Consider a group of colleagues going from their offices to their homes, via their preferred subway or bus routes, who wish to find k alternative restaurants to meet and which would minimize a given aggregate deviation distance from their typical routes. We call this the "k-Optimal Meeting Points for Public Transit" (k-OMPPT) query and present two approaches for returning provably correct answers for both SUM and MAX aggregate detour distances. Both approaches exploit geometric properties of the problem in order to refine the POI search space and hence reduce the query's processing time. Our experiments, using real datasets, compare the efficiency of both approaches and show which approach is preferable given the type of aggregate the group is interested in minimizing.
{"title":"Optimal Meeting Points for Public Transit Users","authors":"E. Ahmadi, M. Nascimento","doi":"10.1109/MDM.2018.00017","DOIUrl":"https://doi.org/10.1109/MDM.2018.00017","url":null,"abstract":"Consider a group of colleagues going from their offices to their homes, via their preferred subway or bus routes, who wish to find k alternative restaurants to meet and which would minimize a given aggregate deviation distance from their typical routes. We call this the \"k-Optimal Meeting Points for Public Transit\" (k-OMPPT) query and present two approaches for returning provably correct answers for both SUM and MAX aggregate detour distances. Both approaches exploit geometric properties of the problem in order to refine the POI search space and hence reduce the query's processing time. Our experiments, using real datasets, compare the efficiency of both approaches and show which approach is preferable given the type of aggregate the group is interested in minimizing.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"19 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":"132355601","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}
M. Kjærgaard, M. Werner, Fisayo Caleb Sangogboye, K. Arendt
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.
{"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}
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.
{"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}
In the past years, connectivity has been introduced in automotive production series, enabling vehicles as highly mobile Internet of Things (IoT) sensors and participants. The Horizon 2020 research project AutoMat addressed this scenario by building a vehicle big data marketplace in order to leverage and exploit crowd-sourced sensor data, a so far unexcavated treasure. As part of this project the Common Vehicle Information Model (CVIM) as harmonized data model has been developed. The CVIM allows a common understanding and generic representation, brand-independent throughout the whole data value and processing chain. The demonstrator consists of CVIM vehicle sensor data, which runs through the different components of the whole AutoMat vehicle big data processing pipeline. Finally, at the example of a traffic measurement service the data of a whole vehicle fleet is aggregated and evaluated.
{"title":"The AutoMat CVIM - A Scalable Data Model for Automotive Big Data Marketplaces","authors":"Johannes Pillmann, Benjamin Sliwa, C. Wietfeld","doi":"10.1109/MDM.2018.00052","DOIUrl":"https://doi.org/10.1109/MDM.2018.00052","url":null,"abstract":"In the past years, connectivity has been introduced in automotive production series, enabling vehicles as highly mobile Internet of Things (IoT) sensors and participants. The Horizon 2020 research project AutoMat addressed this scenario by building a vehicle big data marketplace in order to leverage and exploit crowd-sourced sensor data, a so far unexcavated treasure. As part of this project the Common Vehicle Information Model (CVIM) as harmonized data model has been developed. The CVIM allows a common understanding and generic representation, brand-independent throughout the whole data value and processing chain. The demonstrator consists of CVIM vehicle sensor data, which runs through the different components of the whole AutoMat vehicle big data processing pipeline. Finally, at the example of a traffic measurement service the data of a whole vehicle fleet is aggregated and evaluated.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130663760","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}
Benjamin Sliwa, T. Liebig, Robert Falkenberg, Johannes Pillmann, C. Wietfeld
Upcoming Intelligent Traffic Control Systems (ITSCs) will base their optimization processes on crowdsensing data obtained for cars that are used as mobile sensor nodes. In conclusion, public cellular networks will be confronted with massive increases in Machine-Type Communication (MTC) and will require efficient communication schemes to minimize the interference of Internet of Things (IoT) data traffic with human communication. In this demonstration, we present an Open Source framework for context-aware transmission of vehicular sensor data that exploits knowledge about the characteristics of the transmission channel for leveraging connectivity hotspots, where data transmissions can be performed with a high grade if resource efficiency. At the conference, we will present the measurement application for acquisition and live-visualization of the required network quality indicators and show how the transmission scheme performs in real-world vehicular scenarios based on measurement data obtained from field experiments.
{"title":"Resource-Efficient Transmission of Vehicular Sensor Data Using Context-Aware Communication","authors":"Benjamin Sliwa, T. Liebig, Robert Falkenberg, Johannes Pillmann, C. Wietfeld","doi":"10.1109/MDM.2018.00051","DOIUrl":"https://doi.org/10.1109/MDM.2018.00051","url":null,"abstract":"Upcoming Intelligent Traffic Control Systems (ITSCs) will base their optimization processes on crowdsensing data obtained for cars that are used as mobile sensor nodes. In conclusion, public cellular networks will be confronted with massive increases in Machine-Type Communication (MTC) and will require efficient communication schemes to minimize the interference of Internet of Things (IoT) data traffic with human communication. In this demonstration, we present an Open Source framework for context-aware transmission of vehicular sensor data that exploits knowledge about the characteristics of the transmission channel for leveraging connectivity hotspots, where data transmissions can be performed with a high grade if resource efficiency. At the conference, we will present the measurement application for acquisition and live-visualization of the required network quality indicators and show how the transmission scheme performs in real-world vehicular scenarios based on measurement data obtained from field experiments.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121045453","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：