Mobility Patterns

Mouza, Cédric Du; Rigaux, Philippe

doi:10.1007/s10707-005-4574-9

Cited by 79 publications

(39 citation statements)

References 41 publications

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“…A conceptual view on trajectories has been proposed by Spaccapietra et al [34] who decompose a trajectory into a series of moves and stops. This stop-move conceptualization has been applied in several other trajectory studies, and the stops and moves are often coupled with corresponding geographic information to help interpret them [1,18,30]. Transportation networks are an important type of geographic information which is often utilized to make sense of the trajectories [35,27].…”

Section: Semantic Trajectory Ontologiesmentioning

confidence: 99%

A Geo-ontology Design Pattern for Semantic Trajectories

Janowicz

Carral

et al. 2013

Lecture Notes in Computer Science

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Abstract. Trajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.

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Section: Semantic Trajectory Ontologiesmentioning

confidence: 99%

A Geo-ontology Design Pattern for Semantic Trajectories

Janowicz

Carral

et al. 2013

Lecture Notes in Computer Science

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“…Such trajectory modeling concepts have been largely used in several projects on mobility, e.g., GeoP-KDD 2 (Geographic Privacy-aware Knowledge Discovery and Delivery) [Giannotti and Pedreschi 2008], MODAP 3 (Mobility, Data Mining, and Privacy) and SEEK 4 (SEmantic Enrichment of trajectory Knowledge discovery). These modeling concepts are well fitted for the semantic analysis of movements, like tourist movements [Alvares et al 2007], the semantic interpretation of stops [Gómez and Vaisman 2009] and moves [Mouza and Rigaux 2005].…”

Section: Trajectory Data Modelingmentioning

confidence: 99%

Semantic trajectories

Yan

Chakraborty

Parent

et al. 2013

ACM Trans. Intell. Syst. Technol.

162

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With the large-scale adoption of GPS equipped mobile sensing devices, positional data generated by moving objects (e.g., vehicles, people, animals) are being easily collected. Such data are typically modeled as streams of spatio-temporal (x,y,t) points, called trajectories. In recent years trajectory management research has progressed significantly towards efficient storage and indexing techniques, as well as suitable knowledge discovery. These works focused on the geometric aspect of the raw mobility data. We are now witnessing a growing demand in several application sectors (e.g., from shipment tracking to geo-social networks) on understanding the semantic behavior of moving objects. Semantic behavior refers to the use of semantic abstractions of the raw mobility data, including not only geometric patterns but also knowledge extracted jointly from the mobility data and the underlying geographic and application domains information. The core contribution of this paper lies in a Semantic Model and a Computation and Annotation Platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations. We also analyze a number of experiments we did with semantic trajectories in different domains.

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“…Moving objects, carrying location-aware devices, produce trajectory data in the form of a sample of (O id , t, x, y)-tuples, that contain object identifier and timespace information. Recently, the notions of stops and moves were introduced [2,10]. These concepts serve to compress the trajectory data that is produced by moving objects using application-dependent places of interest.…”

Section: Introductionmentioning

confidence: 99%

Aggregation languages for moving object and places of interest

Gómez

Kuijpers

Vaisman

2008

Proceedings of the 2008 ACM Symposium on Applied Computing

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We address aggregate queries over GIS data and moving object data, where non-spatial information is stored in a data warehouse. We propose a formal data model and query language to express complex aggregate queries. Next, we study the compression of trajectory data, produced by moving objects, using the notions of stops and moves. We show that stops and moves are expressible in our query language and we consider a fragment of this language, consisting of regular expressions to talk about temporally ordered sequences of stops and moves. This fragment can be used not only for querying, but also for expressing data mining and pattern matching tasks over trajectory data.

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Mobility Patterns

Cited by 79 publications

References 41 publications

A Geo-ontology Design Pattern for Semantic Trajectories

A Geo-ontology Design Pattern for Semantic Trajectories

Semantic trajectories

Aggregation languages for moving object and places of interest

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