Abstract
The advance of remote sensors and positioning technologies is leading to the eruption of disparate mobility data. For a long while, location sensing devices became released. As a result, different structures of mobility data sources may reveal the details of instantaneous behaviors performed by mobile entities. Collected mobility information forms the need of behavior modeling to understanding behaviors from cognitive and analytics perspectives. Each designer may use a different formalism and representation by using either “conceptual modeling” or “ontology”. The phenomenon of adopting ontologies by organizations creates a new type of data called semantic data handled by semantic databases. The diversity of these formalisms highly increases the structural and semantic heterogeneities and consequently increases the complexity of integration tasks. In this chapter, authors propose a semantic and scalable approach that unifies formalisms and representations by the means of ontologies. This approach is supported by a case study.
TopIntroduction
The fast-paced advance of remote sensors and positioning technologies is leading to the eruption of disparate mobility data. Trajectory information may reveal the details of instantaneous behaviors performed by mobile entities associated to various applications including military (Perry, 2008), biology (Wannous et al., 2013; Sakouhi et al., 2014), bird migration (Spaccapietra et al., 2008), traffic management (Yan et al., 2014), health care (Akaichi & Manaa, 2014), etc. Collected mobility data forms the need of behavior modeling to understanding entities behaviors and activities from cognitive and analytics perspectives.
In the first generation of applications supporting trajectory information, mobility data collection concerns mainly a single location sensing device. Due to the existence of a single device, the identification of conflict trajectory information is an easy step which is realized quickly. For a long while, location sensing devices started to be released. As a result, designers may use different modeling techniques for trajectory information sources design i.e., “conceptual modeling” or “ontology” for the design of trajectory information sources. Classical conceptual modeling techniques present a conceptualization of a universe of discourse through a set of classes defined by properties. Conceptual modeling is based on spatio-temporal and moving object database structures. Besides, ontologies cover a universe of discourse through a set of concepts and properties. The phenomenon of adopting ontologies by organizations creates a new type of data called semantic data. Recent advances in DBMS technology handle ontologies and semantic data in semantic databases. Alongside, due to the autonomy of designers, various trajectory representations can be defined. A very popular, although old, representation has been investigated by the geographic information science community called “Geospatial Lifeline”, mainly based on periods of time during which entity occupies space (Thériault et al., 2002). Instead, the database community has stored and manipulated these data in Spatio-Temporal Databases (STDB) and Moving Object Databases (MOD) by the definition of “spatio-temporal data types” inter alia, moving point, moving line and moving region data types (Güting & Schneider, 2005; Xu & Güting, 2013). Until recently, ontology-building and logics attracted researches aimed at supporting trajectory based applications with new models bearing further semantic information about moving object behavior and/or activity (Yan et al., 2008; Yan & Chakraborty, 2014 ; Wannous et al., 2013). In this case, to identify conflicting trajectories, designers seem to find the “best” and “consensual” knowledge, according to the experts needs in the field through sharing a point of view. Consequently, the semantics of each used concept became unambiguous.
Key Terms in this Chapter
Trajectory: Trajectory is the record of a time-varying spatial phenomenon. Trajectory consists in the description of the movement of some moving objects at specific moment’s time. In reality, trajectory has to be built from a set of sample points which correspond to moving object positions.
Conceptual Model: A high-level description for a system. It allows us understanding and interpreting information related to a field. The later formalize information using a language in order to construct a system. The conceptual model includes a graphical representation of model concepts and represents relations between these elements.
Mobile Object: It is an identifiable geometries real word element that moves. Geometries may be points, lines, areas or volumes, changing over the time like person, car or natural phenomenon.
Ontology: Ontology is a cognitive artifact allowing the shared design and operation for knowledge. Ontology is composed from concepts related to a domain of interest linked with relations.
Scalability: Scalability is more than many user accesses at runtime; it also implies the requirement of a scalable foundation (and therefore scalable methodology) for representing ontological contents itself.
Reasoning Mechanism: Reasoning mechanisms allow deriving new facts from existing concepts and roles that are not expressed in the initial ontology.
Interoperability: It is a property of a product or system, whose interfaces are completely understood, to work with other products or systems, present or future, without any restricted access or implementation.
Meta-Model: A meta-model defines the structure that any model must be in conformity with this meta-model. A model conforms to a meta- model if all elements that constitute the model are defined by the meta- model.
Model: An abstraction of a system designed as a set of facts constructed in a particular intention. It must be used to answer questions about the system under study. In the field of trajectory data, models are used to formalize and analyze trajectories of mobile objects.