Towards fusing uncertain location data from heterogeneous sources

Zhang, Bing; Trajcevski, Goce; Liu, Liu

doi:10.1007/s10707-015-0238-6

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…Zhang et al [46] also build a location aware dataset for their study by making a fusion from different sources.…”

Section: Methodsmentioning

confidence: 99%

Modeling Weather-Aware Prediction of User Activities and Future Visits

Nawshin¹,

Mukta²,

Ali³

et al. 2020

IEEE Access

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In recent years, Location-Based Social Networking (LBSN) sites such as Foursquare, Facebook Places, and Twitter have become extremely popular due to the extensive usage of locationenabled smart phone technologies. These LBSNs allow users to post their check-ins that provide important set of information about users' activities and preferences. Several existing research works predict users' activities from social media check-ins data considering various aspects such as time, venue, and occurrences. However, none of the earlier studies investigate the influence of weather on users' preferences of activities and mode of transportation preferences. Psychological studies show that weather has a strong influence on human activities. In this paper, we predict users' travel mode, day/night time activities and future visit from weather condition derived from social media check-ins. In particular, we develop several classification models to predict users' preferable mode of transportation, day/night activities, and future visits from the users' check-ins based on different weather conditions. We use two real datasets: Tokyo and New York city to validate our models. Our classifiers achieve substantial strength (on an average AUC of 72.77%) to predict users' mode of transportation, day/night activities and their future visit preferences for Tokyo dataset. We also compare performance of the classifiers developed by these two datasets.

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“…Zhang et al [46] also build a location aware dataset for their study by making a fusion from different sources.…”

Section: Methodsmentioning

confidence: 99%

Modeling Weather-Aware Prediction of User Activities and Future Visits

Nawshin¹,

Mukta²,

Ali³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

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“…In the location-time space, the shape of the ellipse becomes a bead (also known as space-time prism [77,100]) as an integrated body of an upward and a downward pointing cone [90,176], and the bead sequence for a discrete trajectory forms a "necklace" [81,99,145,205,268]. Speed-constrained beads can be further refined by fusing spatial constraints derived from additional sensory data (e.g., road-side sensor data [257]). Beyond the speed constraint, Markovian dependencies along a trajectory are exploited such that unobserved locations can be instantiated by a stochastic process over observed locations.…”

Section: Queries Over Low-quality Sidmentioning

confidence: 99%

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

Jensen

et al. 2022

ACM Comput. Surv.

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With the continued deployment of the Internet of Things (IoT) , increasing volumes of devices are being deployed that emit massive spatially referenced data. Due in part to the dynamic, decentralized, and heterogeneous architecture of the IoT, the varying and often low quality of spatial IoT data (SID) presents challenges to applications built on top of this data. This survey aims to provide unique insight to practitioners who intend to develop IoT-enabled applications and to researchers who wish to conduct research that relates to data quality in the IoT setting. The survey offers an inventory analysis of major data quality dimensions in SID and covers significant data characteristics and associated quality considerations. The survey summarizes data quality related technologies from both task and technique perspectives. Organizing the technologies from the task perspective, it covers recent progress in SID quality management, encompassing location refinement, uncertainty elimination, outlier removal, fault correction, data integration, and data reduction; and it covers low-quality SID exploitation, encompassing querying, analysis, and decision-making techniques. Finally, the survey covers emerging trends and open issues concerning the quality of SID.

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“…We plan to consider other kinds of learning approaches in conjunction with different clustering techniques. We also plan for a more thorough treatment of the (impact of the) GPS uncertainty/error [14] to the speed cluster mining algorithm. During the lane labeling process, large GPS errors will incur potential mislabeling, which later lead to noise points in the merging phase.…”

Section: Experiments and Concluding Remarksmentioning

confidence: 99%

Clustering Speed in Multi-lane Traffic Networks

Zhang

Trajcevski

Liu

2016

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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We address the problem of efficient spatio-temporal clustering of speed data in road segments with multiple lanes. We postulate that the navigation/route plans typically reported by different providers as a single-value need not be accurate in multi-lane networks. Our methodology generates laneaware distribution of speed from GPS data and agglomerates the basic space and time units into larger clusters. Thus, we achieve a compact description of speed variations which can be subsequently used for more accurate trips planning. We provide experiments that demonstrate the benefits of our proposed approaches.

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Towards fusing uncertain location data from heterogeneous sources

Cited by 9 publications

References 34 publications

Modeling Weather-Aware Prediction of User Activities and Future Visits

Modeling Weather-Aware Prediction of User Activities and Future Visits

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

Clustering Speed in Multi-lane Traffic Networks

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