\section{Introduction}

With the growth of mobile devices equipped with geographical localization services, it has become economical and technically feasible to capture moving object traces (i.e. trajectories) in real life. Many interesting applications are being developed based on the analysis of moving object trajectories. For example, in a traffic management system, traffic jams may be determined by mining movement patterns of groups of cars. Similarly, in a zoology application, the analysis of a group of bird trajectories can help explaining their migration patterns. In a delivery system, discovering the trucks' movement patterns may be used as input to decision making processes, such as delivery scheduling. From a trajectory data management point of view, these applications require (1) a more structured recording of movement, which allows managing trajectories as first class citizens and attaching whatever semantics the application requires, and (2) methods to model and organize a set of trajectories multidimensionally. From a trajectory analysis point of view, these applications ask for (3) trajectory real time analysis, and (4) trajectory historical analysis, to support the decision-making process by computing aggregations and by visualizing them according to different perspectives and levels of details. These four requirements are key to facilitating the development of applications relying on trajectory data.  

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%Mudei o final da penultima sentenca do paragrafo acima que estava assim antes: by allowing historical analysis and the visualization 
%Mudei basicamente por causa do "historical analysis"
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Substantial research has been conducted on providing methods and prototype systems for storing, querying and analyzing moving object trajectories in relational and multidimensional models (e.g. \cite{Guting2000}  \cite{Pelekis2006} \cite{Orlando2007a} \cite{Orlando2007b} \cite{Spaccapietra2008}). However, none of the proposed approaches provides an adequate solution for managing trajectory data as first class objects in transactional and multidimensional environments. Moreover, only the approach in \cite{Spaccapietra2008} provides a semantic view of trajectory, which enables applications to associate whatever semantics they want with the trajectories. However, this approach is only applicable to transactional schema. Indeed, no work has been published using trajectories as a semantic objects on multidimensional data modeling.

To fill the gap between the transactional view and the multidimensional view on trajectories, a novel approach has to be developed to enable applications to perform traditional operating queries over semantic trajectories stored in transactional databases and compute aggregations on semantic trajectories stored in multidimensional data warehouses. Thus, a set of aggregation functions on semantic trajectory is required. In addition, a semantic view of trajectories is needed for both transactional and multidimensional schemas. This mandates that trajectories be treated as first class objects in both relational and multidimensional schemas, which is beyond the capability of current spatio-temporal prototypes. Therefore, our contributions  are:

\begin{itemize}
	\item An object-relational meta-schema for semantic trajectories;
	\item A star meta-schema for semantic trajectories;
	\item A set of semantic trajectory aggregation functions;
	\item Two ETL algorithms for populating multidimensional databases from object-relational databases of the operating environment of a corporation by considering trajectories as first class objects with semantic data associated with them.
\end{itemize}

The rest of this paper is organized as follows. Section~\ref{sec:RelatedWork} surveys related work. Section~\ref{sec:preliminares} specifies a set of requirements for trajectory modeling and analysis, using an application scenario based on truck fleet management, and informally introduces the definitions used throughout this paper. Section~\ref{secao:contribuicoes} describes two modeling approaches that consider trajectories as first class objects, based on the requirements previously listed, whose purposes are: (i) defining a trajectory data type; (ii) designing trajectory data schemas of both transactional and multidimensional environments; (iii) specifying algorithms for populating operational databases and data warehouses with trajectories treated as first class objects; and (iv) designing a set of aggregation functions for enabling real time and historical analysis over traditional and multidimensional DB, respectively. Section~\ref{secao:experimentos} details the experiments conducted in a real-world application for managing  a delivery truck fleet. Finally, Section~\ref{sec:conclusao} concludes the paper and highlights future work.


