Graph Similarity-based Hierarchical Clustering of Trajectory Data

Sabarish, B. A.; Karthi, R.; Kumar, T. Gireesh

doi:10.1016/j.procs.2020.04.004

Cited by 21 publications

(16 citation statements)

References 10 publications

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“…The k-anonymity method mainly used in Wu et al [19] to enhance privacy protection, and used clustering technology to group users by learning their trajectory data. A graph based trajectory data representation model [20] was proposed, the similarity between trajectories was calculated using the measurement method based on edges and vertices, and similar trajectories were clustered and identified based on paths. Clustering can represent the users' activity rules in some time, and can remove the location with low access frequency, so it has high flexibility.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

Wang

Guo

et al. 2022

Preprint

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Location-based services (LBSS) are widely used with the rapid development of mobile devices and location technology. LBSs not only brings convenience to life, but also brings privacy threats to users. Users upload accurate location information to LBSs to obtain the corresponding services. However, uploading unprocessed location data will directly cause the leakage of users’ privacy information. In this work, an efficient location protection method based on differential privacy is proposed. First of all, the users’ locations information is merged based on the distance and density relationship between multiple groups of continuous locations, and a location clustering (L-cluster) algorithm is proposed to divide the continuous locations into various clusters. Then, differential privacy based location protection algorithm (DPLPA) is proposed to efficiently preserve users’ location privacy, where Laplace noise conforming to the differential privacy mechanism is added to resident points and centroids in the cluster to protect the location privacy. Experimental results show that DPLPA gets high data utility and low time consumption while protecting the privacy of location information.

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Section: Related Workmentioning

confidence: 99%

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

Wang

Guo

et al. 2022

Preprint

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“…The dual graph has then been utilized in a label-based clustering approach to cluster movement trajectories and addresses the main limitation of distance-based trajectory clustering methods. Sabarish et al [74] proposed a hierarchical clustering method based on the graph data structure to identify similar movement patterns of movement trajectories for moving trucks carrying goods. Gómez et al [27] proposed a spatiotemporal graph data structure and transformed users' movement trajectories from the location-based social network to perform Online Analytical Processing operations on movement trajectories.…”

Section: Trajectory Representationmentioning

confidence: 99%

A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML

Ojagh

Saeedi

Liang

2020

IJGI

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With the wide availability of low-cost proximity sensors, a large body of research focuses on digital person-to-person contact tracing applications that use proximity sensors. In most contact tracing applications, the impact of SARS-CoV-2 spread through touching contaminated surfaces in enclosed places is overlooked. This study is focused on tracing human contact within indoor places using the open OGC IndoorGML standard. This paper proposes a graph-based data model that considers the semantics of indoor locations, time, and users’ contexts in a hierarchical structure. The functionality of the proposed data model is evaluated for a COVID-19 contact tracing application with scalable system architecture. Indoor trajectory preprocessing is enabled by spatial topology to detect and remove semantically invalid real-world trajectory points. Results show that 91.18% percent of semantically invalid indoor trajectory data points are filtered out. Moreover, indoor trajectory data analysis is innovatively empowered by semantic user contexts (e.g., disinfecting activities) extracted from user profiles. In an enhanced contact tracing scenario, considering the disinfecting activities and sequential order of visiting common places outperformed contact tracing results by filtering out unnecessary potential contacts by 44.98 percent. However, the average execution time of person-to-place contact tracing is increased by 58.3%.

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“…Cavojsky et al used the Needleman Wunsch algorithm to align the paths that had previously been converted into alphabetic letter sequences. Sabarish et al convert the trajectories into a graph and measure the similarity of the route based on edges and vertices (Sabarish et al, 2020). The similarity of the trajectory can be calculated using approximation equations built using a regression model or interpolation.…”

Section: Introductionmentioning

confidence: 99%

Similarity Analysis of User Trajectories Based on Haversine Distance and Needleman Wunsch Algorithm

Jamhuri

Irawan

Mukhlash

2022

EKW

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In this paper, we discuss the similarity between two trajectories using the Needleman Wunsch algorithm. The calculation steps are interpolating the trajectory, calculating the distance between the trajectory coordinates, identifying the equivalent length, transforming trajectories into a sequence of alphabetic letters, aligning the sequences, and measuring the magnitude of the similarity based on the alignment results. The similarity obtained is compared directly to the length of the trajectories shared by the two lines. The calculation results show that the accuracy of the alignment method reaches more than 90%. Abstrak: Dalam tulisan ini dibahas cara perhitungan persentase kesamaan dari dua buah lintasan menggunakan algoritma Needleman Wunsch dan perhitungan secara manual berdasarkan irisan dari lintasan-lintasan tersebut. Pada perhitungan menggunakan algoritma Needleman Wunsch, tahapan-tahapan yang dilakukan adalah menginterpolasi lintasan, menghitung jarak antara titik-titik koordinat dari kedua lintasan, mengidentifikasi jarak yang ekivalen, mengubah lintasan menjadi sekuens huruf alfabet, menyejajarkan sekuens, dan menentukan besarnya kesamaan berdasarkan hasil penyejajaran. Kesamaan yang diperoleh dari metode penyejajaran dibandingkan secara langsung dengan panjang jalur yang dilalui bersama oleh kedua lintasan, hasil perhitungan menunjukkan bahwa akurasi metode penyejajaran mencapai lebih dari 90%.

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Graph Similarity-based Hierarchical Clustering of Trajectory Data

Cited by 21 publications

References 10 publications

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

A Person-to-Person and Person-to-Place COVID-19 Contact Tracing System Based on OGC IndoorGML

Similarity Analysis of User Trajectories Based on Haversine Distance and Needleman Wunsch Algorithm

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