2019 20th IEEE International Conference on Mobile Data Management (MDM)最新文献_第2页

Towards Usability on Reverse Top-k Geo-Social Keyword Query Results 反向Top-k地理社会关键字查询结果的可用性研究

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-16

Pengfei Jin

引用次数: 1

Activity-Based Shared Mobility Model for Smart Transportation 基于活动的智能交通共享出行模型

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00126

San Yeung, H. M. A. Aziz, S. Madria

引用次数: 4

Toward Efficient Processing of Spatio-Temporal Workloads in a Distributed In-Memory System 分布式内存系统中时空工作负载的高效处理

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-66

Puya Memarzia, Maria Patrou, M. Alam, S. Ray, V. Bhavsar, K. Kent

{"title":"Toward Efficient Processing of Spatio-Temporal Workloads in a Distributed In-Memory System","authors":"Puya Memarzia, Maria Patrou, M. Alam, S. Ray, V. Bhavsar, K. Kent","doi":"10.1109/MDM.2019.00-66","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-66","url":null,"abstract":"Location-based services (LBS) are a widely adopted technology that produces large volumes of spatio-temporal data at high velocity. Spatial data is also being generated from many other geo-spatial applications. To address the challenge of data volume, a number of big spatial data management systems have emerged that are based on the MapReduce paradigm. Recent projects have developed spatial data systems using Spark's distributed in-memory architecture. These projects, which include GeoSpark, SpatialSpark, and LocationSpark, do not support the high update rates required by LBS applications. Alternatively, systems such as MD-HBase support data updates, but are hindered by the performance characteristics of HBase, which is a disk-oriented framework. We present DISTIL+, a distributed spatio-temporal data processing system designed for high velocity location data. Our system achieves high update throughput and low query latency by leveraging the APGAS (Asynchronous Partitioned Global Address Space) architecture to build a multi-level distributed in-memory index. We present extensive experimental evaluation of our system, comparing several indexing and data placement schemes, as well as competing systems. Our results show that DISTIL+ excels at supporting high throughput location updates, and low latency spatio-temporal range queries and kNN queries, while offering better performance than existing approaches.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125427503","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

A Framework for Constrained Graph Partitioning 约束图划分框架

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-21

Lefteris Ntaflos

引用次数: 0

Outdoor Localization Framework with Telco Data 户外定位框架与电信数据

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-14

Yige Zhang

引用次数: 1

k-Collective Influential Facility Placement Over Moving Object k-集体影响设施放置在移动物体上

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-57

Dan Li, Hui Li, Meng Wang, Jiangtao Cui

{"title":"k-Collective Influential Facility Placement Over Moving Object","authors":"Dan Li, Hui Li, Meng Wang, Jiangtao Cui","doi":"10.1109/MDM.2019.00-57","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-57","url":null,"abstract":"In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate locations, a group of moving objects, each of which is associated with a collection of reference points, as well as a budget k, we aim to mine a group of k locations, the combination of whom can influence the most number of moving objects. We show that this problem is NP-hard and present a basic hill-climb algorithm, namely GreedyP. We prove this method with (1 - 1/e ) approximation ratio. One core challenge is to identify and reduce the overlap of the influence from different selected locations to maximize the marginal benefits. Therefore, the GreedyP approach may be very costly when the number of moving objects is large. In order to address the problem, we also propose another GreedyPS algorithm based on FM-sketch technique, which maps the moving objects to bitmaps such that the marginal benefit can be easily observed through bit-wise operations. Through this way, we are able to save more than a half running time while preserving the result quality. Experiments on real datasets verify the efficiency and effectiveness for both algorithms we propose in this paper.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"10 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126130219","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

The Use of Citizen Science in the Characterization of the Lyon's Urban Heat and Cool Islands 公民科学在里昂城市冷热岛表征中的应用

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-18

L. Alonso

引用次数: 1

Neural Network for Predicting Error of AP Location Estimation Method Using Crowdsourced Wi-Fi Fingerprints 基于众包Wi-Fi指纹的AP位置估计方法误差预测神经网络

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.000-9

Changmin Sung, Dongsoo Han

引用次数: 1

STCNN: A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction 用于长期交通预测的时空卷积神经网络

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-53

Zhixiang He, Chi-Yin Chow, Jiadong Zhang

引用次数: 45

Bluetooth Mesh Networks for Indoor Localization 用于室内定位的蓝牙Mesh网络

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-10 DOI: 10.1109/MDM.2019.00-13

Martin Jürgens, Dennis Meis, Dominik Möllers, Felix Nolte, Etienne Stork, Gottfried Vossen, Christian Werner, Hendrik Winkelmann

{"title":"Bluetooth Mesh Networks for Indoor Localization","authors":"Martin Jürgens, Dennis Meis, Dominik Möllers, Felix Nolte, Etienne Stork, Gottfried Vossen, Christian Werner, Hendrik Winkelmann","doi":"10.1109/MDM.2019.00-13","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-13","url":null,"abstract":"Indoor Localization Systems (ILS) are increasingly applied in various use cases, such as tracking the position and movement of staff or equipment within buildings. Creating and applying these systems requires decisions about measuring and transmitting signals, the algorithm transforming signals into position estimations as well as the representation displayed to the user. One approach gaining significance recently in this context are Mesh Networks. A Mesh Network enables single nodes to exchange information with other nodes directly and dynamically, increases the error-tolerance, and facilitates the setup by reducing installation overhead. This paper studies the applicability of Bluetooth Mesh Networking technology for ILS by means of an experimental setup. The quality of localization is evaluated depending on different inference approaches as well as the scalability of the Bluetooth Mesh Technology. As initial results show, ILS based on Bluetooth Mesh Networks provide similar results regarding the accuracy of localization compared to other underlying technologies, such as Wi-Fi mesh networks, and can be set up with lower effort and costs. However, the underlying Bluetooth technology limits the scalability of the mesh network, especially in case of permanent localization of nodes.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122279105","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: 10