Jamal Malki

Modelling Mobile Object Activities Based on Trajectory Ontology Rules Considering Spatial Relationship Rules

Several applications use devices and capture systems to record trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Temporal, spatial and domain related information are fundamental sources used to upgrade trajectories. The objective of semantic trajectories is to help users validating and acquiring more knowledge about mobile objects. In particular, temporal and spatial analysis of semantic trajectories is very important to understand the mobile object behaviour. This article proposes an ontology based modelling approach for semantic trajectories. This approach considers different and independent sources of knowledge represented by domain and spatial ontologies. The domain ontology represents mobile object activities as a set of rules. The spatial ontology represents spatial relationships as a set of rules. To achieve this approach, we need an integration between trajectory and spatial ontologies.

Time Integration in Semantic Trajectories Using an Ontological Modelling Approach

Nowadays, with a growing use of location-aware, wirelessly connected, mobile devices, we can easily capture trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Several research fields are currently focusing on semantic trajectories to support queries and inferences to help users for validating and discovering more knowledge about mobile objects. The inference mechanism is needed for queries on semantic trajectories connected to other sources of information. Time and space knowledge are fundamental sources of information used by the inference operation on semantic trajectories. This article presents a case study of inference mechanism on semantic trajectories. We propose a solution based on an ontological approach for modelling semantic trajectories integrating time information and rules. We give experiments and evaluations of the proposed approach on generated and real data.

Inference on Semantic Trajectory Data Warehouse Using an Ontological Approach

Using location aware devices is getting more and more spread, generating then a huge quantity of mobility data. The latter describes the movement of mobile objects and is called as well Trajectory data. In fact, these raw trajectories lack contextual information about the moving object goals and his activity during the travel. Therefore, the former must be enhanced with semantic information to be called then Semantic Trajectory. The semantic models proposed in the literature are in many cases ontology-based, and are composed of thematic, temporal and spatial ontologies and rules to support inference and reasoning tasks on data. Thus, calculating inference on moving objects trajectories considering all thematic, spatial, and temporal rules can be very long depending on the amount of data involved in this process. On the other side, TDW is an efficient tool for analyzing and extracting valuable information from raw mobility data. For that we propose throughout this work a TDW design, inspired from an ontology model. We will emphasis the trajectory to be seen as a first class semantic concept. Then we apply the inference on the proposed model to see if we can enhance it and make the complexity of this mechanism manageable.

Trajectory ontology inference considering domain and temporal dimensions—Application to marine mammals

Abstract Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. To accomplish reasoning over trajectories, the ontology must consider mobile object, domain and other knowledge. In our approach, we suggest expressing this knowledge in the form of rules. To annotate data with these rules, we need an inference mechanism over trajectory ontology. Experiments over our model using domain and temporal rules address an inference computation complexity. This complexity has two important factors: time computations and space storage. In order to reduce the inference complexity, we proposed optimizations, such as domain constraints and temporal neighbor refinements. In this paper, we define a refinement specifically for the application domain. Then, we evaluate our contribution over real trajectory data. Finally, the results show the positive impact of the last refinement on reducing the complexity of the inference mechanism. This refinement reduces half of the time computation and then allows considering larger data sets.

Analysis of Trajectory Ontology Inference Complexity over Domain and Temporal Rules

Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. This ontology contains temporal concepts, so we map it to a temporal ontology. We present an implementation framework for declarative and imperative parts of ontology rules in a semantic data store. An inference mechanism is computed over these semantic data. The computational time and memory of the inference increases very rapidly as a function of the data size. For this reason, we propose a two-tier inference filters on data. The primary filter analyzes the trajectory data considering all the possible domain constraints. The analyzed data are considered as the first knowledge base. The secondary filter then computes the inference over the filtered trajectory data and yields to the final knowledge base, that the user can query.

An Ontology-Based Approach for Handling Explicit and Implicit Knowledge over Trajectories

The current information systems manage several, different and huge databases. The data can be temporal, spatial and other application domains with specific knowledge. For these reasons, new approaches must be designed to fully exploit data expressiveness and heterogeneity taking into account application’s needs. As part of ontology-based information system design, this paper proposes an ontology modeling approach for trajectories of moving objects. Consider domain, temporal and spatial knowledge gives a complexity to our system. We propose optimizations to annotate data with these knowledge.