A Conceptual Data Model for Trajectory Data Mining

Bogorny, Vânia; Heuser, Carlos A.; Älvares, Luís Otávio

doi:10.1007/978-3-642-15300-6_1

Cited by 17 publications

(13 citation statements)

References 10 publications

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“…In this model, spatio-temporal positions are complemented with annotations. To model more semantic information for data mining, Bogorny et al presented a semantic trajectory conceptual data model named CONSTAnt, which involves the concepts of semantic sub trajectories, semantic points, and geographical places, events and behaviour [52,58,59]. Damiani et al proposed a data model of symbolic trajectories representing different types of semantics [60].…”

Section: Related Workmentioning

confidence: 99%

Modeling and Querying Moving Objects with Social Relationships

Zhang

Lü

2016

IJGI

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Current moving-object database (MOD) systems focus on management of movement data, but pay less attention to modelling social relationships between moving objects and spatial-temporal trajectories in an integrated manner. This paper combines moving-object database and social network systems and presents a novel data model called Geo-Social-Moving (GSM) that enables the unified management of trajectories, underlying geographical space and social relationships for mass moving objects. A bulk of user-defined data types and corresponding operators are also proposed to facilitate geo-social queries on moving objects. An implementation framework for the GSM model is proposed, and a prototype system based on native Neo4J is then developed with two real-world data sets from the location-based social network systems. Compared with solutions based on traditional extended relational database management systems characterized by time-consuming table join operations, the proposed GSM model characterized by graph traversal is argued to be more powerful in representing mass moving objects with social relationships, and more efficient and stable for geo-social querying.

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Section: Related Workmentioning

confidence: 99%

Modeling and Querying Moving Objects with Social Relationships

Zhang

Lü

2016

IJGI

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“…Mining raw data about moving objects (e.g., see Andrienko, Andrienko, & Heurich, 2011;Bogorny, Heuser, & Alvares, 2010) can be useful to extract information that can later be used to build semantic trajectories. For example, in some situations the task of automatic place labeling can be considered as a multi-class classification task (Do & Gatica-Perez, 2014).…”

Section: Analysis and Mining Of Semantic Mobility Datamentioning

confidence: 99%

Semantic management of moving objects: A vision towards smart mobility

Ilarri

Stojanović

Ray

2015

Expert Systems with Applications

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. b s t r a c tThis position paper presents our vision for the semantic management of moving objects. We argue that exploiting semantic techniques in mobility data management can bring valuable benefits to many domains characterized by the mobility of users and moving objects in general, such as traffic management, urban dynamics analysis, ambient assisted living, emergency management, m-health, etc. We present the state-of-the-art in the domain of management of semantic locations and trajectories, and outline research challenges that need to be investigated to enable a full-fledged and intelligent semantic management of moving objects and location-based services that support smarter mobility. We propose a distributed framework for the semantic enrichment and management of mobility data and analyze the potential deployment and exploitation of such a framework.

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“…Another conceptual model for trajectories has been proposed in Bogorny et al. (). That model associates several data mining tasks to the stops and moves of trajectories.…”

Section: Basic Concepts and Related Workmentioning

confidence: 99%

CONSTAnT – A Conceptual Data Model for Semantic Trajectories of Moving Objects

Bogorny

Renso²,

Aquino

et al. 2013

Transactions in GIS

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105

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Several works have been proposed in the last few years for raw trajectory data analysis, and some attempts have been made to define trajectories from a more semantic point of view. Semantic trajectory data analysis has received significant attention recently, but the formal definition of semantic trajectory, the set of aspects that should be considered to semantically enrich trajectories and a conceptual data model integrating these aspects from a broad sense is still missing. This article presents a semantic trajectory conceptual data model named CONSTAnT, which defines the most important aspects of semantic trajectories. We believe that this model will be the foundation for the design of semantic trajectory databases, where several aspects that make a trajectory "semantic" are taken into account. The proposed model includes the concepts of semantic subtrajectory, semantic points, geographical places, events, goals, environment and behavior, to create a general concept of semantic trajectory. The proposed model is the result of several years of work by the authors in an effort to add more semantics to raw trajectory data for real applications. Two application examples and different queries show the flexibility of the model for different domains.In order to clarify the proposed conceptual model for semantic trajectories, this section defines some basic concepts and presents the closest related works. CONSTAnT -A Conceptual Data Model for Semantic Trajectories 67

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A Conceptual Data Model for Trajectory Data Mining

Cited by 17 publications

References 10 publications

Modeling and Querying Moving Objects with Social Relationships

Modeling and Querying Moving Objects with Social Relationships

Semantic management of moving objects: A vision towards smart mobility

CONSTAnT – A Conceptual Data Model for Semantic Trajectories of Moving Objects

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