22
A. Chatzimichail et al.
in general, thus enhancing the feasibility in exploiting information in order to realize
complex event processes to a greater extent, encompassed within the domain of time.
The basic principle that needs to be abided by so as to indite the essence of
time is the time instant, an infinitesimal moment in time, based on which more
compound temporal concepts are able to be defined, such as time intervals, duration,
commencement and conclusion of events, periodicality, schedules and so on [136].
Based on those structural entities and with the adjustment and application of advanced
reasoning techniques, one can monitor the temporal flow of occurrences of events,
time-irrelevant instances alterations over time and evolution [137].
What differentiates the temporal reasoning from stream reasoning is the rapid
frequency in which novel data are acquired and/or metamorphosed and the urgent
need for live reasoning. In more detail, it requires fully-automated fault-tolerant
pipelines along with heuristic optimization techniques to be able to address to nearly
instantaneous rearrangement demands based on live triggers [138].
3.3.5 Querying—Linked Data
The last decades a great amount of data has been available through web technologies.
Most of these data are associated with geolocation information. Since geolocated data
are rapidly increasing, there comes the need to use and combine these information
to extract hidden knowledge. Semantic web technologies are responsible for such
tasks as they use reasoning techniques to combine data from heterogeneous sources,
supporting in that way more complex semantic queries. Some of the most popular
sources are DBpedia, Open Street Maps and Wikidata and their usage in semantic
reasoning systems is shown below.
In [139] the authors propose a system which utilizes Open Street Map data to
support more complex reasoning rules. The system uses an information broker to
apply rule-based reasoning and extract topological relations among entities. More
specifically, OWL is used to represent semantically the information and SQWRL
rules and vertical plane sweeping technique are used as spatial reasoners. The vertical
plane sweeping technique calculates the overlapping polygon, given two polygons.
SQWRL rules build on top of the ontology to specify some standards that extract
hidden knowledge. In this work the standards are associated with travel planning and
footways retrieval.
In [140] OSM is used to gather georeferenced information about points of interest (POIs). The collected information contain points, polylines or polygons and a
combination of both indicating the relation between them. The purpose is to detect
human activities happening in nearby locations. The methodology creates a connection between human activities and POIs or periods of time. A DL reasoning service
defines rules for grouping a number of human operations per category of point of
interest and a number of human operations in a specific period of time. The system
also predicts human activities according to the popularity of POIs using High Level
Representation of Behavioral Model (HRBModel).
A. Chatzimichail et al.
in general, thus enhancing the feasibility in exploiting information in order to realize
complex event processes to a greater extent, encompassed within the domain of time.
The basic principle that needs to be abided by so as to indite the essence of
time is the time instant, an infinitesimal moment in time, based on which more
compound temporal concepts are able to be defined, such as time intervals, duration,
commencement and conclusion of events, periodicality, schedules and so on [136].
Based on those structural entities and with the adjustment and application of advanced
reasoning techniques, one can monitor the temporal flow of occurrences of events,
time-irrelevant instances alterations over time and evolution [137].
What differentiates the temporal reasoning from stream reasoning is the rapid
frequency in which novel data are acquired and/or metamorphosed and the urgent
need for live reasoning. In more detail, it requires fully-automated fault-tolerant
pipelines along with heuristic optimization techniques to be able to address to nearly
instantaneous rearrangement demands based on live triggers [138].
3.3.5 Querying—Linked Data
The last decades a great amount of data has been available through web technologies.
Most of these data are associated with geolocation information. Since geolocated data
are rapidly increasing, there comes the need to use and combine these information
to extract hidden knowledge. Semantic web technologies are responsible for such
tasks as they use reasoning techniques to combine data from heterogeneous sources,
supporting in that way more complex semantic queries. Some of the most popular
sources are DBpedia, Open Street Maps and Wikidata and their usage in semantic
reasoning systems is shown below.
In [139] the authors propose a system which utilizes Open Street Map data to
support more complex reasoning rules. The system uses an information broker to
apply rule-based reasoning and extract topological relations among entities. More
specifically, OWL is used to represent semantically the information and SQWRL
rules and vertical plane sweeping technique are used as spatial reasoners. The vertical
plane sweeping technique calculates the overlapping polygon, given two polygons.
SQWRL rules build on top of the ontology to specify some standards that extract
hidden knowledge. In this work the standards are associated with travel planning and
footways retrieval.
In [140] OSM is used to gather georeferenced information about points of interest (POIs). The collected information contain points, polylines or polygons and a
combination of both indicating the relation between them. The purpose is to detect
human activities happening in nearby locations. The methodology creates a connection between human activities and POIs or periods of time. A DL reasoning service
defines rules for grouping a number of human operations per category of point of
interest and a number of human operations in a specific period of time. The system
also predicts human activities according to the popularity of POIs using High Level
Representation of Behavioral Model (HRBModel).