scholarly journals Trajectory Data Warehouses: Design and Implementation Issues

2007 ◽  
Vol 1 (2) ◽  
pp. 211-232 ◽  
Author(s):  
Salvatore Orlando ◽  
Renzo Orsini ◽  
Alessandra Raffaeta ◽  
Alessandro Roncato ◽  
Claudio Silvestri
Keyword(s):  
Data Warehouses ◽  
Trajectory Data ◽  
Design And Implementation

A Trajectory Ontology Design Pattern for Semantic Trajectory Data Warehouses

2019 ◽  
pp. 83-104 ◽  
Author(s):  
Marwa Manaa ◽  
Thouraya Sakouhi ◽  
Jalel Akaichi
Keyword(s):  
Case Studies ◽  
Design Pattern ◽  
Design Patterns ◽  
Domain Knowledge ◽  
Data Warehouses ◽  
Trajectory Data ◽  
Semantic Approach ◽  
Mobility Data ◽  
Ontology Design ◽  
Ontology Design Pattern

Mobility data became an important paradigm for computing performed in various areas. Mobility data is considered as a core revealing the trace of mobile objects displacements. While each area presents a different optic of trajectory, they aim to support mobility data with domain knowledge. Semantic annotations may offer a common model for trajectories. Ontology design patterns seem to be promising solutions to define such trajectory related pattern. They appear more suitable for the annotation of multiperspective data than the only use of ontologies. The trajectory ontology design pattern will be used as a semantic layer for trajectory data warehouses for the sake of analyzing instantaneous behaviors conducted by mobile entities. In this chapter, the authors propose a semantic approach for the semantic modeling of trajectory and trajectory data warehouses based on a trajectory ontology design pattern. They validate the proposal through real case studies dealing with behavior analysis and animal tracking case studies.

Trajectory Data Warehouses

2014 ◽  
pp. 475-506
Author(s):  
Alejandro Vaisman ◽  
Esteban Zimányi
Keyword(s):  
Data Warehouses ◽  
Trajectory Data

Towards Trajectory Data Warehouses

2008 ◽  
pp. 189-211 ◽  
Author(s):  
N. Pelekis ◽  
A. Raffaetà ◽  
M. -L. Damiani ◽  
C. Vangenot ◽  
G. Marketos ◽  
...  
Keyword(s):  
Data Warehouses ◽  
Trajectory Data

A Meta-model Based Approach for Data Warehouses Design and Implementation

2015 ◽  
Author(s):  
Leopoldo Zepeda ◽  
Luis Santillan ◽  
Elizabeth Cecena ◽  
Emir Manjarrez ◽  
Liliana Vega ◽  
...  
Keyword(s):  
Data Warehouses ◽  
Meta Model ◽  
Design And Implementation ◽  
Model Based

Design and Implementation of Active Stream Data Warehouses

2021 ◽  
pp. 288-311
Author(s):  
Sandro Bimonte ◽  
Omar Boussaid ◽  
Michel Schneider ◽  
Fabien Ruelle
Keyword(s):  
Data Warehouse ◽  
Conceptual Modeling ◽  
Data Warehouses ◽  
Stream Data ◽  
Modeling Tools ◽  
Uml Profile ◽  
Classical Information ◽  
Design And Implementation ◽  
Eca Rules

In the era of Big Data, more and more stream data is available. In the same way, Decision Support Systems (DSS) tools, such as data warehouses and alert systems, become more and more sophisticated, and conceptual modeling tools are consequently mandatory for successfully DSS projects. Formalisms such as UML and ER have been widely used in the context of classical information and data warehouse systems, but they have not been investigated yet for stream data warehouses to deal with alert systems. Therefore, in this article, the authors introduce the notion of Active Stream Data Warehouse (ASDW) and this article proposes a UML profile for designing Active Stream Data Warehouses. Indeed, this article extends the ICSOLAP profile to take into account continuous and window OLAP queries. Moreover, this article studies the duality of the stream and OLAP decision-making process and the authors propose a set of ECA rules to automatically trigger OLAP operators. The UML profile is implemented in a new OLAP architecture, and it is validated using an environmental case study concerning the wind monitoring.

scholarly journals Mobility Data Warehouses

2019 ◽  
Vol 8 (4) ◽  
pp. 170 ◽  
Author(s):  
Alejandro Vaisman ◽  
Esteban Zimányi
Keyword(s):  
Spatial Data ◽  
Moving Objects ◽  
Moving Object ◽  
Data Warehouses ◽  
Mobility Analysis ◽  
Trajectory Data ◽  
Data Types ◽  
Seamless Integration ◽  
Moving Object Database ◽  
Mobility Data

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB.

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