460
Y. Sheng et al.
group of route points related to one flight process. Route points can be further
generalized as a structural property of a trajectory.
Relationships demonstrate how concepts interact with others. For example,
an aircraft will depart from or arrive at an airport, and the airport is its origin or
destination. This airport may provide local weather forecasts and thunderstorm
data to support the aircraft to fly in certain airspace. In this paper, we mainly
focus on the relationship between the flights and weather.
2.2 Ontologies for Decision-Making in Air Traffic Management
The National Aeronautics and Space Administration (NASA) proposes an
ontology-based data integration system that shows the feasibility of integrating, querying, and searching over different sources of heterogeneous ATM data.
These data generate from FAA, related enterprises, NASA, etc. In this system,
a shared ontology enables stakeholders to bridge the gap of various types of data
models in aviation and query date across data sets [11,12].
Traffic Management Initiatives (TMIs) also leverage an ontology to reduce
the inconsistent between systems. An ontology is designed as a module of the
Traffic Advisory Systems (TAS). TAS manages to reduce traffic imbalances in the
National Airspace System (NAS) [13]. The TMI ontology defines a set of shared
concepts and relationships, which promote the interoperations and support the
TAS to reach its goal.
FAA uses an ontology as a controlled vocabulary for documents. Thus, the
terminology used in FAA has a standard, and agencies in FAA search documents
with less effort make decision more timely. This ontology is also used to tag
semistructured text documents [14].
The above-mentioned papers notice the effectiveness and importance of ontology in ATM, but do not build ontology with spatio-temporal information, neither
set-related reasoning rules.
3 Spatio-Temporal Ontology Model
3.1 Temporal Ontology
James Allen and George Ferguson proposed temporal logic in their paper
“Actions and Events in Interval Temporal Logic” in July 1994. They hope to
present a way of representing temporal information on events and actions based
on logic. Compared to previous AI research, interval temporal logic is more
expressive and can be easily understood.
Time can be expressed by intervals and instant points. Intervals are time
span between two instant points. Usually, intervals have positive time length,
time interval with zero length can also be considered as an interval because it
has two instants overlapping with each other. Instants are independent points
on the timeline that have zero time length.
There are 11 types of temporal relations between two intervals, as shown in
Fig. 1. They express the temporal relations between 4D trajectories and thunderstorms.
Y. Sheng et al.
group of route points related to one flight process. Route points can be further
generalized as a structural property of a trajectory.
Relationships demonstrate how concepts interact with others. For example,
an aircraft will depart from or arrive at an airport, and the airport is its origin or
destination. This airport may provide local weather forecasts and thunderstorm
data to support the aircraft to fly in certain airspace. In this paper, we mainly
focus on the relationship between the flights and weather.
2.2 Ontologies for Decision-Making in Air Traffic Management
The National Aeronautics and Space Administration (NASA) proposes an
ontology-based data integration system that shows the feasibility of integrating, querying, and searching over different sources of heterogeneous ATM data.
These data generate from FAA, related enterprises, NASA, etc. In this system,
a shared ontology enables stakeholders to bridge the gap of various types of data
models in aviation and query date across data sets [11,12].
Traffic Management Initiatives (TMIs) also leverage an ontology to reduce
the inconsistent between systems. An ontology is designed as a module of the
Traffic Advisory Systems (TAS). TAS manages to reduce traffic imbalances in the
National Airspace System (NAS) [13]. The TMI ontology defines a set of shared
concepts and relationships, which promote the interoperations and support the
TAS to reach its goal.
FAA uses an ontology as a controlled vocabulary for documents. Thus, the
terminology used in FAA has a standard, and agencies in FAA search documents
with less effort make decision more timely. This ontology is also used to tag
semistructured text documents [14].
The above-mentioned papers notice the effectiveness and importance of ontology in ATM, but do not build ontology with spatio-temporal information, neither
set-related reasoning rules.
3 Spatio-Temporal Ontology Model
3.1 Temporal Ontology
James Allen and George Ferguson proposed temporal logic in their paper
“Actions and Events in Interval Temporal Logic” in July 1994. They hope to
present a way of representing temporal information on events and actions based
on logic. Compared to previous AI research, interval temporal logic is more
expressive and can be easily understood.
Time can be expressed by intervals and instant points. Intervals are time
span between two instant points. Usually, intervals have positive time length,
time interval with zero length can also be considered as an interval because it
has two instants overlapping with each other. Instants are independent points
on the timeline that have zero time length.
There are 11 types of temporal relations between two intervals, as shown in
Fig. 1. They express the temporal relations between 4D trajectories and thunderstorms.