Table 1 - uploaded by Benjamin Klotz
Content may be subject to copyright.
Comparison of the different initiatives in regard to the hypothesis: we can enable semantic trajectory enrichments with signal values for generic applications
Source publication
In this paper, we use semantic technologies for enriching trajectory data in the automotive industry for offline analysis. We proposed to re-use a combination of existing ontologies and we designed a Vehicle Signal Specification ontology to provide an environment in which we developed an application that analyzes the variations of signal values and...
Context in source publication
Context 1
... hypothesis being that we can enable semantic trajectory enrich- ments with signal values for generic applications, there are many initiatives that solve partially the problem, but not completely as visible in Table 1. The representations of static car data focus on the e-commerce domain and do not describe dynamic data. ...
Similar publications
Les expressions idiomatiques représentent une partie intégrante de la communication quotidienne. Cependant, lorsqu’il s’agit de leur présence dans l’enseignement du FLE, elles sont en quelque sorte négligées. On s’interroge sur la cause de cette négligence et ce qu’il faudrait faire pour surmonter ce défaut. Dans cet article, nous nous proposons d’...
Citations
... In [10][11], authors presented vehicle signal specification ontology (VSSO).To this end, they used the concepts of Sensor, Observation, Sample, and Actuator (SOSA) and SSN ontology in order to represent observation of car signals. Comparison results of integration of VSSO, SOSA/SSN and STEP to other ontologies demonstrates it effectiveness in term sensor coverage, semantics, trajectory enrichments metrics. ...
Handling interoperability of data exchange among road traffic sensor devices, connected vehicles, infrastructure components and heterogeneous traffic management center applications has become an important and basic requirement nowadays. To meet this requirement, this paper proposes an ontology based framework to capture the knowledge domain about traffic automatic incident detection system (AIDS) based on Connected Vehicles (CVs) technology. This ontology addresses the semantic data interoperability needed between different heterogeneous entities constituent this AIDS. This contribution aims at modeling and capturing the semantic of the anomaly information used in the incident detection process and describing the AIDS components, their observations, measurements and communications messages features. First, to achieve this goal, NeOn methodology was adopted. Then, we defined the basic concepts and observations of a traffic sensor and CVs that has been extended to define concepts related to the data sensing and gathering layer of this framework based on ontology concepts. In addition, to ensure data interoperability and identify ontology’s restrictions, we used the OWL (Web Ontology Language) language. Furthermore, to build this ontology, we used the OWL under Protégé tool. Finally, OAIDS consisted of 93 concepts and 33 object properties. OntoMetrics was used to confirm the effectiveness of this proposed ontology to carry out the interoperability of CV’s sensor data in the urban road AIDS domain.
... In [96], the authors propose to use contextual signals provided by Location Based Social Networks (LBSNs) such as time or location for events recommendation. In [78], the authors propose to compute an interest score for places and events in case of an in-car use, based on user preferences (given explicitly by the user) and weather conditions (contextual information). In [161], the authors use a neural network to learn user preferences, then used a context graph in order to regularize the obtained user preferences embedding. ...
The overall objective of this PhD is to explore and propose new approaches leveraging a large volume of heterogeneous data that needs to be integrated and semantically enriched, and recent advances in machine and deep learning techniques, in order to exploit both the increased variety of offers that an airline can make to its customers as well as the knowledge it has of its customers with the ultimate goal of optimizing conversion and purchase. The overall goal of this thesis can be broken down into three main research questions: 1) What piece of content (ancillary services, third-party content) should be recommended and personalized to each traveler? 2) When should a recommendation be made and for which communication channel to optimize conversion? 3) How do we group ancillary services and third-party content and can we learn what often goes together based on purchase logs?
... SIGNAL ONTOLOGY'' [117], [118] Based on VSS (see Section V-C5) and further ontologies, 12 Klotz et al. create an extensive ontology for vehicle signals 11 https://www.pegasusprojekt.de/de/ 12 e.g. ...
Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user’s life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these ever-increasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future.
... Klotz et. al. [9] show how vehicles' trajectories can be semantically described using such ontologies. However, it uses simple arbitrary rules to extract statuses that are not generically applicable to other vehicle data uses. ...
Vehicle architectures have evolved over the past two decades to provide support for data-driven functionalities. The typical approach in this domain has been application-centric, leading to data models that are disparate, repetitive, and hardly maintainable in the long run. As a result, the software complexity increases, while the knowledge remains hidden in the applications' code. We argue that it is essential to enrich the data with standard semantic models to enable a smooth integration of heterogeneous data. In this paper, we propose an ontology-based approach to integrate vehicle-related data. It consists of semantically annotating application-specific data with a well-defined semantic model that considers its streaming-nature. Three applications that use vehicle data are implemented and annotated with the presented procedure. The resulting semantic data is validated with elaborated analytical competency questions that combine application-specific data. Such questions are satisfied with the implementation of queries that follow the patterns of the semantic model. Our work shows that ontology-based data integration is a suitable component for vehicle architectures. The use of this type of integration implies the one-time implementation of queries that are stable over time, reusability of application-specific data, and increased semantics.
... Tandis que les informations issues d'autres capteurs se focalisent sur l'état du véhicule et ses capacités à fournir un service (niveau de carburant, taux de remplissage d'une benne pour un camion à ordure, etc.). De même, la combinaison des coordonnées GPS et des données issues des capteurs des véhicules permet la recherche de comportements de conduite : éco-conduite (Klotz et al., 2018), etc. Cependant, l'envoi par les véhicules de grandes quantités de données chaque seconde entraîne des problématiques classiques liées au big data. Le flux de données arrivant en continu produit une masse de données hétérogènes considérable à traiter. ...
... There are a number of open topics in this field that need to be investigated further such as high performance trajectory classification methods [12,9,78,74,43], accurate trajectory segmentation methods [81,75,75,64,29], trajectory compression and reduction [67,25], privacy in trajectory mining [8,26,19], trajectory similarity and clustering [35,17,52,37], dealing with trajectory uncertainty [32,29], and semantic trajectories [55,39,50], and active learning strategies for trajectory data [33]. These topics are highly correlated and solving one of them requires to some extent exploring the other questions. ...
Predicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism, traffic congestion, smart cities management, animal behaviour analysis, environmental preservation, and traffic dynamics are some of the trajectory mining applications. Transportation modes prediction as one of the tasks in human mobility and vehicle mobility applications plays an important role in resource allocation, traffic management systems, tourism planning and accident detection. In this work, the proposed framework in Etemad et al. is extended to consider other aspects in the task of transportation modes prediction. Wrapper search and information retrieval methods were investigated to find the best subset of trajectory features. Finding the best classifier and the best feature subset, the framework is compared against two related papers that applied deep learning methods. The results show that our framework achieved better performance. Moreover, the ground truth noise removal improved accuracy of transportation modes prediction task; however, the assumption of having access to test set labels in pre-processing task is invalid. Furthermore, the cross validation approaches were investigated and the performance results show that the random cross validation method provides optimistic results.
... For instance in [5] we focus on two main use cases: generate segments of trajectory annotated according to the evolution of a given signal value, and a "smooth" driving percentage label attached to a trajectory when longitudinal and angular acceleration are bound. This is made possible by combining a Vehicle Signal ontology with SOSA/SSN [4] for Observation patterns and STEP [6] for Semantic Trajectory annotation patterns. ...
... Use with cache and WoT Events: Another potential improvement for our prototype would consist in including complex event properties, using records of timed events. A use case could be to do signal analysis on a sliding window like in [5]. This would enable more complex applications based on WoT interactions and ultimately, WoT-based enrichment of car data. ...
... For instance in [5] we focus on two main use cases: generate segments of trajectory annotated according to the evolution of a given signal value, and a "smooth" driving percentage label attached to a trajectory when longitudinal and angular acceleration are bound. This is made possible by combining a Vehicle Signal ontology with SOSA/SSN [4] for Observation patterns and STEP [6] for Semantic Trajectory annotation patterns. ...
... Use with cache and WoT Events: Another potential improvement for our prototype would consist in including complex event properties, using records of timed events. A use case could be to do signal analysis on a sliding window like in [5]. This would enable more complex applications based on WoT interactions and ultimately, WoT-based enrichment of car data. ...
Car signal data is usually hard to access, understand and integrate for non automotive domain experts. In this paper, we use semantic technologies for enriching signal data in the automotive industry and access it through Web of Things interactions. This combination allows the access and integration of car data from the web. We built VSSo, a Vehicle Signal ontology based on SOSA/SSN Observations and Actuations, and generated WoT Actions, Events and Properties, enriched with domain metadata. We mapped VSSo to a Web of Things ontology and we developed a Web of Things protocol binding with LwM2M, and made an implementation in a real car. This implementation resulted in a first working prototype, and a number of future improvements required in order to be compliant with automotive standards.
... In addition to that, many complex algorithms used by different services can be housed in the cloud. For example, semantic reasoning on vehicular data for trajectory mining [17], or tourism recommendation system [18], can be deployed as a service. We introduce the concept of microservices to extend the Cloud capabilities by adding new application logic as a microservice. ...
