Mobile devices generate massive amounts of data that is used to get an insight into the user behavior by enterprise systems. Data privacy is a concern in such systems as users have little control over the data that is generated by them. Blockchain systems offer ways to ensure privacy and security of the user data with the implementation of an access control mechanism. In this demonstration, we present ChainMOB, a mobility analytics application that is built on top of blockchain and addresses the fundamental privacy and security concerns in enterprise systems. Further, the extent of data sharing along with the intended audience is also controlled by the user. Another exciting feature is that user is part of the business model and is incentivized for sharing the personal mobility data. The system also supports queries that can be used in a variety of application domains.
{"title":"ChainMOB: Mobility Analytics on Blockchain","authors":"Bulat Nasrulin, M. Muzammal, Qiang Qu","doi":"10.1109/MDM.2018.00056","DOIUrl":"https://doi.org/10.1109/MDM.2018.00056","url":null,"abstract":"Mobile devices generate massive amounts of data that is used to get an insight into the user behavior by enterprise systems. Data privacy is a concern in such systems as users have little control over the data that is generated by them. Blockchain systems offer ways to ensure privacy and security of the user data with the implementation of an access control mechanism. In this demonstration, we present ChainMOB, a mobility analytics application that is built on top of blockchain and addresses the fundamental privacy and security concerns in enterprise systems. Further, the extent of data sharing along with the intended audience is also controlled by the user. Another exciting feature is that user is part of the business model and is incentivized for sharing the personal mobility data. The system also supports queries that can be used in a variety of application domains.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"19 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":"116968498","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 objective of this paper is to determine how ridesharing can help lowering the travel cost of a user who already has a preplanned trip. This problem is formulated as the Ridesharing-Inspired Trip Recommendation Query (RSTR). In the first phase of the proposed method, the trip of the query initializer is matched with other users. In the second phase, a heuristic-based algorithm is employed to generate a new trip recommendation. Experimental results showed that the proposed solution is comparable to the optimal solution and performs much better in run-time efficiency and scalability.
{"title":"Ridesharing-Inspired Trip Recommendations","authors":"S. Madria, San Yeung, Katrina Ward","doi":"10.1109/MDM.2018.00019","DOIUrl":"https://doi.org/10.1109/MDM.2018.00019","url":null,"abstract":"The objective of this paper is to determine how ridesharing can help lowering the travel cost of a user who already has a preplanned trip. This problem is formulated as the Ridesharing-Inspired Trip Recommendation Query (RSTR). In the first phase of the proposed method, the trip of the query initializer is matched with other users. In the second phase, a heuristic-based algorithm is employed to generate a new trip recommendation. Experimental results showed that the proposed solution is comparable to the optimal solution and performs much better in run-time efficiency and scalability.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"25 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":"124641938","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}
Map-matching is an important preprocessing task for many location-based services (LBS). It projects each GPS point in trajectory data onto digital maps. The state of art work typically employed the Hidden Markov model (HMM) by shortest path computation. Such shortest path computation may not work very well for very low sampling rate trajectory data, leading to low matching precision and high running time. To solve this problem, this paper, we first identify the frequent patterns from historical trajectory data and next perform the map matching for higher precision and faster running time. Since the identified frequent patterns indicate the mobility behaviours for the majority of trajectories, the map matching thus has chance to satisfy the matching precision with high confidence. Moreover, the proposed FP-forest structure can greatly speedup the lookup of frequent paths and lead to high computation efficiency. Our experiments on real world data set validate that the proposed FP-matching outperforms state of arts in terms of effectiveness and efficiency.
{"title":"Frequent Pattern-Based Map-Matching on Low Sampling Rate Trajectories","authors":"Yukun Huang, Weixiong Rao, Zhiqiang Zhang, Peng Zhao, Mingxuan Yuan, Jia Zeng","doi":"10.1109/MDM.2018.00046","DOIUrl":"https://doi.org/10.1109/MDM.2018.00046","url":null,"abstract":"Map-matching is an important preprocessing task for many location-based services (LBS). It projects each GPS point in trajectory data onto digital maps. The state of art work typically employed the Hidden Markov model (HMM) by shortest path computation. Such shortest path computation may not work very well for very low sampling rate trajectory data, leading to low matching precision and high running time. To solve this problem, this paper, we first identify the frequent patterns from historical trajectory data and next perform the map matching for higher precision and faster running time. Since the identified frequent patterns indicate the mobility behaviours for the majority of trajectories, the map matching thus has chance to satisfy the matching precision with high confidence. Moreover, the proposed FP-forest structure can greatly speedup the lookup of frequent paths and lead to high computation efficiency. Our experiments on real world data set validate that the proposed FP-matching outperforms state of arts in terms of effectiveness and efficiency.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"31 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":"126794209","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}
M. Saleem, F. Costa, Peter Dolog, Panagiotis Karras, T. Pedersen, T. Calders
Location-based social networks (LBSN) are social networks complemented with users' location data, such as geo-tagged activity data. Predicting such activities finds application in marketing, recommendation systems, and logistics management. In this paper, we exploit LBSN data to predict future visitors at given locations. We fetch the travel history of visitors by their check-ins in LBSNs and identify five features that significantly drive the mobility of a visitor towards a location: (i) historic visits, (ii) location category, (iii) time, (iv) distance, and (v) friends' activities. We provide a visitor prediction model, CMViP, based on collective matrix factorization and influence propagation. CMViP first utilizes collective matrix factorization to map the first four features to a common latent space to find visitors having a significant potential to visit a given location. Then, it utilizes an influence-mining approach to further incorporate friends of those visitors, who are influenced by the visitors' activities and likely to follow them. Our experiments on two real-world data-sets show that our methods outperform the state of art in terms of precision and accuracy.
{"title":"Predicting Visitors Using Location-Based Social Networks","authors":"M. Saleem, F. Costa, Peter Dolog, Panagiotis Karras, T. Pedersen, T. Calders","doi":"10.1109/MDM.2018.00043","DOIUrl":"https://doi.org/10.1109/MDM.2018.00043","url":null,"abstract":"Location-based social networks (LBSN) are social networks complemented with users' location data, such as geo-tagged activity data. Predicting such activities finds application in marketing, recommendation systems, and logistics management. In this paper, we exploit LBSN data to predict future visitors at given locations. We fetch the travel history of visitors by their check-ins in LBSNs and identify five features that significantly drive the mobility of a visitor towards a location: (i) historic visits, (ii) location category, (iii) time, (iv) distance, and (v) friends' activities. We provide a visitor prediction model, CMViP, based on collective matrix factorization and influence propagation. CMViP first utilizes collective matrix factorization to map the first four features to a common latent space to find visitors having a significant potential to visit a given location. Then, it utilizes an influence-mining approach to further incorporate friends of those visitors, who are influenced by the visitors' activities and likely to follow them. Our experiments on two real-world data-sets show that our methods outperform the state of art in terms of precision and accuracy.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"16 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":"125228961","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 19th IEEE International Conference on Mobile Data Management, held on 26–28 June 2018 in Aalborg, Denmark, follows 18 successful editions of the MDM conference. Since its inception in 1999, the MDM conference has established itself as a premier and prestigious forum for the presentation of high-impact research and exchange of innovative and significant ideas in the area of mobile data management. MDM 2018 maintained this tradition by providing a high quality program comprising papers that bridge academic research with real-world use-cases, and enable the exchange of innovations and experiences.
{"title":"Message from the MDM 2018 Program Co-Chairs","authors":"T. Hara, Wang-Chien Lee, Bin Yang","doi":"10.1109/MDM.2018.00006","DOIUrl":"https://doi.org/10.1109/MDM.2018.00006","url":null,"abstract":"The 19th IEEE International Conference on Mobile Data Management, held on 26–28 June 2018 in Aalborg, Denmark, follows 18 successful editions of the MDM conference. Since its inception in 1999, the MDM conference has established itself as a premier and prestigious forum for the presentation of high-impact research and exchange of innovative and significant ideas in the area of mobile data management. MDM 2018 maintained this tradition by providing a high quality program comprising papers that bridge academic research with real-world use-cases, and enable the exchange of innovations and experiences.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"26 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":"123651347","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 Advanced Seminars Co-Chairs","authors":"","doi":"10.1109/mdm.2018.00007","DOIUrl":"https://doi.org/10.1109/mdm.2018.00007","url":null,"abstract":"","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":"125796408","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}
Privacy-preserved tracking of WiFi-enabled devices such as smartphones offers a highly scalable solution for large-scale crowd movement studies. However, extracting knowledge out of pedestrian-tracking data acquired this way is not simple. This is, generally, due to the inherent inaccuracy of the measurement technique. Segmenting an individual's trajectory data into periods of stops and moves is a fundamental step in analyzing crowds' movement. Such distinctions allow us to answer advanced questions regarding visited locations or even social behavior. Algorithms previously designed for distinguishing movements from stay periods, assume datasets are gathered using GPS, which offers precise positioning. WiFi tracking, however, does not offer such precision. The location of devices can at best be reduced to a large area around the WiFi scanner. In this paper, we study a set of established algorithms for detecting periods of stops and moves from GPS-based datasets and their applicability to WiFi-based data. Consequently, we propose possible improvements to such algorithms considering the inherent characteristics of WiFi tracking data.
{"title":"Identifying Movements in Noisy Crowd Analytics Data","authors":"C. Chilipirea, C. Dobre, Mitra Baratchi, M. Steen","doi":"10.1109/MDM.2018.00033","DOIUrl":"https://doi.org/10.1109/MDM.2018.00033","url":null,"abstract":"Privacy-preserved tracking of WiFi-enabled devices such as smartphones offers a highly scalable solution for large-scale crowd movement studies. However, extracting knowledge out of pedestrian-tracking data acquired this way is not simple. This is, generally, due to the inherent inaccuracy of the measurement technique. Segmenting an individual's trajectory data into periods of stops and moves is a fundamental step in analyzing crowds' movement. Such distinctions allow us to answer advanced questions regarding visited locations or even social behavior. Algorithms previously designed for distinguishing movements from stay periods, assume datasets are gathered using GPS, which offers precise positioning. WiFi tracking, however, does not offer such precision. The location of devices can at best be reduced to a large area around the WiFi scanner. In this paper, we study a set of established algorithms for detecting periods of stops and moves from GPS-based datasets and their applicability to WiFi-based data. Consequently, we propose possible improvements to such algorithms considering the inherent characteristics of WiFi tracking data.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"42 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":"126345561","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 rapid development and commercialization of location acquisition technologies generates large trajectory datasets, that trace moving objects' trips. In this work, we propose a new trajectory mining algorithm, for discovering paths that are frequently followed by the given trajectories, named as corridors. We claim that the moving objects follow common paths-corridors. Detecting corridors from a collection of trajectories is extremely challenging due to the nature of the data (low sampling rates, different speeds, noisy measurements etc.). In this work we propose and evaluate a pipelined algorithm that abstracts from trajectories their underlying frequent paths.
{"title":"Corridor Learning Using Individual Trajectories","authors":"Nikolaos Zygouras, D. Gunopulos","doi":"10.1109/MDM.2018.00032","DOIUrl":"https://doi.org/10.1109/MDM.2018.00032","url":null,"abstract":"The rapid development and commercialization of location acquisition technologies generates large trajectory datasets, that trace moving objects' trips. In this work, we propose a new trajectory mining algorithm, for discovering paths that are frequently followed by the given trajectories, named as corridors. We claim that the moving objects follow common paths-corridors. Detecting corridors from a collection of trajectories is extremely challenging due to the nature of the data (low sampling rates, different speeds, noisy measurements etc.). In this work we propose and evaluate a pipelined algorithm that abstracts from trajectories their underlying frequent paths.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"114 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":"128201634","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}
Shen Wang, Aditya Grover, Brian Mac Namee, Philip Plantholt, J. Lopez-Leones, P. Sanchez-Escalonilla
Flight efficiency indicators reported monthly in the European area by the Performance Review Unit (PRU) help the air traffic management (ATM) community determine if excessive distances are being flown (compared with the ideal lengths of flight routes). Recent research, however, provides more indicators that comprehensively capture flight efficiencies in terms of other factors including fuel consumption, time adherence, and route charges. The efficacy of all of these indicators, however, is diminished as they are currently only available almost a month after flights take place. This is not sufficiently timely to use these indicators for the alleviation of unpredictable hotspots (i.e. sectors with congested air traffic), which often leads to unexpected ground delays. This paper proposes a methodology to calculate general flight efficiency indicators on-line in near real-time using nearest point search. A prototype system called ROGER (compRehensive On-line fliGht Efficiency monitoRing) is implemented using Apache Kafka and Spark. ROGER can digest large-scale heterogeneous datasets (i.e. mainly ADS-B data, the next generation aircraft surveillance technology) to compute indicators every 5 seconds. Our experiments on realistic datasets demonstrate that the proposed on-line indicator calculation method can achieve high accuracy compared with existing off-line approaches, and that ROGER can achieve desirable system performance in throughput and latency. A use case is also described showing how ROGER can assist in alleviating hotspots more effectively.
{"title":"ROGER: An On-Line Flight Efficiency Monitoring System Using ADS-B Data","authors":"Shen Wang, Aditya Grover, Brian Mac Namee, Philip Plantholt, J. Lopez-Leones, P. Sanchez-Escalonilla","doi":"10.1109/MDM.2018.00041","DOIUrl":"https://doi.org/10.1109/MDM.2018.00041","url":null,"abstract":"Flight efficiency indicators reported monthly in the European area by the Performance Review Unit (PRU) help the air traffic management (ATM) community determine if excessive distances are being flown (compared with the ideal lengths of flight routes). Recent research, however, provides more indicators that comprehensively capture flight efficiencies in terms of other factors including fuel consumption, time adherence, and route charges. The efficacy of all of these indicators, however, is diminished as they are currently only available almost a month after flights take place. This is not sufficiently timely to use these indicators for the alleviation of unpredictable hotspots (i.e. sectors with congested air traffic), which often leads to unexpected ground delays. This paper proposes a methodology to calculate general flight efficiency indicators on-line in near real-time using nearest point search. A prototype system called ROGER (compRehensive On-line fliGht Efficiency monitoRing) is implemented using Apache Kafka and Spark. ROGER can digest large-scale heterogeneous datasets (i.e. mainly ADS-B data, the next generation aircraft surveillance technology) to compute indicators every 5 seconds. Our experiments on realistic datasets demonstrate that the proposed on-line indicator calculation method can achieve high accuracy compared with existing off-line approaches, and that ROGER can achieve desirable system performance in throughput and latency. A use case is also described showing how ROGER can assist in alleviating hotspots more effectively.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"2 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":"133161806","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. A challenging task in this domain is that of mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. To this end, we introduce Trajectolizer, an online system for interactive analysis and exploration of trajectory group dynamics over time and space. We describe the system and demonstrate its effectiveness on discovering group patterns on trajectories of pedestrians. The system architecture and methods are general and can be used to perform group analysis of any domain-specific trajectories.
{"title":"Trajectolizer: Interactive Analysis and Exploration of Trajectory Group Dynamics","authors":"Abdullah M. Sawas, Abdullah Abuolaim, M. Afifi, M. Papagelis","doi":"10.1109/MDM.2018.00053","DOIUrl":"https://doi.org/10.1109/MDM.2018.00053","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. A challenging task in this domain is that of mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. To this end, we introduce Trajectolizer, an online system for interactive analysis and exploration of trajectory group dynamics over time and space. We describe the system and demonstrate its effectiveness on discovering group patterns on trajectories of pedestrians. The system architecture and methods are general and can be used to perform group analysis of any domain-specific trajectories.","PeriodicalId":205319,"journal":{"name":"2018 19th IEEE International Conference on Mobile Data Management (MDM)","volume":"11 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":"123865625","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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