2019 20th IEEE International Conference on Mobile Data Management (MDM)最新文献_第4页

Utility-Time Social Event Planning on EBSN 基于EBSN的效用时间社会事件策划

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-58

Linlin Ding, Hanlin Zhang, Ze Chen, Baoyan Song

{"title":"Utility-Time Social Event Planning on EBSN","authors":"Linlin Ding, Hanlin Zhang, Ze Chen, Baoyan Song","doi":"10.1109/MDM.2019.00-58","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-58","url":null,"abstract":"At present, event-based social network (EBSN) platforms are becoming more and more popular, which main function is to arrange appropriate social activities for interested users. The existing methods usually assume that each user can participate in a limited number of events and solve the spatio-temporal conflicts caused by the limited number of events. However, in practical applications, the existing methods emerge the following problems: (1) they don't estimate the time cost caused by travel distance; (2) the constraint of the limiting number of users participating events and the schedule of users is not accurate enough. Therefore, first, we combine the position information and propose RDP algorithm to provide personalized event planning based on considering the free time of users, the average moving speed of users, the interest value of users as a whole, which ensures the approximate ratio of our algorithm. Second, we present RGPV and the RGPT algorithms to reduce the running time and improve the efficiency of time and space, so as to ensure each user can participate in the events on time. Finally, the experiments based on the real dataset can show that the proposed algorithms are effective and efficient.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129540769","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: 0

Geometric Top-k Processing: Updates Since MDM'16 [Advanced Seminar] 几何Top-k处理:自MDM'16以来的更新[高级研讨会]

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-81

K. Mouratidis

引用次数: 1

A Martingale-Based Approach for Flight Behavior Anomaly Detection 基于鞅的飞行行为异常检测方法

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-75

S. Ho, Matthew Schofield, Bo Sun, Jason Snouffer, J. Kirschner

{"title":"A Martingale-Based Approach for Flight Behavior Anomaly Detection","authors":"S. Ho, Matthew Schofield, Bo Sun, Jason Snouffer, J. Kirschner","doi":"10.1109/MDM.2019.00-75","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-75","url":null,"abstract":"The timely detection of anomalous flight behavior is critical to ensure a prompt and appropriate response to mitigate any dangers to flight safety or hindrance of logistics operations. Most previous approaches focused on anomaly detection, leading them to only be able to raise an alert after an occurrence of an anomaly. A more effective approach is to predict a potential anomaly based on current observations, thus cutting down on detection time and allowing for a more expedient response. We propose a novel martingale-based approach to predict anomalous flight behavior in the near future as data points are observed one by one in real-time. The proposed anomaly prediction method consists of two components: (i) utilization of regression to model the historical full flight behavior and (ii) monitoring of the real-time flight behavior using a martingale (stochastic) process. The latter component consists of two prediction steps: (i) first to predict future values of multiple target variables (e.g., latitude, longitude, and altitude) using regression models, and (ii) then to decide whether the predicted values exhibit anomalies. In particular, our proposed method uses martingale tests on multiple Gaussian process regression (GPR) predictive models of target variables. The main advantages of the proposed method are: (i) the use of multiple martingale tests allows one to have a tighter false positive bound for anomaly detection/prediction, and (ii) the prediction steps reduce the delay time for anomaly detection. Experimental results on real-world data show that the performance (mean delay time, recall, and precision) of our proposed approach is competitive against other compared methods.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114529147","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

Publishing Sensitive Trajectory Data Under Enhanced l-Diversity Model 增强l-Diversity模型下敏感轨迹数据的发布

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-61

Lin Yao, Xinyu Wang, Xin Wang, Haibo Hu, Guowei Wu

引用次数: 13

MISCELA: Discovering Correlated Attribute Patterns in Time Series Sensor Data 发现时间序列传感器数据中的相关属性模式

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-72

Kei Harada, Yuya Sasaki, Makoto Onizuka

{"title":"MISCELA: Discovering Correlated Attribute Patterns in Time Series Sensor Data","authors":"Kei Harada, Yuya Sasaki, Makoto Onizuka","doi":"10.1109/MDM.2019.00-72","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-72","url":null,"abstract":"The urban condition is monitored by a wide variety of sensors with several attributes such as temperature and traffic volume. It is expected to discover the correlated attributes to accurately analyze and understand the urban condition. Several mining techniques for spatio-temporal data have been proposed for discovering the sets of sensors that are spatially close to each other and temporally correlated in their measurements. However, they cannot discover correlated attributes efficiently because their targets are correlated sensors with a single attribute. In this paper, we introduce a problem of discovering correlations among multiple attributes, which we call correlated attribute pattern (CAP) mining. Although the existing spatio-temporal data mining methods can be extended to discover CAPs, they are inefficient because they extract unnecessary correlated sensors that do not have CAPs. Therefore, we propose a CAP mining method MISCELA to efficiently discover CAPs. In MISCELA, we develop a new tree structure called CAP search tree, by which we can effectively prune the unnecessary patterns for the CAP mining. Our experiments using real sensor datasets show that the response time of MISCELA is up to 79% faster compared to the state-of-the-art.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126567684","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

Message from the MDASC'19 Workshop Chairs MDASC第19届研讨会主席致辞

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/mdm.2019.00-87

引用次数: 0

Automated Product Localization Through Mobile Data Analysis 通过移动数据分析实现自动化产品本地化

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.00-78

M. Oplenskedal, Amirhosein Taherkordi, P. Herrmann

{"title":"Automated Product Localization Through Mobile Data Analysis","authors":"M. Oplenskedal, Amirhosein Taherkordi, P. Herrmann","doi":"10.1109/MDM.2019.00-78","DOIUrl":"https://doi.org/10.1109/MDM.2019.00-78","url":null,"abstract":"Recent developments in the field of indoor RealTime Locating Systems (RTLS) using mobile devices stimulate decision support for users. For instance, smartphone-based navigation in shops can enable location-aware recommendations of certain products to customers. An impeding factor to realize such systems is that they need the exact position of products. Existing product localization solutions, however, are based on tagging or manual location registering which tend to be quite costly and laborious. In this paper, we propose an automated product localization approach solving this problem. Our system infers the location of products based on the results of accumulating two sets of customer data, i.e., the locations at which the customers stop for picking up items as well as the list of the items, they purchase. These two data sets are accumulated for a large number of users, making it possible to build correct mappings between the products and their positions. We introduce a basic version of our localization algorithm and two extensions. One helps to improve calculating the position of relocated products while the other one fosters a faster localization using a smaller number of user data sets. We discuss the results of various simulation runs which give evidence that our system has a good potential to work in practice","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130192250","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

Next Generation Blockchain Network (NGBN) 下一代区块链网络(NGBN)

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.000-3

Zhibin Lei, Chao Feng, Yang Liu, Dennis Lee, Tony Tsang, Jun Liang, Zhijun Xiong, Yuquan Liu, Gang Chen

引用次数: 4

Sequence-Aware Recommendation with Long-Term and Short-Term Attention Memory Networks 长、短期注意记忆网络的顺序感知推荐

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/MDM.2019.000-6

Daochang Chen, Rui Zhang, Bo Yuan

{"title":"Sequence-Aware Recommendation with Long-Term and Short-Term Attention Memory Networks","authors":"Daochang Chen, Rui Zhang, Bo Yuan","doi":"10.1109/MDM.2019.000-6","DOIUrl":"https://doi.org/10.1109/MDM.2019.000-6","url":null,"abstract":"Next item recommendation is an important yet challenging task in real-world applications such as E-commerce. Since people often carry out a series of online shopping activities, in order to predict what a user may purchase next, it is essential to model the user's general taste as well as the sequential correlation between purchases. Existing models combine these two factors directly without considering the dynamic changes of a user's long-term and short-term preferences. Meanwhile, when a purchase session contains multiple items, not all of them have the same impact on the next item to purchase. To address these limitations, we propose a model that introduces hierarchical attention to dynamically balance between general taste (long-term preference) and sequential behavior (short-term preference). To weight individual items in the same session, we design a neural memory network with attention mechanism to learn the dynamic weights. Our model can adapt the embedding of each session as well as the embedding of long-term and short-term preferences. Extensive experiments on three real-world datasets show that our model significantly outperforms state-of-the-art methods based on commonly used evaluation metrics.","PeriodicalId":241426,"journal":{"name":"2019 20th IEEE International Conference on Mobile Data Management (MDM)","volume":"43 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133293187","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: 3

Message from the MLDQ'19 Workshop Chairs MLDQ'19研讨会主席致辞

2019 20th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2019-06-01 DOI: 10.1109/mdm.2019.00-90

引用次数: 0