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

VIPTRA: Visualization and Interactive Processing on Big Trajectory Data VIPTRA:大轨迹数据的可视化和交互处理

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00055

Xin Ding, R. Chen, Lu Chen, Yunjun Gao, Christian S. Jensen

引用次数: 4

Decaying Telco Big Data with Data Postdiction 衰减的电信大数据与数据定位

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00027

Constantinos Costa, Andreas Charalampous, Andreas Konstantinidis, D. Zeinalipour-Yazti, M. Mokbel

{"title":"Decaying Telco Big Data with Data Postdiction","authors":"Constantinos Costa, Andreas Charalampous, Andreas Konstantinidis, D. Zeinalipour-Yazti, M. Mokbel","doi":"10.1109/MDM.2018.00027","DOIUrl":"https://doi.org/10.1109/MDM.2018.00027","url":null,"abstract":"In this paper, we present a novel decaying operator for Telco Big Data (TBD), coined TBD-DP (Data Postdiction). Unlike data prediction, which aims to make a statement about the future value of some tuple, our formulated data postdiction term, aims to make a statement about the past value of some tuple, which does not exist anymore as it had to be deleted to free up disk space. TBD-DP relies on existing Machine Learning (ML) algorithms to abstract TBD into compact models that can be stored and queried when necessary. Our proposed TBD-DP operator has the following two conceptual phases: (i) in an offline phase, it utilizes a LSTM-based hierarchical ML algorithm to learn a tree of models (coined TBD-DP tree) over time and space; (ii) in an online phase, it uses the TBD-DP tree to recover data within a certain accuracy. In our experimental setup, we measure the efficiency of the proposed operator using a ~10GB anonymized real telco network trace and our experimental results in Tensorflow over HDFS are extremely encouraging as they show that TBD-DP saves an order of magnitude storage space while maintaining a high accuracy on the recovered data.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"9 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":"121583760","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

Targets and Shapes Tracking (Advanced Seminar) 目标和形状跟踪(高级研讨会)

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00015

Goce Trajcevski, P. Scheuermann

{"title":"Targets and Shapes Tracking (Advanced Seminar)","authors":"Goce Trajcevski, P. Scheuermann","doi":"10.1109/MDM.2018.00015","DOIUrl":"https://doi.org/10.1109/MDM.2018.00015","url":null,"abstract":"The topics of tracking moving objects and moving shapes have been extensively researched in multiple communities – from Moving Objects Databases (MOD) and spatio-temporal data management, through image/video processing and traffic management, to environmental and ecology studies. This paper gives a summary of the topics discussed in the advanced seminar on tracking objects and shapes, as well as an overview of its proposed structure. After a brief introduction and motivation-survey of different research fields and societal applications, the first part of the seminar will give a historic survey of the fundamental techniques for tracking mobile objects. The second part will give an overview of the approaches popular in MOD and spatiotemporal data management communities (tracking and querying, streaming data, map-matching, etc.). The third part is the central one – discussing the issues and solutions in distributed tracking of moving objects and shapes: from topological predicates and trends detection, through tracking deformable shapes, to specifics of indoor tracking. The fourth major part is intended to be a \"potpourri-style\" review of different application contexts and the popular approaches for tracking individual objects and shapes – spanning from collective motion analysis in social networks and animal herds, through toxic elements, pollutants, and geoprocesses (landslides), to different approaches for visual analytics in this context. The main objective of this advanced seminar is to provide a cohesive overview of the different perspectives on motion tracking; the corresponding approaches for its effective management; and possibilities for other research directions","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":"132354570","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

Message from the MDM 2018 General Co-Chairs 2018年MDM联合主席致辞

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00005

A. Joshi, Simonas Šaltenis, Xiaofang Zhou

引用次数: 0

MDM 2018 Keynote MDM 2018主题演讲

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/mdm.2018.00012

引用次数: 0

Top-k Query Processing with Replication Strategy in Mobile Ad Hoc Networks 基于复制策略的移动Ad Hoc网络Top-k查询处理

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00039

Yuya Sasaki, T. Hara, Y. Ishikawa

引用次数: 5

POI Recommendation of Location-Based Social Networks Using Tensor Factorization 基于张量分解的位置社交网络POI推荐

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00028

Guoqiong Liao, Shan Jiang, Zhiheng Zhou, Changxuan Wan, X. Liu

{"title":"POI Recommendation of Location-Based Social Networks Using Tensor Factorization","authors":"Guoqiong Liao, Shan Jiang, Zhiheng Zhou, Changxuan Wan, X. Liu","doi":"10.1109/MDM.2018.00028","DOIUrl":"https://doi.org/10.1109/MDM.2018.00028","url":null,"abstract":"With the rapid development of wireless communication technologies, location-based social networks (LBSNs) like foursquare and Gowalla have become very popular. Point of interest (POI) recommendation is a kind of important recommendation in LBSNs for enhancing user experiences. Unlike online social networks, LBSNs have a great deal of check-in data and comment information, which can provide valuable information for POI recommendation. In this paper, a novel recommendation strategy using tensor factorization is proposed for improving accurate rate of POI recommendation. Firstly, the latent dirichlet allocation(LDA) topic model is used to extract topic information and generate topic probability distribution of each POI based on comment information from users. Secondly, the check-in data of each user is divided into multiple data slices corresponding to each hour of a day. By connecting with the topic distributions of the visited POIs of each user, a user-topic-time tensor is conducted to present the potential preferences of all users. Finally, a higher order singular value decomposition (HOSVD) algorithm is employed to decompose the third-order tensor, to get dense preference information for POI recommendation. The experiments on a real dataset show that the proposed approach have better performance than the baseline methods.","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":"127917092","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: 23

TBD-DP: Telco Big Data Visual Analytics with Data Postdiction TBD-DP:电信大数据可视化分析与数据定位

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00050

Constantinos Costa, Andreas Charalampous, Andreas Konstantinidis, D. Zeinalipour-Yazti, M. Mokbel

引用次数: 0

Crowd-Based Ecofriendly Trip Planning 以大众为本的环保行程规划

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00018

Dimitrios Tomaras, V. Kalogeraki, T. Liebig, D. Gunopulos

{"title":"Crowd-Based Ecofriendly Trip Planning","authors":"Dimitrios Tomaras, V. Kalogeraki, T. Liebig, D. Gunopulos","doi":"10.1109/MDM.2018.00018","DOIUrl":"https://doi.org/10.1109/MDM.2018.00018","url":null,"abstract":"In recent years we have witnessed a growing interest in trip planning systems aiming at organizing daily travel schedules in smart cities. Such systems use specialized engines to find optimal means of transport between two geospatial endpoints to provide recommendations to citizens for short routes across the city. At the same time, alternative means of transportation, such as bike sharing systems, have enjoyed tremendous success since they offer a green and facile solution for daily commuters and tourists. However, one major challenge of the bike sharing systems is that the distribution of bikes among the stations can be quite uneven during rush hours or due to topography. This often results in shortage of bikes and increasing numbers of disappointed users. Existing works in the literature are limited since they only focus on predicting the demand or apply a-posteriori methods for balancing the load of stations. Furthermore, none of these works consider the benefit of these systems in concert. In this work, we present \"MOToR\" (MultimOdal Trip Rebalancing), a system that builds upon the OpenTripPlanner framework to incorporate dynamic transit schedule data while balancing the availability of bikes among the bike stations. Our experimental evaluation shows that our approach is practical, efficient and outperforms state-of-the-art methods for route planning.","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":"133298636","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

Concept for Evaluation of Techniques for Trajectory Distance Measures 弹道距离测量技术评价的概念

2018 19th IEEE International Conference on Mobile Data Management (MDM) Pub Date : 2018-06-01 DOI: 10.1109/MDM.2018.00048

Douglas Alves Peixoto, Han Su, Nguyen Quoc Viet Hung, Bela Stantic, Bolong Zheng, Xiaofang Zhou

{"title":"Concept for Evaluation of Techniques for Trajectory Distance Measures","authors":"Douglas Alves Peixoto, Han Su, Nguyen Quoc Viet Hung, Bela Stantic, Bolong Zheng, Xiaofang Zhou","doi":"10.1109/MDM.2018.00048","DOIUrl":"https://doi.org/10.1109/MDM.2018.00048","url":null,"abstract":"Measuring the similarity (or distance) between trajectories of moving objects is a common procedure taken by most trajectory data-driven applications. One of the biggest challenges of trajectory distances measurement is that the distance needs to be carefully defined in order to reflect the true underlying similarity. This is due to the fact that trajectories are essentially non-uniform sequential data with variable length, attached with both spatial and temporal attributes, which may or may not be considered for similarity measures. Therefore, tens of similarity measures for trajectory data have been proposed; every technique claim an advantage over the others in a different aspect. Hence, it's difficult for users to choose the best-suited technique, as well as the appropriate parameter values, since each technique has distinct performance and characteristics depending on various factors. In this paper, we develop an application that allows to evaluate several techniques in different aspects (accuracy, sensitivity to trajectory features, performance, etc.). We believe that this tool will be able to serve as a practical guideline for both researchers and developers. While researchers can use our tool to assess existing or new techniques, developers can reuse its components to reduce the development complexity.","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":"116241103","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