A New Approach to Measuring the Similarity of Indoor Semantic Trajectories

People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.

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A Simplified CityGML-Based 3D Indoor Space Model for Indoor Applications

Applied Sciences ◽

10.3390/app10207218 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7218

Author(s):

Qun Sun ◽

Xiaoguang Zhou ◽

Dongyang Hou

Keyword(s):

Semantic Information ◽

Indoor Navigation ◽

Geometric Information ◽

Indoor Space ◽

Emergency Rescue ◽

Interior Spaces ◽

Topological Relationships ◽

Building Components ◽

Relationship Of ◽

Indoor Spaces

With the continuous development of indoor positioning technology, various indoor applications, such as indoor navigation and emergency rescue, have gradually received widespread attention. Indoor navigation and emergency rescue require access to a variety of indoor space information, such as accurate geometric information, rich semantic information and indoor spatial adjacency information; hence, a suitable 3D indoor model is needed. However, the available models, such as BIM and CityGML, mainly represent geometric and semantic information of indoor spaces, and rarely describe the topological adjacency relationship of interior spaces. To address the requirements of indoor navigation and emergency rescue, a simplified 3D indoor model is proposed in this research. The building components and indoor functional spaces of buildings are described in a simplified way. The geometric and semantic information are described based on CityGML, and the topological relationships of indoor adjacent spaces are represented by CityGML XLinks. While describing the indoor level of detail (LOD) of buildings in detail, the model simplifies building components and indoor spaces, which can preserve the characteristics of indoor spaces to the maximum extent and serve as a basis for indoor applications.

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Development of an Indoor Space Semantic Model and Its Implementation as an IndoorGML Extension

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8080333 ◽

2019 ◽

Vol 8 (8) ◽

pp. 333 ◽

Cited By ~ 5

Author(s):

Nishith Maheshwari ◽

Srishti Srivastava ◽

Krishnan Sundara Rajan

Keyword(s):

Semantic Information ◽

Classification Model ◽

Semantic Model ◽

Data Format ◽

Building Evacuation ◽

Indoor Space ◽

Physical Representation ◽

History Of ◽

Space Semantic ◽

Indoor Spaces

Geospatial data capture and handling of indoor spaces is increasing over the years and has had a varied history of data sources ranging from architectural and building drawings to indoor data acquisition approaches. While these have been more data format and information driven primarily for the physical representation of spaces, it is important to note that many applications look for the semantic information to be made available. This paper proposes a space classification model leading to an ontology for indoor spaces that accounts for both the semantic and geometric characteristics of the spaces. Further, a Space semantic model is defined, based on this ontology, which can then be used appropriately in multiple applications. To demonstrate the utility of the model, we also present an extension to the IndoorGML data standard with a set of proposed classes that can help capture both the syntactic and semantic components of the model. It is expected that these proposed classes can be appropriately harnessed for use in diverse applications ranging from indoor data visualization to more user customised building evacuation path planning with a semantic overtone.

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Relative Hausdorff distance for network analysis

Applied Network Science ◽

10.1007/s41109-019-0198-0 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Sinan G. Aksoy ◽

Kathleen E. Nowak ◽

Emilie Purvine ◽

Stephen J. Young

Keyword(s):

Machine Learning ◽

Network Analysis ◽

Similarity Measure ◽

Hausdorff Distance ◽

Data Science ◽

Edit Distance ◽

Similarity Measures ◽

Graph Edit Distance ◽

Computationally Intensive

Abstract Similarity measures are used extensively in machine learning and data science algorithms. The newly proposed graph Relative Hausdorff (RH) distance is a lightweight yet nuanced similarity measure for quantifying the closeness of two graphs. In this work we study the effectiveness of RH distance as a tool for detecting anomalies in time-evolving graph sequences. We apply RH to cyber data with given red team events, as well to synthetically generated sequences of graphs with planted attacks. In our experiments, the performance of RH distance is at times comparable, and sometimes superior, to graph edit distance in detecting anomalous phenomena. Our results suggest that in appropriate contexts, RH distance has advantages over more computationally intensive similarity measures.

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Clustering Indoor Positioning Data Using E-DBSCAN

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10100669 ◽

2021 ◽

Vol 10 (10) ◽

pp. 669

Author(s):

Dayu Cheng ◽

Guo Yue ◽

Tao Pei ◽

Mingbo Wu

Keyword(s):

Semantic Information ◽

Indoor Positioning ◽

Base Station ◽

Behavioral Pattern ◽

Shopping Behavior ◽

Shopping Mall ◽

Indoor Environments ◽

Clustering Methods ◽

Trajectory Data ◽

Indoor Location

Indoor positioning data reflects human mobility in indoor spaces. Revealing patterns of indoor trajectories may help us understand human indoor mobility. Clustering methods, which are based on the measurement of similarity between trajectories, are important tools for identifying those patterns. However, due to the specific characteristics of indoor trajectory data, it is difficult for clustering methods to measure the similarity between trajectories. These characteristics are manifested in two aspects. The first is that the nodes of trajectories may have clear semantic attributes; for example, in a shopping mall, the node of a trajectory may contain information such as the store type and visit duration time, which may imply a customer’s interest in certain brands. The semantic information can only be obtained when the position precision is sufficiently high so that the relationship between the customer and the store can be determined, which is difficult to realize for outdoor positioning, either using GPS or mobile base station, due to the relatively large positioning error. If the tendencies of customers are to be considered, the similarity of geometrical morphology does not reflect the real similarity between trajectories. The second characteristic is the complex spatial shapes of indoor trajectory caused by indoor environments, which include elements such as closed spaces, multiple obstacles and longitudinal extensions. To deal with these challenges caused by indoor trajectories, in this article we proposed a new method called E-DBSCAN, which extended DBSCAN to trajectory clustering of indoor positioning data. First, the indoor location data were transformed into a sequence of residence points with rich semantic information, such as the type of store customer visited, stay time and spatial location of store. Second, a Weighted Edit Distance algorithm was proposed to measure the similarity of the trajectories. Then, an experiment was conducted to verify the correctness of E-DBSCAN using five days of positioning data in a shopping mall, and five shopping behavior patterns were identified and potential explanations were proposed. In addition, a comparison was conducted among E-DBSCAN, the k-means and DBSCAN algorithms. The experimental results showed that the proposed method can discover customers’ behavioral pattern in indoor environments effectively.

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Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

Remote Sensing ◽

10.3390/rs10111815 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1815 ◽

Cited By ~ 5

Author(s):

Ahmed Elseicy ◽

Shayan Nikoohemat ◽

Michael Peter ◽

Sander Oude Elberink

Keyword(s):

Real World ◽

Point Cloud ◽

Laser Scanning ◽

Semantic Information ◽

State Of The Art ◽

Laser Scanner ◽

Indoor Space ◽

Laser Scanners ◽

Real World Datasets ◽

Indoor Spaces

State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases.

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MATHURA (MBI) - A NOVEL IMPUTATION MEASURE FOR IMPUTATION OF MISSING VALUES IN MEDICAL DATASETS

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191216123352 ◽

2019 ◽

Vol 13 ◽

Author(s):

B. Mathura Bai ◽

N. Mangathayaru ◽

B. Padmaja Rani ◽

Shadi Aljawarneh

Keyword(s):

Similarity Measure ◽

Medical Records ◽

Missing Values ◽

Similarity Measures ◽

Common Problems ◽

Experiment Analysis

: Missing attribute values in medical datasets are one of the most common problems faced when mining medical datasets. Estimation of missing values is a major challenging task in pre-processing of datasets. Any wrong estimate of missing attribute values can lead to inefficient and improper classification thus resulting in lower classifier accuracies. Similarity measures play a key role during the imputation process. The use of an appropriate and better similarity measure can help to achieve better imputation and improved classification accuracies. This paper proposes a novel imputation measure for finding similarity between missing and non-missing instances in medical datasets. Experiments are carried by applying both the proposed imputation technique and popular benchmark existing imputation techniques. Classification is carried using KNN, J48, SMO and RBFN classifiers. Experiment analysis proved that after imputation of medical records using proposed imputation technique, the resulting classification accuracies reported by the classifiers KNN, J48 and SMO have improved when compared to other existing benchmark imputation techniques.

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A comparative analysis of trajectory similarity measures

GIScience & Remote Sensing ◽

10.1080/15481603.2021.1908927 ◽

2021 ◽

pp. 1-27

Author(s):

Yaguang Tao ◽

Alan Both ◽

Rodrigo I. Silveira ◽

Kevin Buchin ◽

Stef Sijben ◽

...

Keyword(s):

Comparative Analysis ◽

Similarity Measures ◽

Trajectory Similarity

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Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽

10.3390/s21103493 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3493

Author(s):

Gahyeon Lim ◽

Nakju Doh

Keyword(s):

Point Cloud ◽

Dimensional Space ◽

Three Dimensional ◽

Reconstruction Method ◽

Complex Environments ◽

Indoor Space ◽

Multi Level ◽

Level Building ◽

Indoor Spaces ◽

Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

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A set theory based similarity measure for text clustering and classification

Journal Of Big Data ◽

10.1186/s40537-020-00344-3 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 1

Author(s):

Ali A. Amer ◽

Hassan I. Abdalla

Keyword(s):

Set Theory ◽

Similarity Measure ◽

Similarity Measures ◽

Text Clustering ◽

Plagiarism Detection ◽

K Nearest Neighbor ◽

Single Measure ◽

Highly Effective ◽

Clustering And Classification ◽

Effectiveness And Efficiency

Abstract Similarity measures have long been utilized in information retrieval and machine learning domains for multi-purposes including text retrieval, text clustering, text summarization, plagiarism detection, and several other text-processing applications. However, the problem with these measures is that, until recently, there has never been one single measure recorded to be highly effective and efficient at the same time. Thus, the quest for an efficient and effective similarity measure is still an open-ended challenge. This study, in consequence, introduces a new highly-effective and time-efficient similarity measure for text clustering and classification. Furthermore, the study aims to provide a comprehensive scrutinization for seven of the most widely used similarity measures, mainly concerning their effectiveness and efficiency. Using the K-nearest neighbor algorithm (KNN) for classification, the K-means algorithm for clustering, and the bag of word (BoW) model for feature selection, all similarity measures are carefully examined in detail. The experimental evaluation has been made on two of the most popular datasets, namely, Reuters-21 and Web-KB. The obtained results confirm that the proposed set theory-based similarity measure (STB-SM), as a pre-eminent measure, outweighs all state-of-art measures significantly with regards to both effectiveness and efficiency.

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Evaluating the effect of compressing algorithms for trajectory similarity and classification problems

GeoInformatica ◽

10.1007/s10707-021-00434-1 ◽

2021 ◽

Author(s):

Antonios Makris ◽

Camila Leite da Silva ◽

Vania Bogorny ◽

Luis Otavio Alvares ◽

Jose Antonio Macedo ◽

...

Keyword(s):

Trajectory Analysis ◽

Similarity Measures ◽

Classification Problems ◽

Trajectory Data ◽

Compression Algorithms ◽

Time Ratio ◽

Ratio Speed ◽

Trajectory Similarity ◽

Real World Datasets ◽

The Impact

AbstractDuring the last few years the volumes of the data that synthesize trajectories have expanded to unparalleled quantities. This growth is challenging traditional trajectory analysis approaches and solutions are sought in other domains. In this work, we focus on data compression techniques with the intention to minimize the size of trajectory data, while, at the same time, minimizing the impact on the trajectory analysis methods. To this extent, we evaluate five lossy compression algorithms: Douglas-Peucker (DP), Time Ratio (TR), Speed Based (SP), Time Ratio Speed Based (TR_SP) and Speed Based Time Ratio (SP_TR). The comparison is performed using four distinct real world datasets against six different dynamically assigned thresholds. The effectiveness of the compression is evaluated using classification techniques and similarity measures. The results showed that there is a trade-off between the compression rate and the achieved quality. The is no “best algorithm” for every case and the choice of the proper compression algorithm is an application-dependent process.

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