) is a challenging research area in context-aware computing which has attracted much attention recently. Many studies have been conducted in the field of activity recognition (AR) along with their applications in domains such as health, smart homes, daily living and life logging. However, still many open issues exist. Lack of an energy-efficient approach is one of the most vital issues in the context of AR. GAR work often suffers from energy consumption issues for the reason that, apart from AR process, there is the requirement to have more interaction among members of the group and a need to run more complex recognition processes. Moreover, almost all work in GAR are technology-oriented and assume that our real-life environment remains fixed once the system has been established, but this may not be the case. Hence, we propose a framework called Group Sense for GAR towards addressing these issues. Also, a relatively simple scheme for GAR, with a protocol for the exchange of information required for GAR, has been implemented, tested and evaluated. We then conclude with lessons learnt for GAR.","PeriodicalId":322071,"journal":{"name":"2014 IEEE 15th International Conference on Mobile Data Management","volume":"35 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":"133705642","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

Using Location-Based Social Networks for Time-Constrained Information Dissemination 基于位置的社交网络在时间约束下的信息传播

2014 IEEE 15th International Conference on Mobile Data Management Pub Date : 2014-07-14 DOI: 10.1109/MDM.2014.26

Juliana Litou, Ioannis Boutsis, V. Kalogeraki

引用次数: 5