Finding dense locations in symbolic indoor tracking data: modeling, indexing, and processing

Ahmed, Tanvir; Pedersen, Torben Bach; Lu, Hua

doi:10.1007/s10707-016-0276-8

Cited by 12 publications

(23 citation statements)

References 23 publications

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“…We compare our method with two indoor flow computing methods [3], [4], [27] using RFID data. One method [3], [4] is specialized for semi-constrained indoor movement environments (e.g., convey belt systems) where each semantic location features one entry and one exit, both having an RFID reader. Such a strict setting enables to count objects in a location within a past time interval.…”

Section: Top-k Results Effectivenessmentioning

confidence: 99%

“…We deploy ordinary RFID readers with 3-meter detection range [41] at doors. Following the experimental settings in [4], [27], reader detection ranges do not overlap in our setting. As a result, some doors are associated with no reader.…”

Section: Top-k Results Effectivenessmentioning

confidence: 99%

“…People may stay in the same place for a long time and their positions only change slightly, resulting in a sequence of sample sets that contain the identical set of P-locations. In this case, we should merge such consecutive sample sets into one because they point to almost the same underlying whereabouts within a time period 4 . We call this operation inter-merge.…”

Section: Data Reduction Methodsmentioning

confidence: 99%

“…On the other hand, it is useful to reduce the sequence length n. People may stay around the same place for a long time and their positions only have slight changes, resulting in a sequence of sample sets that contain the identical set of P-locations. 4 By an inter-merge, such sample sets can be sequentially merged to reduce the sequence length. 5 .…”

Section: Data Reductionmentioning

confidence: 99%

“…Such procedure can be easily integrated with the aforementioned merge operations. Note that Algorithm 1 filters those invalid paths that do not satisfy the indoor spatial topology 4. The flow values defined in this study are independent of the dwell time of an object at a particular location.…”

Section: _ T 1 T 3 T 5 T 6 T 1 T 3 T 5 T 6 T 1 T 3 T 5 T 6 T 1 T 3 T mentioning

confidence: 99%

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Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Shou

et al. 2019

IEEE Trans. Knowl. Data Eng.

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Knowing popular indoor locations can benefit many applications like exhibition planning and location-based advertising, among others. In this work, we use uncertain historical indoor mobility data to find the top-k popular indoor semantic locations with the highest flow values. In the data we use, an object positioning report contains a set of samples, each consisting of an indoor location and a corresponding probability. The problem is challenging due to the difficulty in obtaining reliable flow values and the heavy computational workload on probabilistic samples for large numbers of objects. To address the first challenge, we propose an indoor flow definition that takes into account both data uncertainty and indoor topology. To efficiently compute flows for individual indoor semantic locations, we design data structures for facilitating accessing the relevant data, a data reduction method that reduces the intermediate data to process, and an overall flow computing algorithm. Furthermore, we design search algorithms for finding the top-k popular indoor semantic locations. All proposals are evaluated extensively on real and synthetic data. The evaluation results show that our data reduction method significantly reduces the data volume in computing, our search algorithms are efficient and scalable, and the top-k popular semantic locations returned are in good accord with ground truth.

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Section: Top-k Results Effectivenessmentioning

confidence: 99%

Section: Top-k Results Effectivenessmentioning

confidence: 99%

Section: Data Reduction Methodsmentioning

confidence: 99%

Section: Data Reductionmentioning

confidence: 99%

Section: _ T 1 T 3 T 5 T 6 T 1 T 3 T 5 T 6 T 1 T 3 T 5 T 6 T 1 T 3 T mentioning

confidence: 99%

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Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Shou

et al. 2019

IEEE Trans. Knowl. Data Eng.

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Spatial Queries for Indoor Location-based Services

Liu

2022

2022 23rd IEEE International Conference on Mobile Data Management (MDM)

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In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data

Shou

et al. 2018

IEEE Trans. Knowl. Data Eng.

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As people spend significant parts of daily lives indoors, it is useful and important to measure indoor densities and find the dense regions in many indoor scenarios like space management and security control. In this paper, we propose a data-driven approach that finds top-k indoor dense regions by using indoor positioning data. Such data is obtained by indoor positioning systems working at a relatively low frequency, and the reported locations in the data are discrete, from a preselected location set that does not continuously cover the entire indoor space. When a search is triggered, the object positioning information is already out-of-date and thus object locations are uncertain. To this end, we first integrate object location uncertainty into the definitions for counting objects in an indoor region and computing its density. Subsequently, we conduct a thorough analysis of the location uncertainty in the context of complex indoor topology, deriving upper and lower bounds of indoor region densities and introducing distance decaying effect into computing concrete indoor densities. Enabled by the uncertainty analysis outcomes, we design efficient search algorithms for solving the problem. Finally, we conduct extensive experimental studies on our proposals using synthetic and real data. The experimental results verify that the proposed search approach is efficient, scalable, and effective. The top-k indoor dense regions returned by our search are considerably consistent with the ground truth, despite that the search uses neither historical data nor extra knowledge about objects.

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Finding dense locations in symbolic indoor tracking data: modeling, indexing, and processing

Cited by 12 publications

References 23 publications

Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Spatial Queries for Indoor Location-based Services

In Search of Indoor Dense Regions: An Approach Using Indoor Positioning Data

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