A Framework for Trajectory Data Warehousing

International Journal of Computing and Business Research

The usage of location aware devices, such as mobile phones and GPS-enabled devices, is widely spread nowadays, allowing access to large spatiotemporal datasets. The space-time nature of this kind of data results in the generation of huge amounts of trajectory data and imposes new challenges regarding their efficient management. To address this need, the traditional database technology has been extended into Moving Object Databases (MODs) that handle modeling, indexing and query processing issues for trajectories. Moreover, the analysis of such trajectory data raises opportunities for discovering behavioral patterns that can be exploited in applications like traffic management and service accessibility. Online analytical processing (OLAP) and data mining (DM) techniques have been employed in order to convert this vast amount of raw data into useful knowledge. Indicatively, the variable number of moving objects in different urban areas, the average speed of vehicles, the ups and downs of vehicles speed as well as useful insights, like discovering popular movements can be analyzed in a Trajectory Data Warehouse (TDW).

2007

In this paper we discuss how data warehousing technology can be used to store aggregate information about trajectories and perform OLAP operations over them. To this end, we define a data cube with spatial and temporal dimensions, discretized according to a regular grid. We investigate in depth some issues related to the computation of a holistic aggregate function, i.e, the presence, which returns the number of distinct trajectories occurring in a given spatio-temporal area. In particular, we introduce a novel way to compute an approximate, but nevertheless very accurate, presence aggregate function, which uses only a bounded amount of measures stored in the base cells of our cuboid. We also concentrate on the loading phase of our data warehouse, which has to deal with an unbounded stream of trajectory observations. We suggest how the complexity of this phase can be reduced, and we analyse the errors that this procedure induces at the level of the sub-aggregates stored in the base cells. These errors and the accuracy of our approximate aggregate functions are carefully evaluated by means of tests performed on synthetic trajectory datasets.

Turning a collection of simple time-geography data into mobility knowledgeis a key issue in many research domains, such as social analysis and mobility investigation. Although collecting mobility data has become technologically feasible, turning these huge sets of data into mobility knowledge is still an open issue. Data warehousing is a well-established technique used for analysis of summarized data, but is not yet adequate to support spatio-temporal feature-sets. This paper proposes a framework for improving the design and the implementation of data warehouses that support spatio-temporal concepts on top of relational DBMS. This framework has been used for designing and implementing a trajectory data warehouse (TDW) for analyzing traffic data. Besides, we show that this framework also supports OLAP, SOLAP and STOLAP queries.

2010

Abstract Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data.

International Journal of Computing and Business Research

International Journal of Managing Information Technology, 2010

To analyze complex phenomena which involve moving objects, Trajectory Data Warehouse (TDW) seems to be an answer for many recent decision problems related to various professions (physicians, commercial representatives, transporters, ecologists …) concerned with mobility. This work aims to make trajectories as a first class concept in the trajectory data conceptual model and to design a TDW, in which data resulting from mobile information collectors' trajectory are gathered. These data will be analyzed, according to trajectory characteristics, for decision making purposes, such as new products commercialization, new commerce implementation, etc.

2010 Eleventh International Conference on Mobile Data Management, 2010

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.

GeoInformatica, 2013

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International Journal of Data Warehousing and Mining, 2011

Technological advances in sensing technologies and wireless telecommunication devices enable novel research fields related to the management of trajectory data. As it usually happens in the data management world, the challenge after storing the data is the implementation of appropriate analytics for extracting useful knowledge. However, traditional data warehousing systems and techniques were not designed for analyzing trajectory data. Thus, in this work, we demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, we implemented T-Warehouse, a system that incorporates all the required steps for Visual Trajectory Data Warehousing, from trajectory reconstruction and ETL processing to Visual OLAP analysis to mobility data.

2009

In this paper we present an approach for storing and aggregating spatio-temporal patterns by using a Trajectory Data Warehouse (TDW). In particular, our aim is to allow the analysts to quickly evaluate frequent patterns mined from trajectories of moving objects occurring in a specific spatial zone and during a given temporal interval.