2018 19th IEEE International Conference on Mobile Data Management (MDM)最新文献_第5页

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2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/mdm.2018.00011

引用次数: 0

Efficient Matching of Offers and Requests in Social-Aware Ridesharing 基于社会意识的拼车服务的有效匹配

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1007/s10707-019-00369-8

Xiaoyi Fu, Ce Zhang, Hua Lu, Jianliang Xu

引用次数: 8

DriveLaB: A Platform for Reducing Speeding DriveLaB:一个减少超速的平台

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00047

Thomas F. Olsen, Kasper F. Pedersen, Dennis Rasmussen, K. Torp

引用次数: 1

MDM 2018 Program Committee MDM 2018项目委员会

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/mdm.2018.00010

引用次数: 0

Outlier Detection for Multidimensional Time Series Using Deep Neural Networks 基于深度神经网络的多维时间序列离群点检测

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00029

Tung Kieu, B. Yang, Christian S. Jensen

{"title":"Outlier Detection for Multidimensional Time Series Using Deep Neural Networks","authors":"Tung Kieu, B. Yang, Christian S. Jensen","doi":"10.1109/MDM.2018.00029","DOIUrl":"https://doi.org/10.1109/MDM.2018.00029","url":null,"abstract":"Due to the continued digitization of industrial and societal processes, including the deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered observations, known as time series. For example, the behavior of drivers can be captured by GPS or accelerometer as a time series of speeds, directions, and accelerations. We propose a framework for outlier detection in time series that, for example, can be used for identifying dangerous driving behavior and hazardous road locations. Specifically, we first propose a method that generates statistical features to enrich the feature space of raw time series. Next, we utilize an autoencoder to reconstruct the enriched time series. The autoencoder performs dimensionality reduction to capture, using a small feature space, the most representative features of the enriched time series. As a result, the reconstructed time series only capture representative features, whereas outliers often have non-representative features. Therefore, deviations of the enriched time series from the reconstructed time series can be taken as indicators of outliers. We propose and study autoencoders based on convolutional neural networks and long-short term memory neural networks. In addition, we show that embedding of contextual information into the framework has the potential to further improve the accuracy of identifying outliers. We report on empirical studies with multiple time series data sets, which offers insight into the design properties of the proposed framework, indicating that it is effective at detecting outliers.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"1 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":"129892585","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: 136

Title Page i 第1页

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/mdm.2018.00001

引用次数: 0

Distributed kNN Query Authentication 分布式kNN查询认证

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00034

Cheng Xu, Jianliang Xu, Byron Choi

引用次数: 4

Publisher's Information 出版商的信息

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/mdm.2018.00059

引用次数: 0

Data Analytics for Snow Plow Trucks Fleet 雪犁卡车车队的数据分析

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00057

Michael R. Entin, Colin M. Heirichs, A. Peine, Evan R. Warych, J. Timmerman, Shane Cresmore

引用次数: 0

Mining Subgraphs from Propagation Networks through Temporal Dynamic Analysis 基于时间动态分析的传播网络子图挖掘

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00023

S. Hosseini, Hongzhi Yin, Meihui Zhang, Y. Elovici, Xiaofang Zhou

{"title":"Mining Subgraphs from Propagation Networks through Temporal Dynamic Analysis","authors":"S. Hosseini, Hongzhi Yin, Meihui Zhang, Y. Elovici, Xiaofang Zhou","doi":"10.1109/MDM.2018.00023","DOIUrl":"https://doi.org/10.1109/MDM.2018.00023","url":null,"abstract":"An alarm is raised due to a defect in a transportation system. Given a graph over which the alarms propagate, we aim to exploit a set of subgraphs with highly correlated nodes (or entities). The edge weight between each pair of entities can be computed using the temporal dynamics of the propagation process. We retrieve the top k edge weights and each group of connected entities can consequently form a tightly coupled subgraph. However, numerous challenges abound. First, the textual contents associated with the alarms of the same type differ during the propagation process. Hence, in the lack of textual data, the temporal information can only be employed to compute the correlation weights. Second, in many scenarios, the same alarm does not propagate. Third, given a pair of entities, the propagation can occur in both directions. Most of the prior work only consider the time-window and assume that the propagation between a pair of entities occurs sequentially. But, the propagation process should be inferred using miscellaneous temporal features. Therefore, we devise a generative approach that, on the one hand, utilizes infinite temporal latent factors (e.g. hour, day, and etc.) to compute the correlation weights, and on the other hand, analyzes how an alarm in one entity can cause a set of alarms in another. We also conduct an extensive set of experiments to compare the performance of the subgraph mining methods. The results show that our unified framework can effectively exploit the tightly coupled subgraphs.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"39 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":"132274031","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: 17