Semantic Trajectory Research Articles

Generic Semantic Trajectory Data Modelling Approach based on Ontologies

Generic Semantic Trajectory Data Modelling Approach based on Ontologies

ST-TrajGAN: A synthetic trajectory generation algorithm for privacy preservation

The rapid growth of large-scale trajectory data poses privacy risks for location-based services (LBS), primarily through centralized storage and processing of data, as well as insecure data transmission channels (such as the Internet and wireless networks), which can lead to unauthorized access or manipulation of users' location information by attackers. To enhance trajectory privacy protection while improving the trajectory utility, this paper proposes an efficient and secure deep learning model Semantic and Transformer-based Trajectory Generative Adversarial Networks (ST-TrajGAN) for trajectory data generation and publication. First, this article introduces a semantic trajectory encoding model for preprocessing trajectory points. Through this model, trajectory points can be transformed into vector representations with semantic information. Next, by learning the spatio-temporal and semantic features of real trajectory data, a deep learning model is used to generate synthetic trajectories with more uncertainty and practicality. Furthermore, a novel TrajLoss loss metric function was crafted to gauge the trajectory similarity loss within the trained deep learning model. Ultimately, the efficacy of the generated synthetic trajectories and the model's utility are assessed through Trajectory-User Linking (TUL) and Trajectory Sharing Percentage (TSP) values on three authentic Location-Based Services (LBS) datasets. Numerous experiments have shown that our method outperforms other methods in terms of privacy protection effectiveness and utility.

Trajectory privacy protection method based on sensitive semantic location replacement

As Location-Based Services (LBSs) proliferate within the Social Internet of Vehicles (SIoVs), the collection and utilization of vehicle trajectories, which are rich in spatio-temporal and semantic informations, have intensified. Publishing these trajectories without adequate privacy safeguards significantly threatens user semantic trajectory privacy. Current methodologies for protecting semantic trajectory privacy do not adequately consider the similarity in query probability between sensitive and alternative semantic locations, thus undermining the efficacy of privacy protection. To address this critical gap, we introduces a novel trajectory privacy protection method based on sensitive semantic location replacement (SSLR), which strategically replaces sensitive semantic locations. This method consists of three key steps: first, semantically annotating trajectory sampling locations to identify sensitive semantic points; second, employing a unique double semicircular area to define replacement regions and selecting replacement Points of Interests (PoIs) that align in semantic attributes and query probabilities; third, reconstructing and publishing the modified trajectories. Our simulation results confirm that SSLR advances the state of the art by delivering superior performance in average recognition rate, geographic similarity, and semantic similarity compared to existing methods. Crucially, by incorporating considerations of query probability similarities, this paper not only enhances the effect of trajectory privacy protection but also preserves its utility. This dual enhancement represents a groundbreaking approach to trajectory privacy, with significant implications for advancing privacy strategies in LBSs within SIoVs.

Indoor mobility data encoding with TSTM-in: A topological-semantic trajectory model

The growing ubiquity of location/activity sensing technologies has created unprecedented opportunities for research on human spatiotemporal interaction behavior in mobile environments. However, existing studies of human mobility need to sufficiently account for the association of indoor scenes with the semantics of human behavior. This paper introduces TSTM-in, a trajectory model that combines trajectory data and indoor scenes using topological semantic modeling, semantic trajectory reconstruction, and trajectory queries. The model effectively manages indoor semantic trajectory data and extracts topological behavioral semantics by incorporating important points across a trajectory to reflect the semantics of key points connected to indoor corridors and regions. These topological semantics facilitate the creation of a flexible intersection-based indoor semantic trajectory reconstruction. Reconstructed semantic trajectories represent human mobility by integrating semantic data sets along the time axis. A case study with real-world trajectory queries from travelers demonstrates the model's effectiveness. TSTM-in realizes the association of indoor scenes with human behavior semantics, supporting the construction of mobile object management applications for indoor scenes and providing scientific and reasonable spatiotemporal semantic information description for location service-based intelligent cities.

CHEKG: a collaborative and hybrid methodology for engineering modular and fair domain-specific knowledge graphs

Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios.

Short-term trajectory prediction for individual metro passengers based on multi-level periodicity mining from semantic trajectory

Short-term trajectory prediction for individual metro passengers is an essential component of many location-based applications, such as bottleneck identification, anomalous trajectory detection, and emergency evacuation. Accurate and interpretable trajectory prediction in short-term remains challenging since the trajectory of individual metro passengers is influenced by time-varying and periodic mobility patterns. Moreover, it is still difficult for deep learning-based prediction approaches to intuitively explain the individual periodic mobility patterns captured by the black-box model. In this study, a long short-term memory method based on multi-level periodicity mining (LSTM-MP) from semantic trajectory is developed to achieve accurate and interpretable short-term trajectory prediction for individual metro passengers. The newly defined semantic trajectories of metro passengers that combine the semantic information about the geometric and operational characteristics of the metro with the raw trajectory data are constructed to help obtain fine-grained periodic mobility patterns of individual passengers. Multi-level periodicity which includes long-range, weekly-interval, and daily-interval periodicities, is calculated with variable time granularity and its polymerization from the constructed semantic trajectories. Finally, the characteristics of multi-level periodicity are considered by developing a principal trajectory attention mechanism to select the most relevant trajectories for prediction in the LSTM-MP framework. The prediction performance of LSTM-MP is compared with 2-order Markov chain and Naïve-LSTM algorithms using the large real-world anonymous passenger WiFi probe dataset. Experimental results show that LSTM-MP can predict when and where an individual will be in the future time series with an average accuracy of 82.3%. This demonstrates the effectiveness of LSTM-MP in the short-term trajectory prediction for individual metro passengers. Potential applications of the short-term trajectory prediction for individual metro passengers are also identified.

Clustering-based multidimensional sequential pattern mining of semantic trajectories

Clustering-based multidimensional sequential pattern mining of semantic trajectories

Clustering-based Multidimensional Sequential Pattern Mining of Semantic Trajectories

Clustering-based Multidimensional Sequential Pattern Mining of Semantic Trajectories

Semantic Trajectory Planning for Industrial Robotics

The implementation of industrial robots across various sectors has ushered in unparalleled advancements in efficiency, productivity, and safety. This paper explores the domain of semantic trajectory planning in the area of industrial robotics. By adeptly merging physical constraints and semantic knowledge of environments, the proposed methodology enables robots to navigate complex surroundings with utmost precision and efficiency. In a landscape marked by dynamic challenges, the research positions semantic trajectory planning as a linchpin in fostering adaptability. It ensures robots interact safely with their surroundings, providing vital object detection and recognition capabilities. The proposed ResNet model exhibits remarkable classification performance, bolstering overall productivity. The study underscores the significance of this approach in addressing real-world industrial applications while emphasizing accuracy, precision, and enhanced productivity.

Inequalities in experiencing urban functions. An exploration of human digital (geo-)footprints

Studies on mobility inequalities have so far mostly relied on Survey data or Censuses. While such studies have demonstrated that inequalities strongly influence everyday mobility choices, these data sources lack granular information on people’s movements on a daily basis. By capitalising on high spatio-temporal resolution data provided by Spectus.ai, this study aims at investigating how the deprivation level of the area where people live influences the kinds of urban environment they are more likely to use for their everyday activities. To do this, raw GPS trajectories collected in 2019 in Great Britain (GB) are transformed into semantic trajectories where short-time changes and the functional nature of urban contexts are acknowledged as two key dimensions to understand human spatial behaviours. Hourly sequences of stops are extracted from GPS trajectories and enriched with contextual information based on a new area-based classification detecting urban functions. The data exploration shows that some human patterns are widely common across all levels of deprivation, such as the tendency to be mostly exposed to the urban context near the home location. At the same time, we show that differences exist, especially between those who live in the most deprived areas and those who live in the least deprived areas of GB. It appears that people living in the most deprived areas tend to have a less regular working pattern and be more exposed to urban-based functions and well-served areas, while those living in the least deprived areas have a more regular working patterns and are mostly exposed to the countryside and low-density areas. Our approach and results provide new insights on the temporal and contextual dimensions of mobility inequalities, informing on who is exposed to issues characterising certain urban environments.

Find Another me Across the World - Large-Scale Semantic Trajectory Analysis Using Spark

In today's society, location-based services are widely used which collect a huge amount of human trajectories. Analyzing semantic meanings of these trajectories can benefit numerous real-world applications, such as product advertisement, friend recommendation, and social behavior analysis. However, existing works on semantic trajectories are mostly centralized approaches that are not able to keep up with the rapidly growing trajectory collections. In this paper, we propose a novel large-scale semantic trajectory analysis algorithm in Apache Spark. We design a new hash function along with efficient distributed algorithms that can quickly compute semantic trajectory similarities and identify communities of people with similar behavior across the world. The experimental results show that our approach is more than 30 times faster than centralized approaches without sacrificing any accuracy like other parallel approaches.

STKST-I: An Efficient Semantic Trajectory Search by Temporal and Semantic Keywords

STKST-I: An Efficient Semantic Trajectory Search by Temporal and Semantic Keywords

A distributed framework for large-scale semantic trajectory similarity join

A distributed framework for large-scale semantic trajectory similarity join

MA-STS-Based Social Intimacy Analysis Algorithm Using Real Campus Network Data

In recent years, the widespread availability of Wi-Fi in various settings, including universities, enterprises, and large shopping centers, has become increasingly prevalent. The user’s time and location information embedded in wireless network systems can reveal individual and group social relationships, which indirectly reflect each person’s psychological well-being. However, due to challenges in obtaining complete data, the high complexity of related data, and the absence of suitable data analysis models, few studies have analyzed student social behavior using data from university campus networks. This paper employs real-world data from a renowned Chinese university’s wireless campus network for in-depth analysis and introduces a novel multiangle semantic trajectory similarity (MA-STS) algorithm to infer the intimacy and relationship types (such as teacher-student, friends, classmates, or romantic partners) between users. The experiments demonstrate that the proposed algorithm achieves an accuracy of over 95%.

Image embedding for denoising generative models

Denoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent generative quality, and the solid probabilistic foundation. In this article, we address the problem of embedding an image into the latent space of Denoising Diffusion Models, that is finding a suitable “noisy” image whose denoising results in the original image. We particularly focus on Denoising Diffusion Implicit Models due to the deterministic nature of their reverse diffusion process. As a side result of our investigation, we gain a deeper insight into the structure of the latent space of diffusion models, opening interesting perspectives on its exploration, the definition of semantic trajectories, and the manipulation/conditioning of encodings for editing purposes. A particularly interesting property highlighted by our research, which is also characteristic of this class of generative models, is the independence of the latent representation from the networks implementing the reverse diffusion process. In other words, a common seed passed to different networks (each trained on the same dataset), eventually results in identical images.

A KG-Based Integrated UAV Approach for Engineering Semantic Trajectories in the Cultural Heritage Documentation Domain

Data recordings of the movement of vehicles can be enriched with heterogeneous and multimodal data beyond latitude, longitude, and timestamp and enhanced with complementary segmentations, constituting a semantic trajectory. Semantic Web (SW) technologies have been extensively used for the semantic integration of heterogeneous and multimodal movement-related data, and for the effective modeling of semantic trajectories, in several domains. In this paper, we present an integrated solution for the engineering of cultural heritage semantic trajectories generated from unmanned aerial vehicles (UAVs) and represented as knowledge graphs (KGs). Particularly, this work is motivated by, and evaluated based on, the application domain of UAV missions for documenting regions/points of cultural heritage interest. In this context, this research work extends our previous work on UAV semantic trajectories, contributing (a) an updated methodology for the engineering of semantic trajectories as KGs (STaKG), (b) an implemented toolset for the management of KG-based semantic trajectories, (c) a refined ontology for the representation of knowledge related to UAV semantic trajectories and to cultural heritage documentation, and (d) the application and evaluation of the proposed methodology, the developed toolset, and the ontology within the domain of UAV-based cultural heritage documentation. The evaluation of the integrated UAV solution was achieved by exploiting real datasets collected during three UAV missions to document sites of cultural interest in Lesvos, Greece, i.e., the UNESCO-protected petrified forest of Lesvos Petrified Forest/Geopark, the village of Vrissa, and University Hill.

ReconTraj4Drones: A Framework for the Reconstruction and Semantic Modeling of UAVs’ Trajectories on MovingPandas

Unmanned aerial vehicles (UAVs), also known as drones, are important for several application domains, such as the military, agriculture, cultural heritage documentation, surveillance, and the delivery of goods/products/services. A drone’s trajectory can be enriched with external and heterogeneous data beyond latitude, longitude, and timestamp to create its semantic trajectory, providing meaningful and contextual information on its movement data, enabling decision makers to acquire meaningful and enriched contextual information about the current situation in the field of its operation and eventually supporting simulations and predictions of high-level critical events. In this paper, we present an ontology-based, tool-supported framework for the reconstruction, modeling, and enrichment of drones’ semantic trajectories. This framework extends MovingPandas, a widely used and open-source trajectory analytics and visualization tool. The presented research extends our preliminary work on drones’ semantic trajectories by contributing (a) an updated methodology for the reconstruction of drones’ trajectories from geo-tagged photos taken by drones during their flights in cases in which flight plans and/or real-time movement data have been lost or corrupted; (b) an enrichment of the reconstructed trajectories with external data; (c) the semantic annotation of the enriched trajectories based on a related ontology; and (d) the use of SPARQL queries to analyze and retrieve knowledge related to the flight of a drone and the field of operations (context). An evaluation of the presented framework, namely, ReconTraj4Drones, was conducted against several criteria, using real and open datasets.

The elusive meanings of translation: J.de Nerval’s sonnet “El Desdichado” from the “Chimeras” cycle

The study of the inversion of meaning, which inevitably occurs during translation, has become one of the priorities of both domestic and world comparative studies in recent decades. This problem formed the basis of the theory of cultural transfer, which began to be developed in the mid-1980s among French historians-Germanists. The methodology of cultural transfer, which has taught us to see the phenomenon rather positive in those transformations of meaning that occur during direct translation from one language to another, has retouched the question of the “archaeological” side of this phenomenon, which has long received the designation “reverse translation” in science. The article is devoted to the analysis of the first sonnet in Nerval’s poetic cycle “Chimeras”. A detailed analysis of the intertextual allusions concealed by the extremely hermetic sonnet “El Desdichado” is accompanied by a parallel analysis of two translations of this sonnet into Russian, made respectively by N.Ya. Rykova and Y.M.Denisov. Despite the high quality of translation, the method of “slow reading” of the sonnet shows how in the translation, even if seemingly close to the original, the semantic trajectories of the original and translated texts diverge. Such a comparative analysis allows us to reactualise Alexander Mikhailov’s 1990s idea of reverse translation, which turns out to be absolutely necessary when reading and perceiving especially translated poetry.

Towards Optimal Planning for Green, Smart, and Semantically Enriched Cultural Tours

This concept paper presents our viewpoint regarding the exploitation of cutting-edge technologies for the delivery of smart tourism cultural tours. Specifically, the paper reports preliminary work on the design of a novel smart tourism solution tailored to a multiobjective optimization system based on factors such as the preferences and constraints of the tourist/visitor, the city’s accessibility and traffic, the weather conditions, and others. By optimizing cultural tours and delivering comfortable, easy-to-follow, green, acceptable visiting experiences, the proposed solution, namely, OptiTours, aims to become a leading actor in tourism industry transformation. Moreover, specific actions, applications, and methodologies target increasing touring acceptance while advancing the overall (smart) city impression. OptiTours aims to deliver a novel system to attract visitors and guide them to enjoy a city’s possible points of interest, achieving high visitor acceptance. Advanced technologies in semantic trajectories’ management and optimization in route planning will be exploited towards the discovery of optimal, smart, green, and comfortable routes/tours. A novel multiscale and multifactor optimization system aims to deliver not only optimal personalized routes but also alternative routes, ranked based on visitors’ preferences and constraints. In this concept paper, we contribute a detailed description of the OptiTours approach for ICT-based smart tourism, and a high-level architectural design of the solution that is planned to be implemented in the near future.

A novel approach for handling semantic trajectories on data warehouses

A trajectory is a set of traces left by a moving object. It contains spatio-temporal information about where and when that object was, as well as other semantical relevant information. It is described by a continuation of movement. Data concerning moving objects and their trajectories can be stored in a Trajectory Data Warehouses for organization, managing, and analysis purposes. This work is dedicated to semantic trajectory data warehouses. A logical schema is proposed, called S-TrODW, where an object relational framework is used. The main novelty of the S-TrODW model is the integration of trajectories and their segments in the fact table by means of a nested relation. An algorithm is presented for transforming the flat star schema (with non-nested trajectory segments) to the S-TrODW schema. The proposal is validated through a case study dealing with freight transportation. A more natural modelling and queries formulation, as well as the improvement of query execution time are among the contributions of this paper compared to other works.