article

A general framework for trajectory data warehousing and visual OLAP

Authors:

Salvatore Orlando,

Alessandra Raffaetà,

Alessandro Roncato,

Claudio Silvestri,

Gennady Andrienko,

Natalia AndrienkoAuthors Info & Claims

Geoinformatica, Volume 18, Issue 2

Pages 273 - 312

https://doi.org/10.1007/s10707-013-0181-3

Published: 01 April 2014 Publication History

Abstract

In this paper we present a formal framework for modelling a trajectory data warehouse (TDW), namely a data warehouse aimed at storing aggregate information on trajectories of moving objects, which also offers visual OLAP operations for data analysis. The data warehouse model includes both temporal and spatial dimensions, and it is flexible and general enough to deal with objects that are either completely free or constrained in their movements (e.g., they move along a road network). In particular, the spatial dimension and the associated concept hierarchy reflect the structure of the environment in which the objects travel. Moreover, we cope with some issues related to the efficient computation of aggregate measures, as needed for implementing roll-up operations. The TDW and its visual interface allow one to investigate the behaviour of objects inside a given area as well as the movements of objects between areas in the same neighbourhood. A user can easily navigate the aggregate measures obtained from OLAP queries at different granularities, and get overall views in time and in space of the measures, as well as a focused view on specific measures, spatial areas, or temporal intervals. We discuss two application scenarios of our TDW, namely road traffic and vessel movement analysis, for which we built prototype systems. They mainly differ in the kind of information available for the moving objects under observation and their movement constraints.

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Cited By

Garani GArboleda FVerykios V(2022)A novel approach for handling semantic trajectories on data warehousesIntelligent Decision Technologies10.3233/IDT-22012316:4(679-690)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDT-220123
Oikonomou KGarani GBimonte SWrembel R(2022)What Logical Model Is Suitable for Relational Trajectory Data Warehouses?Database and Expert Systems Applications10.1007/978-3-031-12423-5_30(389-403)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-12423-5_30
Ghane SKulik LRamamoharao K(2020)A differentially private algorithm for range queries on trajectoriesKnowledge and Information Systems10.1007/s10115-020-01520-w63:2(277-303)Online publication date: 28-Oct-2020
https://dl.acm.org/doi/10.1007/s10115-020-01520-w
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Index Terms

A general framework for trajectory data warehousing and visual OLAP
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

Index terms have been assigned to the content through auto-classification.

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cover image Geoinformatica

Geoinformatica Volume 18, Issue 2

April 2014

239 pages

ISSN:1384-6175

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Copyright © Copyright © 2014 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2014

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Cited By

Garani GArboleda FVerykios V(2022)A novel approach for handling semantic trajectories on data warehousesIntelligent Decision Technologies10.3233/IDT-22012316:4(679-690)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDT-220123
Oikonomou KGarani GBimonte SWrembel R(2022)What Logical Model Is Suitable for Relational Trajectory Data Warehouses?Database and Expert Systems Applications10.1007/978-3-031-12423-5_30(389-403)Online publication date: 22-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-12423-5_30
Ghane SKulik LRamamoharao K(2020)A differentially private algorithm for range queries on trajectoriesKnowledge and Information Systems10.1007/s10115-020-01520-w63:2(277-303)Online publication date: 28-Oct-2020
https://dl.acm.org/doi/10.1007/s10115-020-01520-w
Ghane SKulik LRamamohanarao KSacharidis DGamper JBöhlen M(2018)Publishing spatial histograms under differential privacyProceedings of the 30th International Conference on Scientific and Statistical Database Management10.1145/3221269.3223039(1-12)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.1145/3221269.3223039
Fanaeepour MRubinstein B(2018)Differentially private counting of users' spatial regionsKnowledge and Information Systems10.1007/s10115-017-1113-654:1(5-32)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10115-017-1113-6
Trajcevski GDonevska IVaisman AAvci BZhang TTian D(2015)Semantics-Aware Warehousing of Symbolic TrajectoriesProceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming10.1145/2833165.2833174(1-8)Online publication date: 3-Nov-2015
https://dl.acm.org/doi/10.1145/2833165.2833174
Tanin EXie HAltingovde ICambazoglu BTonellotto N(2015)Count or Not to CountProceedings of the 2015 Workshop on Large-Scale and Distributed System for Information Retrieval10.1145/2809948.2809954(13-14)Online publication date: 22-Oct-2015
https://dl.acm.org/doi/10.1145/2809948.2809954
Song JGuo CWang ZZhang YYu GPierson J(2015)HaoLapJournal of Systems and Software10.1016/j.jss.2014.09.024102:C(167-181)Online publication date: 1-Apr-2015
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Fanaeepour MKulik LTanin EP. Rubinstein B(2015)The CASE histogramGeoinformatica10.1007/s10707-015-0228-819:4(747-798)Online publication date: 1-Oct-2015
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Xie HTanin EKulik LScheuermann PTrajcevski GFanaeepour M(2014)Euler histogram treeProceedings of the 7th ACM SIGSPATIAL International Workshop on Computational Transportation Science10.1145/2674918.2674921(18-24)Online publication date: 4-Nov-2014
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