Xin Ding, R. Chen, Lu Chen, Yunjun Gao, Christian S. Jensen
Massive trajectory data is being collected and used widely in many applications such as transportation, location-based services, and urban computing. As a result, abundant methods and systems have been proposed for managing and processing trajectory data. However, it remains difficult for users to interact well with data management and processing, due to the lack of efficient data processing methods and effective visualization techniques for big trajectory data. In this demonstration, we present a new framework, VIPTRA, to process big trajectory data visually and interactively. VIPTRA builds upon UlTraMan, a distributed in-memory system for big trajectory data, and thus, it takes advantage of its capability of high performance. The demonstration shows the efficiency of data processing and user-friendly visualization and interaction techniques provided in VIPTRA, via several scenarios of visual analysis and trajectory editing tasks.
{"title":"VIPTRA: Visualization and Interactive Processing on Big Trajectory Data","authors":"Xin Ding, R. Chen, Lu Chen, Yunjun Gao, Christian S. Jensen","doi":"10.1109/MDM.2018.00055","DOIUrl":"https://doi.org/10.1109/MDM.2018.00055","url":null,"abstract":"Massive trajectory data is being collected and used widely in many applications such as transportation, location-based services, and urban computing. As a result, abundant methods and systems have been proposed for managing and processing trajectory data. However, it remains difficult for users to interact well with data management and processing, due to the lack of efficient data processing methods and effective visualization techniques for big trajectory data. In this demonstration, we present a new framework, VIPTRA, to process big trajectory data visually and interactively. VIPTRA builds upon UlTraMan, a distributed in-memory system for big trajectory data, and thus, it takes advantage of its capability of high performance. The demonstration shows the efficiency of data processing and user-friendly visualization and interaction techniques provided in VIPTRA, via several scenarios of visual analysis and trajectory editing tasks.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"70 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":"116545695","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}
Constantinos Costa, Andreas Charalampous, Andreas Konstantinidis, D. Zeinalipour-Yazti, M. Mokbel
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.
{"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}
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
{"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}
This year’s MDM also covers these aspects and includes an exciting single-track program of full and short research papers, industrial papers, and demos. Trajectory mining and machine learning on mobile data appear to be the trending topics of the conference program. The program also features a keynote talk: Inference of Social Relationships from Location Data by Cyrus Shahabi from the University of Southern California; two invited talks on indoor services from Danish companies MapsPeople and Systematic; and a panel discussion on indoor information systems.
{"title":"Message from the MDM 2018 General Co-Chairs","authors":"A. Joshi, Simonas Šaltenis, Xiaofang Zhou","doi":"10.1109/MDM.2018.00005","DOIUrl":"https://doi.org/10.1109/MDM.2018.00005","url":null,"abstract":"This year’s MDM also covers these aspects and includes an exciting single-track program of full and short research papers, industrial papers, and demos. Trajectory mining and machine learning on mobile data appear to be the trending topics of the conference program. The program also features a keynote talk: Inference of Social Relationships from Location Data by Cyrus Shahabi from the University of Southern California; two invited talks on indoor services from Danish companies MapsPeople and Systematic; and a panel discussion on indoor information systems.","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":"134454554","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 Keynote","authors":"","doi":"10.1109/mdm.2018.00012","DOIUrl":"https://doi.org/10.1109/mdm.2018.00012","url":null,"abstract":"","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"86 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":"117075712","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}
In this paper, we propose a method that fully combines top-k query processing with replication strategy in mobile ad hoc networks (MANETs). The goal is to acquire perfect accuracy of query results with a minimal overhead and delay. Currently, no replication strategy achieves efficient allocation of replicas for top-k queries, and no top-k query processing guarantees perfect accuracy of query results in MANETs. We propose a new replication strategy FReT (topology-Free Replication for Top-k query) and new top-k query processing methods. FReT advantages efficient top-k query processing from limited search area even if mobile nodes move. In our top-k query processing method, the search area gradually increases until receiving an exact answer. We demonstrate, through extensive simulations, that our approaches function well in terms of small delay and overhead.
在本文中，我们提出了一种在移动自组织网络(manet)中充分结合top-k查询处理和复制策略的方法。目标是以最小的开销和延迟获得查询结果的完美准确性。目前，没有复制策略能够实现top-k查询的高效副本分配，也没有top-k查询处理能够保证MANETs查询结果的完美准确性。我们提出了一种新的复制策略FReT(拓扑- free replication for Top-k query)和新的Top-k查询处理方法。即使移动节点移动，FReT也能在有限的搜索区域内进行高效的top-k查询处理。在我们的top-k查询处理方法中，搜索区域逐渐增加，直到得到确切的答案。我们通过大量的模拟证明，我们的方法在小延迟和开销方面运行良好。
{"title":"Top-k Query Processing with Replication Strategy in Mobile Ad Hoc Networks","authors":"Yuya Sasaki, T. Hara, Y. Ishikawa","doi":"10.1109/MDM.2018.00039","DOIUrl":"https://doi.org/10.1109/MDM.2018.00039","url":null,"abstract":"In this paper, we propose a method that fully combines top-k query processing with replication strategy in mobile ad hoc networks (MANETs). The goal is to acquire perfect accuracy of query results with a minimal overhead and delay. Currently, no replication strategy achieves efficient allocation of replicas for top-k queries, and no top-k query processing guarantees perfect accuracy of query results in MANETs. We propose a new replication strategy FReT (topology-Free Replication for Top-k query) and new top-k query processing methods. FReT advantages efficient top-k query processing from limited search area even if mobile nodes move. In our top-k query processing method, the search area gradually increases until receiving an exact answer. We demonstrate, through extensive simulations, that our approaches function well in terms of small delay and overhead.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"83 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":"124043947","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}
Guoqiong Liao, Shan Jiang, Zhiheng Zhou, Changxuan Wan, X. Liu
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.
{"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}
Constantinos Costa, Andreas Charalampous, Andreas Konstantinidis, D. Zeinalipour-Yazti, M. Mokbel
In this demonstration paper, we present the TBD-DP operator, which relies on existing Machine Learning (ML) algorithms to abstract Telco Big Data (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. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate the efficiency of the proposed operator using SPATE, which is a novel TBD visual analytic architecture we have developed. Our demo will enable attendees to interactively explore synthetic antenna signal traces, we will provide, in both visual and SQL mode. In both cases, the performance of the propositions will be quantitatively conveyed to the attendees through dedicated dashboards.
{"title":"TBD-DP: Telco Big Data Visual Analytics with Data Postdiction","authors":"Constantinos Costa, Andreas Charalampous, Andreas Konstantinidis, D. Zeinalipour-Yazti, M. Mokbel","doi":"10.1109/MDM.2018.00050","DOIUrl":"https://doi.org/10.1109/MDM.2018.00050","url":null,"abstract":"In this demonstration paper, we present the TBD-DP operator, which relies on existing Machine Learning (ML) algorithms to abstract Telco Big Data (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. Our framework also includes visual and declarative interfaces for a variety of telco-specific data exploration tasks. We demonstrate the efficiency of the proposed operator using SPATE, which is a novel TBD visual analytic architecture we have developed. Our demo will enable attendees to interactively explore synthetic antenna signal traces, we will provide, in both visual and SQL mode. In both cases, the performance of the propositions will be quantitatively conveyed to the attendees through dedicated dashboards.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"17 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":"126247445","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}
Dimitrios Tomaras, V. Kalogeraki, T. Liebig, D. Gunopulos
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.
{"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}
Douglas Alves Peixoto, Han Su, Nguyen Quoc Viet Hung, Bela Stantic, Bolong Zheng, Xiaofang Zhou
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.
{"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}
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