T-Warehouse: Visual OLAP analysis on trajectory data

Leonardi, Luca; Μαρκετοσ, Γερασιμοσ; Frentzos, Elias; Giatrakos, Nikos; Orlando, Salvatore; Pelekis, Nikos; Raffaetà, Alessandra; Roncato, Alessandro; Silvestri, Claudio; Theodoridis, Yannis

doi:10.1109/icde.2010.5447749

Cited by 28 publications

(29 citation statements)

References 5 publications

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“…We compared our approach against approaches in [12] (we refer to it as TDW) and [16] (we refer to it as Hermes). In particular, we compared the ETL performances, since both approaches [12] and [16] offer good performances in the ETL step.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Warehousing and querying trajectory data streams with error estimation

Masciari¹

2012

Proceedings of the Fifteenth International Workshop on Data Warehousing and OLAP

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In this paper, we address the problem of trajectory data streams warehousing and querying, that revealed really challenging as we deal with data (trajectories) for which the order of elements is relevant. We propose an end to end framework in order to make the querying step quite effective. We performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed techniques.

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Section: Experimental Evaluationmentioning

confidence: 99%

Warehousing and querying trajectory data streams with error estimation

Masciari¹

2012

Proceedings of the Fifteenth International Workshop on Data Warehousing and OLAP

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“…Only if the data set is small enough to fit into main memory, so-called dynamic aggregators can be applied directly within CommonGIS. The second approach by Leonardi et al [8] performs aggregation using a TDW. TDW [3], [4] have recently been developed and are a first step into OLAP analysis of trajectory data.…”

Section: Related Workmentioning

confidence: 99%

Fast Visual Trajectory Analysis Using Spatial Bayesian Networks

Liebig

Körner

May

2009

2009 IEEE International Conference on Data Mining Workshops

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During the past years the first tools for visual analysis of trajectory data appeared. Considering the growing sizes of trajectory collections, one important task is to ensure user interactivity during data analysis. In this paper we present a fast, model-based visualization approach for the analysis of location dependencies in large trajectory collections.Existing approaches are not suitable for visual dependency analysis as the size and complexity of trajectory data constrain ad hoc and advance computations. Also recent developments in the area of trajectory data warehouses cannot be applied because the spatial correlations are lost during trajectory aggregation. Our approach builds a compact model which represents the dependency structures of the data. The visualisation toolkit then interacts only with the model and is thus independent of the size of the underlying trajectory database. More precisely, we build a Bayesian Network model using the Scalable Sparse Bayesian Network Learning (SSBNL) algorithm [1], which we improve to represent also negative correlations. We implement our approach into the GIS MapInfo using MapBasic scripts for the user interface and an independent mediator script to retrieve patterns from the model. We demonstrate our approach using mobile phone data of the city of Milan, Italy.

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“…Based on the works of [16] and [14], the prototype system T-Warehouse presented in [12] that offers a Visual OLAP interface for multidimensional and interactive analysis as well as incorporates all the required steps for Visual Trajectory Data Warehousing.…”

Section: Related Workmentioning

confidence: 99%

Ad-hoc OLAP on Trajectory Data

Μαρκετοσ

Theodoridis

2010

2010 Eleventh International Conference on Mobile Data Management

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The application of Data Warehousing (DW) and OLAP techniques on conventional data has been extensively studied in the literature. On the other hand, Trajectory Data Warehousing and Trajectory OLAP are relatively new research areas, which have to deal with the spatiotemporal (hence dynamic) nature of such data. In this paper, we present an innovative organization of a trajectory data cube in order to be able to answer OLAP queries considering different interpretations of the notion of trajectory. Thus, ad-hoc analysis on trajectory data cubes can be achieved, which can be really useful for a number of applications. Preliminary experimental results illustrate the applicability and efficiency of our approach.

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T-Warehouse: Visual OLAP analysis on trajectory data

Cited by 28 publications

References 5 publications

Warehousing and querying trajectory data streams with error estimation

Warehousing and querying trajectory data streams with error estimation

Fast Visual Trajectory Analysis Using Spatial Bayesian Networks

Ad-hoc OLAP on Trajectory Data

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