{"title":"MDM 2018 Conference Officers","authors":"","doi":"10.1109/mdm.2018.00009","DOIUrl":"https://doi.org/10.1109/mdm.2018.00009","url":null,"abstract":"","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"71 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":"121598819","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}
Abdullah M. Sawas, Abdullah Abuolaim, M. Afifi, M. Papagelis
Mining large-scale trajectory data streams (of moving objects) has been of ever increasing research interest due to an abundance of modern tracking devices and its large number of critical applications. In this paper, we are interested in mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining task that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. In particular, we focus on trajectories of pedestrians coming from motion video analysis and we are interested in interactive analysis and exploration of group dynamics, including various definitions of group gathering and dispersion. Towards this end, we present a suite of (three) tensor-based methods for efficient discovery of evolving groups of pedestrians. Traditional approaches to solve the problem heavily rely on well-defined clustering algorithms to discover groups of pedestrians at each time point, and then post-process these groups to discover groups that satisfy specific group pattern semantics, including time constraints. In contrast, our proposed methods are based on efficiently discovering pairs of pedestrians that move together over time, under varying conditions. Pairs of pedestrians are subsequently used as a building block for effectively discovering groups of pedestrians. The suite of proposed methods provides the ability to adapt to many different scenarios and application requirements. Furthermore, a query-based search method is provided that allows for interactive exploration and analysis of group dynamics over time and space. Through experiments on real data, we demonstrate the effectiveness of our methods on discovering group patterns of pedestrian trajectories against sensible baselines, for a varying range of conditions. In addition, a visual testing is performed on real motion video to assert the group dynamics discovered by each method.
{"title":"Tensor Methods for Group Pattern Discovery of Pedestrian Trajectories","authors":"Abdullah M. Sawas, Abdullah Abuolaim, M. Afifi, M. Papagelis","doi":"10.1109/MDM.2018.00024","DOIUrl":"https://doi.org/10.1109/MDM.2018.00024","url":null,"abstract":"Mining large-scale trajectory data streams (of moving objects) has been of ever increasing research interest due to an abundance of modern tracking devices and its large number of critical applications. In this paper, we are interested in mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining task that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. In particular, we focus on trajectories of pedestrians coming from motion video analysis and we are interested in interactive analysis and exploration of group dynamics, including various definitions of group gathering and dispersion. Towards this end, we present a suite of (three) tensor-based methods for efficient discovery of evolving groups of pedestrians. Traditional approaches to solve the problem heavily rely on well-defined clustering algorithms to discover groups of pedestrians at each time point, and then post-process these groups to discover groups that satisfy specific group pattern semantics, including time constraints. In contrast, our proposed methods are based on efficiently discovering pairs of pedestrians that move together over time, under varying conditions. Pairs of pedestrians are subsequently used as a building block for effectively discovering groups of pedestrians. The suite of proposed methods provides the ability to adapt to many different scenarios and application requirements. Furthermore, a query-based search method is provided that allows for interactive exploration and analysis of group dynamics over time and space. Through experiments on real data, we demonstrate the effectiveness of our methods on discovering group patterns of pedestrian trajectories against sensible baselines, for a varying range of conditions. In addition, a visual testing is performed on real motion video to assert the group dynamics discovered by each method.","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":"122382777","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}
Given a pair of Origin-Destination (OD) locations, the set of trajectories passing from the original to destination, usually possesses the nature to reflect different traveling patterns between OD. In general, the higher diversity these trajectories have, the more various traveling behaviors and greater robustness of the connectivity can be revealed, which highly raises the value of transportation analysis towards the corresponding OD pair. Therefore, in this paper, we introduce a comprehensive and rational measure for trajectory diversity, on top of which we propose a novel query, Top-k Diversified Search (TkDS), that aims to find a set of k OD pairs among all the given OD pairs such that the trajectories traversing in-between have the highest diversity. Owing to the intrinsic characteristics of trajectory data, the computational cost for diversity is considerably high. Thus we present an efficient bounding algorithm with early termination to filter the candidates that are impossible to contribute the result. Finally, we demonstrate some case studies for trajectory diversity on real world dataset and give a comprehensive performance evaluation on the Top-k Diversified Search.
{"title":"Origin-Destination Trajectory Diversity Analysis: Efficient Top-k Diversified Search","authors":"Dan He, Boyu Ruan, Bolong Zheng, Xiaofang Zhou","doi":"10.1109/MDM.2018.00030","DOIUrl":"https://doi.org/10.1109/MDM.2018.00030","url":null,"abstract":"Given a pair of Origin-Destination (OD) locations, the set of trajectories passing from the original to destination, usually possesses the nature to reflect different traveling patterns between OD. In general, the higher diversity these trajectories have, the more various traveling behaviors and greater robustness of the connectivity can be revealed, which highly raises the value of transportation analysis towards the corresponding OD pair. Therefore, in this paper, we introduce a comprehensive and rational measure for trajectory diversity, on top of which we propose a novel query, Top-k Diversified Search (TkDS), that aims to find a set of k OD pairs among all the given OD pairs such that the trajectories traversing in-between have the highest diversity. Owing to the intrinsic characteristics of trajectory data, the computational cost for diversity is considerably high. Thus we present an efficient bounding algorithm with early termination to filter the candidates that are impossible to contribute the result. Finally, we demonstrate some case studies for trajectory diversity on real world dataset and give a comprehensive performance evaluation on the Top-k Diversified Search.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"91 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":"125493142","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 Spatial Crowdsourcing (SC), a set of spatio-temporal tasks are outsourced to a set of workers, i.e., individuals with mobile devices who physically travel to task locations. The process of matching workers to tasks is performed by a SC server. To perform matching, the SC server needs access to worker locations. However, the SC server may not be trustworthy. Current solutions for protecting locations of workers assume that a trusted cellular service provider (CSP) knows the identities and locations of workers and sanitizes locations before sharing them with the SC server. In practice, the CSP may not have the technical ability, nor the proper incentives to perform the sanitization task. Thus, location protection must be performed by a Location Privacy Provider (LPP). To prevent identity disclosure to the LPP, we propose a novel solution based on anonymous credentials which preserves worker privacy. Our solution allows registered workers to log on to the LPP and receive tasks from the SC-server anonymously. In addition, our solution assures the confidentiality and integrity of spatial tasks. Our implementation and experiments demonstrate that our solution is practical.
{"title":"Privacy-Preserving Spatial Crowdsourcing Based on Anonymous Credentials","authors":"X. Yi, Fang-Yu Rao, Gabriel Ghinita, E. Bertino","doi":"10.1109/MDM.2018.00036","DOIUrl":"https://doi.org/10.1109/MDM.2018.00036","url":null,"abstract":"In Spatial Crowdsourcing (SC), a set of spatio-temporal tasks are outsourced to a set of workers, i.e., individuals with mobile devices who physically travel to task locations. The process of matching workers to tasks is performed by a SC server. To perform matching, the SC server needs access to worker locations. However, the SC server may not be trustworthy. Current solutions for protecting locations of workers assume that a trusted cellular service provider (CSP) knows the identities and locations of workers and sanitizes locations before sharing them with the SC server. In practice, the CSP may not have the technical ability, nor the proper incentives to perform the sanitization task. Thus, location protection must be performed by a Location Privacy Provider (LPP). To prevent identity disclosure to the LPP, we propose a novel solution based on anonymous credentials which preserves worker privacy. Our solution allows registered workers to log on to the LPP and receive tasks from the SC-server anonymously. In addition, our solution assures the confidentiality and integrity of spatial tasks. Our implementation and experiments demonstrate that our solution is practical.","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":"131208776","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}
Layla Pournajaf, Farnaz Tahmasebian, Li Xiong, V. Sunderam, C. Shahabi
Reverse k-nearest neighbor (RkNN) queries are prevalent in location-based services to find those locations that have the query point as one of their k nearest neighbors. However, such query requires users to disclose the location of the query point to a service provider who might be untrustworthy. Previous attempts to preserve the privacy of RkNN queries are either based on weaker notions of privacy such as location cloaking or not efficient when k > 1. In this paper, we propose novel solutions based on the private information retrieval (PIR) mechanism to preserve the privacy of RkNN query points. Our solutions include server-side data indexing and client-side query processing methods to facilitate PIR which is an inherently expensive data retrieval mechanism. We experimentally evaluate our approach using real-world datasets and show that it preserves the location privacy of queries with reasonable computation and storage overhead.
{"title":"Privacy Preserving Reverse k-Nearest Neighbor Queries","authors":"Layla Pournajaf, Farnaz Tahmasebian, Li Xiong, V. Sunderam, C. Shahabi","doi":"10.1109/MDM.2018.00035","DOIUrl":"https://doi.org/10.1109/MDM.2018.00035","url":null,"abstract":"Reverse k-nearest neighbor (RkNN) queries are prevalent in location-based services to find those locations that have the query point as one of their k nearest neighbors. However, such query requires users to disclose the location of the query point to a service provider who might be untrustworthy. Previous attempts to preserve the privacy of RkNN queries are either based on weaker notions of privacy such as location cloaking or not efficient when k > 1. In this paper, we propose novel solutions based on the private information retrieval (PIR) mechanism to preserve the privacy of RkNN query points. Our solutions include server-side data indexing and client-side query processing methods to facilitate PIR which is an inherently expensive data retrieval mechanism. We experimentally evaluate our approach using real-world datasets and show that it preserves the location privacy of queries with reasonable computation and storage overhead.","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":"128773956","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 Delay Tolerant Networks (DTNs), to ensure successful message delivery, contribution of mobile nodes in relaying in an opportunistic fashion is essential. In our proposed data-centric dissemination protocol here, messages (images) are annotated with keywords by the source, and then intermediate nodes are presented with an option of adding keyword-based annotations to create higher content strength messages enroute toward the destination. Therefore, the message contents like images get enriched as the ground situation evolves and learned by these intermediate nodes, such as in a disaster situation, or in a battlefield. Due to limited battery and storage capacity in mobile devices, nodes might turn selfish and do not participate in relaying or improving the quality of messages. Thus, additionally, an incentive mechanism is proposed in this paper which considers factors like message quality, level of interests, battery usage, etc for the calculation of incentives. At the same time, in order to prevent the nodes from turning malicious by adding inappropriate message tags in pursuit of acquiring more incentive, a distributed reputation model (DRM) is developed and integrated with the proposed incentive scheme. DRM takes into account inputs from the intermediate users like ratings of the message quality, relevance of annotations in the message, etc. The proposed scheme thus ensures avoidance of congestion due to uncooperative or selfish nodes in the system. The performance evaluations show that our approach delivers more high priority and quality messages with reduced traffic with a slightly lower message delivery ratio compared to a more recent DTN routing like ChitChat, where a source forwards a message to intermediate nodes, which meet or exceed the matching strength of keyword-based interests.
{"title":"Reputation and Credit Based Incentive Mechanism for Data-Centric Message Delivery in DTNs","authors":"Himanshu Jethawa, S. Madria","doi":"10.1109/MDM.2018.00038","DOIUrl":"https://doi.org/10.1109/MDM.2018.00038","url":null,"abstract":"In Delay Tolerant Networks (DTNs), to ensure successful message delivery, contribution of mobile nodes in relaying in an opportunistic fashion is essential. In our proposed data-centric dissemination protocol here, messages (images) are annotated with keywords by the source, and then intermediate nodes are presented with an option of adding keyword-based annotations to create higher content strength messages enroute toward the destination. Therefore, the message contents like images get enriched as the ground situation evolves and learned by these intermediate nodes, such as in a disaster situation, or in a battlefield. Due to limited battery and storage capacity in mobile devices, nodes might turn selfish and do not participate in relaying or improving the quality of messages. Thus, additionally, an incentive mechanism is proposed in this paper which considers factors like message quality, level of interests, battery usage, etc for the calculation of incentives. At the same time, in order to prevent the nodes from turning malicious by adding inappropriate message tags in pursuit of acquiring more incentive, a distributed reputation model (DRM) is developed and integrated with the proposed incentive scheme. DRM takes into account inputs from the intermediate users like ratings of the message quality, relevance of annotations in the message, etc. The proposed scheme thus ensures avoidance of congestion due to uncooperative or selfish nodes in the system. The performance evaluations show that our approach delivers more high priority and quality messages with reduced traffic with a slightly lower message delivery ratio compared to a more recent DTN routing like ChitChat, where a source forwards a message to intermediate nodes, which meet or exceed the matching strength of keyword-based interests.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"08 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":"127273857","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}
Marcus J. Beuchert, Steffen Hald Jensen, Omar Ali Sheikh-Omar, Mathias Bach Svendsen, B. Yang
We demonstrate aSTEP, a spatio-temporal data management and analytics platform developed at Aalborg University (a.k.a. aau) that aims at providing a range of core functionalities for outdoor location-based service, indoor locationbased service, and location-based social networks, which facilitates application developers to develop their own, specific locationbased services on top of aSTEP. aSTEP also consolidates many recent research results on spatio-temporal data management and analytics, and serves as a testbed for exploring advanced solutions to a range of challenges related to spatio-temporal data management and analytics, e.g., Mobility-as-a-Service, dataintensive routing. In addition, from education perspectives, every spring semester aSTEP accommodates some 30 to 40 software engineering students' group-based bachelor projects at the Department of Computer Science, Aalborg University.
{"title":"aSTEP: Aau's Spatio-TEmporal Data Analytics Platform","authors":"Marcus J. Beuchert, Steffen Hald Jensen, Omar Ali Sheikh-Omar, Mathias Bach Svendsen, B. Yang","doi":"10.1109/MDM.2018.00049","DOIUrl":"https://doi.org/10.1109/MDM.2018.00049","url":null,"abstract":"We demonstrate aSTEP, a spatio-temporal data management and analytics platform developed at Aalborg University (a.k.a. aau) that aims at providing a range of core functionalities for outdoor location-based service, indoor locationbased service, and location-based social networks, which facilitates application developers to develop their own, specific locationbased services on top of aSTEP. aSTEP also consolidates many recent research results on spatio-temporal data management and analytics, and serves as a testbed for exploring advanced solutions to a range of challenges related to spatio-temporal data management and analytics, e.g., Mobility-as-a-Service, dataintensive routing. In addition, from education perspectives, every spring semester aSTEP accommodates some 30 to 40 software engineering students' group-based bachelor projects at the Department of Computer Science, Aalborg University.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"33 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":"134499186","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}
Travel-time estimation for paths in a road network often relies on pre-computed histograms that are usually available on a road segment level. Then the pre-computed histograms of the segments of a path are convolved to obtain a histogram that estimates the travel time. With the growing sizes of trajectory datasets, it becomes possible to compute histograms for increasingly longer sub-paths. Since pre-computation is infeasible for all sub-paths in a road network, we propose computing histograms on-the-fly, i.e., during routing. Such an on-the-fly method must filter the underlying trajectory dataset by spatio-temporal predicates to obtain the relevant trajectories and offers the opportunity to apply additional filtering predicates to the trajectories with little overhead. We report on a study showing that considerable improvements in accuracy of the histograms obtained for paths can be obtained by choosing filtering predicates that not only adapt to the intended start of a trip, but also to the driver and the weather. We also make the cases for a sub-path partitioning based on segment categories since there are significant differences between road types when applying our on-the-fly method.
{"title":"Adaptive Travel-Time Estimation: A Case for Custom Predicate Selection","authors":"Robert Waury, Christian S. Jensen, K. Torp","doi":"10.1109/MDM.2018.00026","DOIUrl":"https://doi.org/10.1109/MDM.2018.00026","url":null,"abstract":"Travel-time estimation for paths in a road network often relies on pre-computed histograms that are usually available on a road segment level. Then the pre-computed histograms of the segments of a path are convolved to obtain a histogram that estimates the travel time. With the growing sizes of trajectory datasets, it becomes possible to compute histograms for increasingly longer sub-paths. Since pre-computation is infeasible for all sub-paths in a road network, we propose computing histograms on-the-fly, i.e., during routing. Such an on-the-fly method must filter the underlying trajectory dataset by spatio-temporal predicates to obtain the relevant trajectories and offers the opportunity to apply additional filtering predicates to the trajectories with little overhead. We report on a study showing that considerable improvements in accuracy of the histograms obtained for paths can be obtained by choosing filtering predicates that not only adapt to the intended start of a trip, but also to the driver and the weather. We also make the cases for a sub-path partitioning based on segment categories since there are significant differences between road types when applying our on-the-fly method.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"49 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":"133912656","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":"Message from the MDM 2018 Demonstration Track Co-Chairs","authors":"Y. Huang, Goce Trajcevski","doi":"10.1109/MDM.2018.00008","DOIUrl":"https://doi.org/10.1109/MDM.2018.00008","url":null,"abstract":"","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"22 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":"129610000","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}
Amílcar Soares Júnior, V. Times, C. Renso, S. Matwin, L. A. Cabral
A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajectories into homogeneous segments based on some criteria. Although trajectory segmentation has been the object of several approaches in the last decade, a proposal based on a semi-supervised approach remains inexistent. A semi-supervised approach means that a user labels manually a small set of trajectories with meaningful segments and, from this set, the method infers in an unsupervised way the segments of the remaining trajectories. The main advantage of this method compared to pure supervised ones is that it reduces the human effort to label the number of trajectories. In this work, we propose the use of the Minimum Description Length (MDL) principle to measure homogeneity inside segments. We also introduce the Reactive Greedy Randomized Adaptive Search Procedure for semantic Semi-supervised Trajectory Segmentation (RGRASP-SemTS) algorithm that segments trajectories by combining a limited user labeling phase with a low number of input parameters and no predefined segmenting criteria. The approach and the algorithm are presented in detail throughout the paper, and the experiments are carried out on two real-world datasets. The evaluation tests prove how our approach outperforms state-of-the-art competitors when compared to ground truth.
{"title":"A Semi-Supervised Approach for the Semantic Segmentation of Trajectories","authors":"Amílcar Soares Júnior, V. Times, C. Renso, S. Matwin, L. A. Cabral","doi":"10.1109/MDM.2018.00031","DOIUrl":"https://doi.org/10.1109/MDM.2018.00031","url":null,"abstract":"A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajectories into homogeneous segments based on some criteria. Although trajectory segmentation has been the object of several approaches in the last decade, a proposal based on a semi-supervised approach remains inexistent. A semi-supervised approach means that a user labels manually a small set of trajectories with meaningful segments and, from this set, the method infers in an unsupervised way the segments of the remaining trajectories. The main advantage of this method compared to pure supervised ones is that it reduces the human effort to label the number of trajectories. In this work, we propose the use of the Minimum Description Length (MDL) principle to measure homogeneity inside segments. We also introduce the Reactive Greedy Randomized Adaptive Search Procedure for semantic Semi-supervised Trajectory Segmentation (RGRASP-SemTS) algorithm that segments trajectories by combining a limited user labeling phase with a low number of input parameters and no predefined segmenting criteria. The approach and the algorithm are presented in detail throughout the paper, and the experiments are carried out on two real-world datasets. The evaluation tests prove how our approach outperforms state-of-the-art competitors when compared to ground truth.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"AES-19 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":"132502172","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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