Indoor data management

Lu, Hua; Cheema, Muhammad Aamir

doi:10.1109/icde.2016.7498358

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Such statistics may be based on data gathered from several sources: vision systems [1], [2], [3], indoor-positioning [4], [5] or mobile apps. The most interesting (and challenging) is the possibility of integrating data from multiple sources [6] to gather even more commercially valuable insight.…”

Section: A Principle Of Operationmentioning

confidence: 99%

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Ludziejewski

Grad

Przebinda³

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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Embedded platforms with GPU acceleration, designed for performing machine learning on the edge, enabled the creation of inexpensive and pervasive computer vision systems. Smartphones are nowadays widely used for profiling and tracking in marketing, based on WiFi data or beacon-based positioning systems. We present the Arahub system, which aims at integrating world of computer vision systems with smartphone tracking for delivering data useful in interactive applications, such as interactive advertisements. In this paper we present the architecture of the Arahub system and provide insight about its particular elements. Our preliminary results, obtained from reallife test environments and scenarios, show that the Arahub system is able to accurately assign smartphones to their bearers, based on visual and WiFi/Bluetooth positioning data. We show the commercial value of such system and its potential applications.

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Section: A Principle Of Operationmentioning

confidence: 99%

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Ludziejewski

Grad

Przebinda³

et al. 2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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“…Indoor space has been studied for years [4][5][6][7]. In general, there are three kinds of indoor entities in indoor space, including indoor elements, sensor deployment, and moving object data.…”

Section: Preliminariesmentioning

confidence: 99%

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Cui

2020

Information

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The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average.

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“…At present, all walks of life, including the fields of map, are faced with the problem that huge data is difficult to store and complex to query [4]. Since the data sets of a map scene are very large, they are not able to be stored directly by the indexes.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Technique of the 3D Indoor Map Data Based on an Improved Octree Structure

Wang

et al. 2020

Mathematical Problems in Engineering

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The construction and retrieval of indoor maps are important for indoor positioning and navigation. It is necessary to ensure a good user experience while meeting real-time requirements. Unlike outdoor maps, indoor space is limited, and the relationship between indoor objects is complex which would result in an uneven indoor data distribution and close relationship between the data. A data storage model based on the octree scene segmentation structure was proposed in this paper initially. The traditional octree structure data storage model has been improved so that the data could be backtracked. The proposed method will solve the problem of partition lines within the range of the object data and improve the overall storage efficiency. Moreover, a data retrieval algorithm based on octree storage structure was proposed. The algorithm adopts the idea of “searching for a point, points around the searched point are within the searching range.” Combined with the octree neighbor retrieval methods, the closure constraints are added. Experimental results show that using the improved octree storage structure, the retrieval cost is 1/8 of R-tree. However, by using the neighbor retrieval, it improved the search efficiency by about 27% on average. After adding the closure constraint, the retrieval efficiency increases by 25% on average.

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Indoor data management

Cited by 5 publications

References 43 publications

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Integrated Human Tracking Based on Video and Smartphone Signal Processing within the Arahub System

Discovering Influential Positions in RFID-Based Indoor Tracking Data

An Optimization Technique of the 3D Indoor Map Data Based on an Improved Octree Structure

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