2018 19th IEEE International Conference on Mobile Data Management (MDM)最新文献_第6页

Optimal Meeting Points for Public Transit Users 公共交通用户的最佳集合点

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00017

E. Ahmadi, M. Nascimento

引用次数: 3

DCount - A Probabilistic Algorithm for Accurately Disaggregating Building Occupant Counts into Room Counts DCount——一种精确地将建筑物住户数分解为房间数的概率算法

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00021

M. Kjærgaard, M. Werner, Fisayo Caleb Sangogboye, K. Arendt

{"title":"DCount - A Probabilistic Algorithm for Accurately Disaggregating Building Occupant Counts into Room Counts","authors":"M. Kjærgaard, M. Werner, Fisayo Caleb Sangogboye, K. Arendt","doi":"10.1109/MDM.2018.00021","DOIUrl":"https://doi.org/10.1109/MDM.2018.00021","url":null,"abstract":"Sensing accurately the number of occupants in the rooms of a building enables many important applications for smart building operation and energy management. A range of sensor technologies has been studied and applied to the problem. However, it is costly to achieve high accuracy by instrumenting all rooms in a building with dedicated occupant sensors. In this paper, we propose a new concept for estimating accurate room-level counts of occupants. The idea is to disaggregate accurate building-level counts via existing common sensors available at the room level. This solution is cost-effective as it scales to large buildings without requiring dedicated sensors in each room. We propose an algorithm named DCount that implements this concept. Our results document that DCount can provide room-level counts with a low normalized root mean squared error of 0.93. This is a major improvement compared to a state-of-the-art algorithm using common sensors and ventilation rate measurements resulting in a normalized root mean squared error of 1.54 on the same data set. Further more, we demonstrate how the results enable occupant-driven analysis of plug-load consumption which is one out of many applications using accurate room-level counts of occupants we hope to enable by proposing DCount.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116668774","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: 12

Future Directions for Indoor Information Systems: A Panel Discussion 室内资讯系统的未来发展方向:小组讨论

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00013

D. Zeinalipour-Yazti

{"title":"Future Directions for Indoor Information Systems: A Panel Discussion","authors":"D. Zeinalipour-Yazti","doi":"10.1109/MDM.2018.00013","DOIUrl":"https://doi.org/10.1109/MDM.2018.00013","url":null,"abstract":"Geographic Information Systems (GIS) have enabled a vast range of applications in outdoor spaces, but these systems are bound to accurate localization technologies that are not available inside buildings where people carry 90% of their activities. Additionally, GIS don't address the unique characteristics of complex indoor environments off-the-shelf. At the same time, we witness the uptake of a new class of Indoor Information Systems (IIS), which store indoor spatial models along with sensor signals (e.g., wireless, light and magnetic) used to localize users. Such IIS might be considered as specialized GIS applications that are tailored to the unique challenges pertinent to indoor spaces, namely new indoor data management operators, new indexes, new data privacy schemes, built-in data-driven localization algorithms, models to crowdsource IIS data and these might even use NoSQL architectures. This panel will explore how the academia and industry are tackling the future challenges that rise in the scope of IIS. 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":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126986592","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

The AutoMat CVIM - A Scalable Data Model for Automotive Big Data Marketplaces AutoMat CVIM——汽车大数据市场的可扩展数据模型

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-05-02 DOI: 10.1109/MDM.2018.00052

Johannes Pillmann, Benjamin Sliwa, C. Wietfeld

引用次数: 7

Resource-Efficient Transmission of Vehicular Sensor Data Using Context-Aware Communication 使用上下文感知通信的车辆传感器数据资源高效传输

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-04-16 DOI: 10.1109/MDM.2018.00051

Benjamin Sliwa, T. Liebig, Robert Falkenberg, Johannes Pillmann, C. Wietfeld

引用次数: 1