Modeling and querying moving objects in networks

Güting, Ralf Hartmut; Almeida, Teixeira de; Ding, Zhiming

doi:10.1007/s00778-005-0152-x

Cited by 193 publications

(106 citation statements)

References 48 publications

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“…However, semantic information of trajectories such as stay points, objects' behaviour, contextual annotations or social relationships mentioned in our study was neglected [37,38]. Those data models for raw trajectories did not embrace different underlying environments such as free geographical spaces [6,37], networks [2,39], and indoor spaces [40][41][42]. Additionally, early related studies were more prone to simulate tracking data produced by a network-based generator [43], Oporto [44], GSTD [45], or GMOBench [46].…”

Section: Related Workmentioning

confidence: 96%

“…Moving object databases (MOD) allow modelling, manage moving entities such as people, vehicles and vessels, and have been extensively studied over the past few years, such as data modelling [1][2][3][4][5][6], indexing [7][8][9][10] , and querying [11][12][13][14][15]. This research focuses on the management of movements and involved geographic environments, but ignores the social relationships between moving objects.…”

Section: Introductionmentioning

confidence: 99%

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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: 96%

Section: Introductionmentioning

confidence: 99%

Modeling and Querying Moving Objects with Social Relationships

Zhang

Lü

2016

IJGI

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“…Sistla et al [12] propose a model, called MOST, to model the moving object with a time-related function, which improves the efficiency of storage and querying of moving objects in a database. Ting et al [16] present a simplistic network model for moving objects. Some index structures such as R-tree [5] and B þ -tree [7] are also used to optimize spatial-temporal queries.…”

Section: Spatial-temporal Data Managementmentioning

confidence: 99%

Bus-OLAP: A Data Management Model for Non-on-Time Events Query Over Bus Journey Data

et al. 2018

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Increasing the on-time rate of bus service can prompt the people's willingness to travel by bus, which is an effective measure to mitigate the city traffic congestion. Performing queries on the bus arrival can be used to identify and analyze various kinds of non-on-time events that happened during the bus journey, which is helpful for detecting the factors of delaying events, and providing decision support for optimizing the bus schedules. We propose a data management model, called Bus-OLAP, for querying bus journey data, considering the characteristics of bus running and the scenarios of nonon-time analysis. While fulfilling typical requirements of bus journey data queries, Bus-OLAP not only provides a flexible way to manage the data and to implement multiple granularity data query and update, but it also supports distributed queries and computation. The experiments on real-world bus journey data verify that Bus-OLAP is effective and efficient.

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“…Transportation networks are an important type of geographic information which is often utilized to make sense of the trajectories [35,27]. Other geo-data, such as those on Points Of Interest (POI), weather, land use, vegetation, and habitats, have also been employed to improve the understanding of trajectories [34,6,19,38]. While geographic information is the key contributor and commonality, domain knowledge is included in trajectories and their ontologies to help understand domain facts [37].…”

Section: Semantic Trajectory Ontologiesmentioning

confidence: 99%

A Geo-ontology Design Pattern for Semantic Trajectories

Janowicz

Carral

et al. 2013

Spatial Information Theory

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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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Modeling and querying moving objects in networks

Cited by 193 publications

References 48 publications

Modeling and Querying Moving Objects with Social Relationships

Modeling and Querying Moving Objects with Social Relationships

Bus-OLAP: A Data Management Model for Non-on-Time Events Query Over Bus Journey Data

A Geo-ontology Design Pattern for Semantic Trajectories

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