Finding Dense Locations in Indoor Tracking Data

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

doi:10.1109/mdm.2014.29

Cited by 18 publications

(17 citation statements)

References 8 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%

“…Referring to the example in Figure 1, for a given P-location p 4 , we find p 6 ≡ p 8 in searching c 6 and p 4 ≡ p 9 in searching {c 1 , c 6 }. To eliminate such redundancy, we can merge those equivalent P-locations sharing a common edge in G ISL , and construct a new M -by-M matrix 3 , where M = |E| is the number of edges in G ISL and M ≤ N . By this merge, we can downsize M IL and consequently reduce the scalability of the possible paths to be generated.…”

Section: Indoor Location Matrixmentioning

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: Indoor Location Matrixmentioning

confidence: 99%

Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Shou

et al. 2019

IEEE Trans. Knowl. Data Eng.

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“…Finding the dense locations in large indoor spaces is very useful for getting overloaded locations, security, crowd management, indoor navigation, and guidance. Recently, there have been some researches on this topic such as [78,79].…”

Section: Privacy Protection In Indoor Spacementioning

confidence: 99%

Towards Mobile Information Systems for Indoor Space

Zhang

Jin

Yue

et al. 2016

Mobile Information Systems

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With the rapid development of Internet of things (IOT) and indoor positioning technologies such as Wi-Fi and RFID, indoor mobile information systems have become a new research hotspot. Based on the unique features of indoor space and urgent needs on indoor mobile applications, in this paper we analyze some key issues in indoor mobile information systems, including positioning technologies in indoor environments, representation models for indoor spaces, query processing techniques for indoor moving objects, and index structures for indoor mobile applications. Then, we present an indoor mobile information management system named IndoorDB. Finally, we give some future research topics about indoor mobile information systems.

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“…RTR-tree and TP 2 R-tree [22] extend the traditional R-tree to index historical trajectories of indoor moving objects that are captured by RFID-type technologies. The Dense Location Time Index (DLT-Index) [2] indexes historical RFID dwelling records of indoor objects in order to support fast finding of dense semantic locations. Hash-based indexes [47], [48] are used to index online indoor moving objects under RFID-type indoor positioning.…”

Section: B Existing Researchmentioning

confidence: 99%

Indoor data management

Cheema

2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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A large part of modern life is lived indoors such as in homes, offices, shopping malls, universities, libraries and airports. However, almost all of the existing location-based services (LBS) have been designed only for outdoor space. This is mainly because the global positioning system (GPS) and other positioning technologies cannot accurately identify the locations in indoor venues. Some recent initiatives have started to cross this technical barrier, promising huge future opportunities for research organizations, government agencies, technology giants, and enterprizing start-ups -to exploit the potential of indoor LBS. Consequently, indoor data management has gained significant research attention in the past few years and the research interest is expected to surge in the upcoming years. This will result in a broad range of indoor applications including emergency services, public services, in-store advertising, shopping, tracking, guided tours, and much more. In this tutorial, we first highlight the importance of indoor data management and the unique challenges that need to be addressed. Subsequently, we provide an overview of the existing research in indoor data management, covering modeling, cleansing, indexing, querying, and other relevant topics. Finally, we discuss the future research directions in this important and growing research area, discussing spatial-textual search, integrating outdoor and indoor spaces, uncertain indoor data, and indoor trajectory mining.

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Finding Dense Locations in Indoor Tracking Data

Cited by 18 publications

References 8 publications

Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Finding Most Popular Indoor Semantic Locations Using Uncertain Mobility Data

Towards Mobile Information Systems for Indoor Space

Indoor data management

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