Athena

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Roberto Trasarti

Athena: Trajectories and city places Athena the greek goddess of wisdom Hotel University Monuments Show kinds of points of interest and landmarks

And more … select kinds of trajectories according to the application domain TouristTrajectory ≡ Trajectory ⊓ ∃hasStop.∃isLocatedIn.TouristPlace ⊓∃hasStop.∃isLocatedIn.AccomodationPlace SELECT trajectory FROM ‘?trajectory rdf:type :TouristTrajectory’; Tourist Trajectories

The idea (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The idea (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Our Contribution so far … ,[object Object],[object Object],[object Object]

The trajectory ontology Geography Ontology (GO) Traffic Domain Ontology (ADO) Geometric Trajectory Ontology (GTO) StreetG Time Instant SimpleTime Geo Line SimpleGeo B.E.S Move Point Interval Person Trajectory Surface hasGeometry hasGeometry hasGeometry hasTime islocatedIn hasTrajectory from is-a is-a is-a is-a is-a is-a Crossing between follows RoadWork islocatedIn StreetT sameAs hasMove Stop Begin End to hasStop hasEnd hasBegin is-a isLocatedIn is-a is-a is-a PointOfInterest Museum Hotel is-a is-a hasHome hasWork locatedIn LongTerm RoadWork GasStation Car hasCar LongTime Interval is-a is-a is-a

All Data are inside DB !! Geometric Trajectory Geographic Knowledge Domain Knowledge HERMES Mobility Pattern Mobility Data Raw Data

Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst

Taxonomies and Axioms Time CityPlace Bridge Church … TouristTrajectory ≡ Trajectory ⊓ ∃hasStop.∃isLocatedIn.TouristPlace ⊓∃hasStop.∃isLocatedIn.AccomodationPlace Morning Geometric Trajectory Ontology (GTO) Time Instant SimpleTime Geo Line SimpleGeo B.E.S Move Point Interval Trajectory Surface hasGeometry hasGeometry hasTime from is-a is-a is-a is-a is-a is-a follows hasMove Stop Begin End to hasStop hasEnd hasBegin is-a isLocatedIn is-a is-a is-a LongTime Interval is-a Afternoon Evening Monument Museum

Mapping Ontology to DB ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Semantic Trajectory HasTrajectory hasComponents BEStop 0:N list 1:1 2:N list 1:1 IsIn 0:1 0:N Move ƒ(T) To 0:1 1:1 1:1 0:1 Its personalization --> IsIn 0:1 0:N The hooks TravelingOT Trajectory SpatialOT1 Bird name birth year location Does Migration year North/South StopsIn Country 0:N list 1:1 2:N list 0:N From SpatialOT2

From Tables to Triples ,[object Object],[object Object],[object Object]

Reasoning Services ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],TouristTrajectory ≡ Trajectory ⊓ ∃hasStop.∃isLocatedIn.TouristPlace ⊓∃hasStop.∃isLocatedIn.AccomodationPlace

Querying only the ontology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Querying the ontology + original data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Ongoing work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a privacy model deﬁning the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of non-sensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajectory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.

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Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.

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1. Enriching trajectory and trajectory pattern semantics with background knowledge Chiara Renso, Roberto Trasarti KDDLab, ISTI, CNR, Italy Stefano Spaccapietra, Christine Parent, Jose Macedo , Zhixian Yan EPFL, Lausanne, Switzerland Miriam Baglioni Pisa University, Italy Monica Wachowicz Technical University of Madrid, Spain

2. Athena: Trajectories and city places Athena the greek goddess of wisdom Hotel University Monuments Show kinds of points of interest and landmarks

3. And more … select kinds of trajectories according to the application domain TouristTrajectory ≡ Trajectory ⊓ ∃hasStop.∃isLocatedIn.TouristPlace ⊓∃hasStop.∃isLocatedIn.AccomodationPlace SELECT trajectory FROM ‘?trajectory rdf:type :TouristTrajectory’; Tourist Trajectories

4. GeoPKDD Tasks

8. Organizing the Knowledge …

9. The trajectory ontology Geography Ontology (GO) Traffic Domain Ontology (ADO) Geometric Trajectory Ontology (GTO) StreetG Time Instant SimpleTime Geo Line SimpleGeo B.E.S Move Point Interval Person Trajectory Surface hasGeometry hasGeometry hasGeometry hasTime islocatedIn hasTrajectory from is-a is-a is-a is-a is-a is-a Crossing between follows RoadWork islocatedIn StreetT sameAs hasMove Stop Begin End to hasStop hasEnd hasBegin is-a isLocatedIn is-a is-a is-a PointOfInterest Museum Hotel is-a is-a hasHome hasWork locatedIn LongTerm RoadWork GasStation Car hasCar LongTime Interval is-a is-a is-a

10. All Data are inside DB !! Geometric Trajectory Geographic Knowledge Domain Knowledge HERMES Mobility Pattern Mobility Data Raw Data

11. Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst

12. Taxonomies and Axioms Time CityPlace Bridge Church … TouristTrajectory ≡ Trajectory ⊓ ∃hasStop.∃isLocatedIn.TouristPlace ⊓∃hasStop.∃isLocatedIn.AccomodationPlace Morning Geometric Trajectory Ontology (GTO) Time Instant SimpleTime Geo Line SimpleGeo B.E.S Move Point Interval Trajectory Surface hasGeometry hasGeometry hasTime from is-a is-a is-a is-a is-a is-a follows hasMove Stop Begin End to hasStop hasEnd hasBegin is-a isLocatedIn is-a is-a is-a LongTime Interval is-a Afternoon Evening Monument Museum

13. Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst

14.

15. Semantic Trajectory HasTrajectory hasComponents BEStop 0:N list 1:1 2:N list 1:1 IsIn 0:1 0:N Move ƒ(T) To 0:1 1:1 1:1 0:1 Its personalization --> IsIn 0:1 0:N The hooks TravelingOT Trajectory SpatialOT1 Bird name birth year location Does Migration year North/South StopsIn Country 0:N list 1:1 2:N list 0:N From SpatialOT2

16. Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst

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19. Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst

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Notes de l'éditeur

Advantages: Oracle is the DMBS chosen in Geopkdd for storing sptaio-temporal data. It also has been chosen for data warehouse. We aim at experimenting the reasoning capabilities. Disadvantages: The tools is not very friendly to use, the OWL reasoning engine is available from version 11g, just distributed on the market. The OWL fragment is a subset of OWL DL called OWLPRIME. Limited expressive power
Advantages: this is the simplest case, exploiting of the ontology reasoning capabilities. The user query the ontology directly. Disadv: size of the ontology may grow, the query are possible only on the ontology: no possibility to query ontology joint with relational data Other scenarios are possible
Advantages: this is the simplest case, exploiting of the ontology reasoning capabilities. The user query the ontology directly. Disadv: size of the ontology may grow, the query are possible only on the ontology: no possibility to query ontology joint with relational data Other scenarios are possible
This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
Advantages: this is the simplest case, exploiting of the ontology reasoning capabilities. The user query the ontology directly. Disadv: size of the ontology may grow, the query are possible only on the ontology: no possibility to query ontology joint with relational data Other scenarios are possible
This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
Advantages: this is the simplest case, exploiting of the ontology reasoning capabilities. The user query the ontology directly. Disadv: size of the ontology may grow, the query are possible only on the ontology: no possibility to query ontology joint with relational data Other scenarios are possible
This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.

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Notes de l'éditeur