{"title":"MDM 2018 Sponsors","authors":"","doi":"10.1109/mdm.2018.00011","DOIUrl":"https://doi.org/10.1109/mdm.2018.00011","url":null,"abstract":"","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"32 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":"125533992","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}
Pub Date : 2018-06-01DOI: 10.1007/s10707-019-00369-8
Xiaoyi Fu, Ce Zhang, Hua Lu, Jianliang Xu
{"title":"Efficient Matching of Offers and Requests in Social-Aware Ridesharing","authors":"Xiaoyi Fu, Ce Zhang, Hua Lu, Jianliang Xu","doi":"10.1007/s10707-019-00369-8","DOIUrl":"https://doi.org/10.1007/s10707-019-00369-8","url":null,"abstract":"","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"4 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":"116882078","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}
Thomas F. Olsen, Kasper F. Pedersen, Dennis Rasmussen, K. Torp
Speed is the major killer in traffic. The typical approach to enforce speed limits is by having the police monitor drivers and issue tickets when they are speeding. In this paper, we introduce a new platform where speeding is reduced by nudging. The three major approaches are to warn drivers if they are speeding, praise the drivers if they are driving within the speed limit, and grade each trip. The latter is used to rank drivers, e.g., drivers within a company are ranked according to their trip scores. We present the DriveLaB smartphone app that provides real-time feedback to the drivers. All computations are done at the server-side and we show how to compute real-time feedback and store trip data. In addition, we report on two field trials in the Copenhagen and Aalborg Areas where the platform is tested in collaboration with a major Danish insurance company.
{"title":"DriveLaB: A Platform for Reducing Speeding","authors":"Thomas F. Olsen, Kasper F. Pedersen, Dennis Rasmussen, K. Torp","doi":"10.1109/MDM.2018.00047","DOIUrl":"https://doi.org/10.1109/MDM.2018.00047","url":null,"abstract":"Speed is the major killer in traffic. The typical approach to enforce speed limits is by having the police monitor drivers and issue tickets when they are speeding. In this paper, we introduce a new platform where speeding is reduced by nudging. The three major approaches are to warn drivers if they are speeding, praise the drivers if they are driving within the speed limit, and grade each trip. The latter is used to rank drivers, e.g., drivers within a company are ranked according to their trip scores. We present the DriveLaB smartphone app that provides real-time feedback to the drivers. All computations are done at the server-side and we show how to compute real-time feedback and store trip data. In addition, we report on two field trials in the Copenhagen and Aalborg Areas where the platform is tested in collaboration with a major Danish insurance company.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"55 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":"121475638","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}
{"title":"MDM 2018 Program Committee","authors":"","doi":"10.1109/mdm.2018.00010","DOIUrl":"https://doi.org/10.1109/mdm.2018.00010","url":null,"abstract":"","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"112 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132462663","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}
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.
{"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}
{"title":"Title Page i","authors":"","doi":"10.1109/mdm.2018.00001","DOIUrl":"https://doi.org/10.1109/mdm.2018.00001","url":null,"abstract":"","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"43 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":"133489446","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}
With the prevalence of location-based services and geo-functioned devices, the trend of spatial data outsourcing is rising. In the data outsourcing scenario, result integrity must be ensured by means of a query authentication scheme. However, most of the existing studies are confined to a centralized environment. In this paper, we investigate the query authentication problem in distributed environments and focus on the k nearest neighbor (kNN) query, which is widely used in spatial data analytics. We design a new distributed spatial authenticated data structure (ADS), distributed MR-tree, to facilitate efficient kNN processing. Furthermore, we propose a basic algorithm to process authenticated kNN queries based on the new ADS. Apart from the results, some verification objects are generated to guarantee the results' integrity. We also design two optimized algorithms to reduce the size of verification objects as well as the verification cost. Our experiments validate the good performance of the proposed techniques in terms of query cost, communication overhead, and verification time.
{"title":"Distributed kNN Query Authentication","authors":"Cheng Xu, Jianliang Xu, Byron Choi","doi":"10.1109/MDM.2018.00034","DOIUrl":"https://doi.org/10.1109/MDM.2018.00034","url":null,"abstract":"With the prevalence of location-based services and geo-functioned devices, the trend of spatial data outsourcing is rising. In the data outsourcing scenario, result integrity must be ensured by means of a query authentication scheme. However, most of the existing studies are confined to a centralized environment. In this paper, we investigate the query authentication problem in distributed environments and focus on the k nearest neighbor (kNN) query, which is widely used in spatial data analytics. We design a new distributed spatial authenticated data structure (ADS), distributed MR-tree, to facilitate efficient kNN processing. Furthermore, we propose a basic algorithm to process authenticated kNN queries based on the new ADS. Apart from the results, some verification objects are generated to guarantee the results' integrity. We also design two optimized algorithms to reduce the size of verification objects as well as the verification cost. Our experiments validate the good performance of the proposed techniques in terms of query cost, communication overhead, and verification time.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"85 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":"133486624","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}
{"title":"Publisher's Information","authors":"","doi":"10.1109/mdm.2018.00059","DOIUrl":"https://doi.org/10.1109/mdm.2018.00059","url":null,"abstract":"","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"27 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":"122007749","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}
Michael R. Entin, Colin M. Heirichs, A. Peine, Evan R. Warych, J. Timmerman, Shane Cresmore
We present a prototype system for efficient management of a fleet of snow plowing trucks. Given the severe impacts that winter weather can have on a traffic in both rural and urban areas, it is a paramount to ensure an effective utilization of the available trucks from multiple perspectives. Namely, the activities needed may involve a combination of snow cleaning, salt dispensing and ice removal from the roads – and the knowledge of the current operational status of the vehicles in the fleet is what will constitute a decisive factor in optimizing the time for improving driving conditions on various road segments. In addition, one needs to account for (re)supplying of the trucks with the materials (e.g., salt, sand) that are to be dispensed. The project is developed for an actual industry-partner (Henderson Products) and its current state and features are what we will demonstrate.
{"title":"Data Analytics for Snow Plow Trucks Fleet","authors":"Michael R. Entin, Colin M. Heirichs, A. Peine, Evan R. Warych, J. Timmerman, Shane Cresmore","doi":"10.1109/MDM.2018.00057","DOIUrl":"https://doi.org/10.1109/MDM.2018.00057","url":null,"abstract":"We present a prototype system for efficient management of a fleet of snow plowing trucks. Given the severe impacts that winter weather can have on a traffic in both rural and urban areas, it is a paramount to ensure an effective utilization of the available trucks from multiple perspectives. Namely, the activities needed may involve a combination of snow cleaning, salt dispensing and ice removal from the roads – and the knowledge of the current operational status of the vehicles in the fleet is what will constitute a decisive factor in optimizing the time for improving driving conditions on various road segments. In addition, one needs to account for (re)supplying of the trucks with the materials (e.g., salt, sand) that are to be dispensed. The project is developed for an actual industry-partner (Henderson Products) and its current state and features are what we will demonstrate.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"77 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":"129307921","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}
S. Hosseini, Hongzhi Yin, Meihui Zhang, Y. Elovici, Xiaofang Zhou
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.
{"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}
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