The Experts below are selected from a list of 135360 Experts worldwide ranked by ideXlab platform
Rui Chen - One of the best experts on this subject based on the ideXlab platform.
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MDM - VIPTRA: Visualization and Interactive Processing on Big Trajectory Data
2018 19th IEEE International Conference on Mobile Data Management (MDM), 2018Co-Authors: Xin Ding, Rui Chen, Lu Chen, Yunjun Gao, Christian S. JensenAbstract: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.
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anonymizing Trajectory Data for passenger flow analysis
Transportation Research Part C-emerging Technologies, 2014Co-Authors: Moein Ghasemzadeh, Rui Chen, Benjamin C. M. Fung, Anjali AwasthiAbstract:The increasing use of location-aware devices provides many opportunities for analyzing and mining human mobility. The Trajectory of a person can be represented as a sequence of visited locations with different timestamps. Storing, sharing, and analyzing personal trajectories may pose new privacy threats. Previous studies have shown that employing traditional privacy models and anonymization methods often leads to low information quality in the resulting Data. In this paper we propose a method for achieving anonymity in a Trajectory Database while preserving the information to support effective passenger flow analysis. Specifically, we first extract the passenger flowgraph, which is a commonly employed representation for modeling uncertain moving objects, from the raw Trajectory Data. We then anonymize the Data with the goal of minimizing the impact on the flowgraph. Extensive experimental results on both synthetic and real-life Data sets suggest that the framework is effective to overcome the special challenges in Trajectory Data anonymization, namely, high dimensionality, sparseness, and sequentiality.
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Privacy-preserving Trajectory Data publishing by local suppression
Information Sciences, 2013Co-Authors: Rui Chen, Benjamin C. M. Fung, Bipin C. Desai, Noman Mohammed, Ke WangAbstract:The pervasiveness of location-aware devices has spawned extensive research in Trajectory Data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing Trajectory Data among different parties often creates an obstacle for effective Data mining. In this paper, we study the challenges of anonymizing Trajectory Data: high dimensionality, sparseness, and sequentiality. Employing traditional privacy models and anonymization methods often leads to low Data utility in the resulting Data and ineffective Data mining. In addressing these challenges, this is the first paper to introduce local suppression to achieve a tailored privacy model for Trajectory Data anonymization. The framework allows the adoption of various Data utility metrics for different Data mining tasks. As an illustration, we aim at preserving both instances of location-time doublets and frequent sequences in a Trajectory Database, both being the foundation of many Trajectory Data mining tasks. Our experiments on both synthetic and real-life Data sets suggest that the framework is effective and efficient to overcome the challenges in Trajectory Data anonymization. In particular, compared with the previous works in the literature, our proposed local suppression method can significantly improve the Data utility in anonymous Trajectory Data.
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Differentially Private Trajectory Data Publication
arXiv: Databases, 2011Co-Authors: Rui Chen, Benjamin C. M. Fung, Bipin C. DesaiAbstract:With the increasing prevalence of location-aware devices, Trajectory Data has been generated and collected in various application domains. Trajectory Data carries rich in- formation that is useful for many Data analysis tasks. Yet, improper publishing and use of Trajectory Data could jeopardize individual privacy. However, it has been shown that existing privacy-preserving Trajectory Data publishing methods derived from partition-based privacy models, for example k-anonymity, are unable to provide sufficient privacy protection. In this paper, motivated by the Data publishing scenario at the Societe de transport de Montreal (STM), the public transit agency in Montreal area, we study the problem of publishing Trajectory Data under the rigorous differential privacy model. We propose an efficient Data-dependent yet differentially private sanitization algorithm, which is applicable to different types of Trajectory Data. The efficiency of our approach comes from adaptively narrowing down the output domain by building a noisy prefix tree based on the underlying Data. Moreover, as a post-processing step, we make use of the inherent constraints of a prefix tree t o conduct constrained inferences, which lead to better utility. This is the first paper to introduce a practical solution for publi shing large volume of Trajectory Data under differential privacy. We examine the utility of sanitized Data in terms of count queries and frequent sequential pattern mining. Extensive experiments on real-life Trajectory Data from the STM demonstrate that our approach maintains high utility and is scalable to large Trajectory Datasets.
Jalel Akaichi - One of the best experts on this subject based on the ideXlab platform.
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Ontology-based modeling and querying of Trajectory Data
Data & Knowledge Engineering, 2017Co-Authors: Marwa Manaa, Jalel AkaichiAbstract:Abstract With the evolution of location-sensing devices and associated technologies, mobility Data driven scientific discovery approaches became an important paradigm for advanced computing performed in various central areas i.e., Internet of things and social networks. Under this paradigm, Trajectory Data is considered as a core revealing details of instantaneous behaviors piloted by mobile entities. This forms the need of modeling of such behaviors and the understanding of them, and actually, gave rise to different modeling approaches using either conceptual modeling or ontologies. Modeling and querying of Trajectory Data are still challenging because of their structural and semantic heterogeneities, and due to the complexity of establishing choices about the domain’ consensual knowledge. Ontologies are promising solutions for the above two problems seeing that they are intended to reduce structural heterogeneity among sources and to specify the semantics of concepts in an unambiguous way. In this paper, we propose a framework for a semantics oriented modeling and querying of Trajectory Data. We present an ontology-based Trajectory pivot model that covers common structures encountered in trajectories associated with links to application and geographic modules. We validate our proposal through a case study dealing with human movement activity.
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ontology based Trajectory Data warehouse conceptual model
International Conference on Big Data, 2016Co-Authors: Marwa Manaa, Jalel AkaichiAbstract:The enormous evolution of positioning technologies and remote sensors is leading to big amounts of disparate mobility Data. Collected mobility Data generates the need of modelling of such behaviour and the understanding of them which gave the rise of different models achieved either by classical conceptual modelling or by those based on ontology. Modelling and analysing Trajectory Data are still challenging because of the heterogeneity of Trajectory Data models and the complexity of establishing choices about domain’s consensual knowledge. To fulfil this objective, we propose a generic ontology that explains the semantics of these Data and we define a Trajectory Data warehouse conceptual model based on the shared ontology in order to analyse Trajectory Data going from users’ short transactions to complex queries involving decision makers. The shared ontology that we propose is an OWL-DL formalism that covers common structures encountered in trajectories. We illustrate our work with a real case study.
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DaWaK - Ontology-Based Trajectory Data Warehouse Conceptual Model
Big Data Analytics and Knowledge Discovery, 2016Co-Authors: Marwa Manaa, Jalel AkaichiAbstract:The enormous evolution of positioning technologies and remote sensors is leading to big amounts of disparate mobility Data. Collected mobility Data generates the need of modelling of such behaviour and the understanding of them which gave the rise of different models achieved either by classical conceptual modelling or by those based on ontology. Modelling and analysing Trajectory Data are still challenging because of the heterogeneity of Trajectory Data models and the complexity of establishing choices about domain’s consensual knowledge. To fulfil this objective, we propose a generic ontology that explains the semantics of these Data and we define a Trajectory Data warehouse conceptual model based on the shared ontology in order to analyse Trajectory Data going from users’ short transactions to complex queries involving decision makers. The shared ontology that we propose is an OWL-DL formalism that covers common structures encountered in trajectories. We illustrate our work with a real case study.
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IIMSS - Automatic Generation of Trajectory Data Warehouse Schemas
Smart Innovation Systems and Technologies, 2016Co-Authors: Nouha Arfaoui, Jalel AkaichiAbstract:A mobile object is a spatial object that changes the form and the location permanently over the time. Each displacement creates a Trajectory that reflects the evolution of its position in space during a given time interval. It generates, then, a huge amount of Trajectory Data that are stored into Trajectory Data warehouse because it is the only tool that can analysis the historical Trajectory Data. In this work, we focus on the design of Trajectory Data warehouse schema and we propose automating this task to reduce human intervention since it is done manually and requires good knowledge of the domain. To achieve this goal, firstly, we automate the extraction of Trajectory Data mart schemas from a moving Data base. Then, we merge them to get the Trajectory Data warehouse schema using a new schema integration methodology that is composed by schema matching and schema mapping.
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Querying a multi-version Trajectory Data warehouse
International Journal of Business Information Systems, 2016Co-Authors: Wided Oueslati, Jalel AkaichiAbstract:Trajectory Data warehouses, which gather Data about mobile objects' activities may be autonomous and consequently may change frequently their structures and contents. This kind of change can alter the Trajectory Data warehouse associated schema, the analysis process and thus the decision making requirements on which the initial conceptual model is performed. For this reason, a TDW maintenance technique is needed to adapt the defined schema. In this paper, we will focus on maintaining a Trajectory Data warehouse after schema changes using for that the versioning approach which allows the existence of many versions of a same Trajectory Data warehouse. However, querying such Data warehouse versions is considered as a big challenge since Data returned by queries may vary according to queried versions. Hence, a solution to ensure Data mapping between different versions is required in order to give valid results necessary for providing elements leading to efficient decisions.
Benjamin C. M. Fung - One of the best experts on this subject based on the ideXlab platform.
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anonymizing Trajectory Data for passenger flow analysis
Transportation Research Part C-emerging Technologies, 2014Co-Authors: Moein Ghasemzadeh, Rui Chen, Benjamin C. M. Fung, Anjali AwasthiAbstract:The increasing use of location-aware devices provides many opportunities for analyzing and mining human mobility. The Trajectory of a person can be represented as a sequence of visited locations with different timestamps. Storing, sharing, and analyzing personal trajectories may pose new privacy threats. Previous studies have shown that employing traditional privacy models and anonymization methods often leads to low information quality in the resulting Data. In this paper we propose a method for achieving anonymity in a Trajectory Database while preserving the information to support effective passenger flow analysis. Specifically, we first extract the passenger flowgraph, which is a commonly employed representation for modeling uncertain moving objects, from the raw Trajectory Data. We then anonymize the Data with the goal of minimizing the impact on the flowgraph. Extensive experimental results on both synthetic and real-life Data sets suggest that the framework is effective to overcome the special challenges in Trajectory Data anonymization, namely, high dimensionality, sparseness, and sequentiality.
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Privacy-preserving Trajectory Data publishing by local suppression
Information Sciences, 2013Co-Authors: Rui Chen, Benjamin C. M. Fung, Bipin C. Desai, Noman Mohammed, Ke WangAbstract:The pervasiveness of location-aware devices has spawned extensive research in Trajectory Data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing Trajectory Data among different parties often creates an obstacle for effective Data mining. In this paper, we study the challenges of anonymizing Trajectory Data: high dimensionality, sparseness, and sequentiality. Employing traditional privacy models and anonymization methods often leads to low Data utility in the resulting Data and ineffective Data mining. In addressing these challenges, this is the first paper to introduce local suppression to achieve a tailored privacy model for Trajectory Data anonymization. The framework allows the adoption of various Data utility metrics for different Data mining tasks. As an illustration, we aim at preserving both instances of location-time doublets and frequent sequences in a Trajectory Database, both being the foundation of many Trajectory Data mining tasks. Our experiments on both synthetic and real-life Data sets suggest that the framework is effective and efficient to overcome the challenges in Trajectory Data anonymization. In particular, compared with the previous works in the literature, our proposed local suppression method can significantly improve the Data utility in anonymous Trajectory Data.
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Differentially Private Trajectory Data Publication
arXiv: Databases, 2011Co-Authors: Rui Chen, Benjamin C. M. Fung, Bipin C. DesaiAbstract:With the increasing prevalence of location-aware devices, Trajectory Data has been generated and collected in various application domains. Trajectory Data carries rich in- formation that is useful for many Data analysis tasks. Yet, improper publishing and use of Trajectory Data could jeopardize individual privacy. However, it has been shown that existing privacy-preserving Trajectory Data publishing methods derived from partition-based privacy models, for example k-anonymity, are unable to provide sufficient privacy protection. In this paper, motivated by the Data publishing scenario at the Societe de transport de Montreal (STM), the public transit agency in Montreal area, we study the problem of publishing Trajectory Data under the rigorous differential privacy model. We propose an efficient Data-dependent yet differentially private sanitization algorithm, which is applicable to different types of Trajectory Data. The efficiency of our approach comes from adaptively narrowing down the output domain by building a noisy prefix tree based on the underlying Data. Moreover, as a post-processing step, we make use of the inherent constraints of a prefix tree t o conduct constrained inferences, which lead to better utility. This is the first paper to introduce a practical solution for publi shing large volume of Trajectory Data under differential privacy. We examine the utility of sanitized Data in terms of count queries and frequent sequential pattern mining. Extensive experiments on real-life Trajectory Data from the STM demonstrate that our approach maintains high utility and is scalable to large Trajectory Datasets.
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walking in the crowd anonymizing Trajectory Data for pattern analysis
Conference on Information and Knowledge Management, 2009Co-Authors: Noman Mohammed, Benjamin C. M. Fung, Mourad DebbabiAbstract:Recently, Trajectory Data mining has received a lot of attention in both the industry and the academic research. In this paper, we study the privacy threats in Trajectory Data publishing and show that traditional anonymization methods are not applicable for Trajectory Data due to its challenging properties: high-dimensional, sparse, and sequential. Our primary contributions are (1) to propose a new privacy model called LKC-privacy that overcomes these challenges, and (2) to develop an efficient anonymization algorithm to achieve LKC-privacy while preserving the information utility for Trajectory pattern mining.
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CIKM - Walking in the crowd: anonymizing Trajectory Data for pattern analysis
Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09, 2009Co-Authors: Noman Mohammed, Benjamin C. M. Fung, Mourad DebbabiAbstract:Recently, Trajectory Data mining has received a lot of attention in both the industry and the academic research. In this paper, we study the privacy threats in Trajectory Data publishing and show that traditional anonymization methods are not applicable for Trajectory Data due to its challenging properties: high-dimensional, sparse, and sequential. Our primary contributions are (1) to propose a new privacy model called LKC-privacy that overcomes these challenges, and (2) to develop an efficient anonymization algorithm to achieve LKC-privacy while preserving the information utility for Trajectory pattern mining.
Bipin C. Desai - One of the best experts on this subject based on the ideXlab platform.
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Privacy-preserving Trajectory Data publishing by local suppression
Information Sciences, 2013Co-Authors: Rui Chen, Benjamin C. M. Fung, Bipin C. Desai, Noman Mohammed, Ke WangAbstract:The pervasiveness of location-aware devices has spawned extensive research in Trajectory Data mining, resulting in many important real-life applications. Yet, the privacy issue in sharing Trajectory Data among different parties often creates an obstacle for effective Data mining. In this paper, we study the challenges of anonymizing Trajectory Data: high dimensionality, sparseness, and sequentiality. Employing traditional privacy models and anonymization methods often leads to low Data utility in the resulting Data and ineffective Data mining. In addressing these challenges, this is the first paper to introduce local suppression to achieve a tailored privacy model for Trajectory Data anonymization. The framework allows the adoption of various Data utility metrics for different Data mining tasks. As an illustration, we aim at preserving both instances of location-time doublets and frequent sequences in a Trajectory Database, both being the foundation of many Trajectory Data mining tasks. Our experiments on both synthetic and real-life Data sets suggest that the framework is effective and efficient to overcome the challenges in Trajectory Data anonymization. In particular, compared with the previous works in the literature, our proposed local suppression method can significantly improve the Data utility in anonymous Trajectory Data.
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Differentially Private Trajectory Data Publication
arXiv: Databases, 2011Co-Authors: Rui Chen, Benjamin C. M. Fung, Bipin C. DesaiAbstract:With the increasing prevalence of location-aware devices, Trajectory Data has been generated and collected in various application domains. Trajectory Data carries rich in- formation that is useful for many Data analysis tasks. Yet, improper publishing and use of Trajectory Data could jeopardize individual privacy. However, it has been shown that existing privacy-preserving Trajectory Data publishing methods derived from partition-based privacy models, for example k-anonymity, are unable to provide sufficient privacy protection. In this paper, motivated by the Data publishing scenario at the Societe de transport de Montreal (STM), the public transit agency in Montreal area, we study the problem of publishing Trajectory Data under the rigorous differential privacy model. We propose an efficient Data-dependent yet differentially private sanitization algorithm, which is applicable to different types of Trajectory Data. The efficiency of our approach comes from adaptively narrowing down the output domain by building a noisy prefix tree based on the underlying Data. Moreover, as a post-processing step, we make use of the inherent constraints of a prefix tree t o conduct constrained inferences, which lead to better utility. This is the first paper to introduce a practical solution for publi shing large volume of Trajectory Data under differential privacy. We examine the utility of sanitized Data in terms of count queries and frequent sequential pattern mining. Extensive experiments on real-life Trajectory Data from the STM demonstrate that our approach maintains high utility and is scalable to large Trajectory Datasets.
Xuesong Zhou - One of the best experts on this subject based on the ideXlab platform.
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Trajectory Data-based traffic flow studies: A revisit
Transportation Research Part C: Emerging Technologies, 2020Co-Authors: Rui Jiang, Xiqun Chen, Xuesong ZhouAbstract:Abstract In this paper, we review Trajectory Data-based traffic flow studies that have been conducted over the last 15 years. Our purpose is to provide a roadmap for readers who have an interest in the latest developments of traffic flow theory that have been stimulated by the availability of Trajectory Data. We first highlight the critical role of Trajectory Data (especially the next generation simulation (NGSIM) Trajectory Dataset) in the recent history of traffic flow studies. Then, we summarize new traffic phenomena/models at the microscopic/mesoscopic/macroscopic levels and provide a unified view of these achievements perceived from different directions of traffic flow studies. Finally, we discuss some future research directions.