A general framework for trajectory data warehousing and visual OLAP

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

On querying and mining semantic-aware mobility timelines

“…Data cubes are views of a DW, used for multi-dimensional analysis, the so-called OnLine Analytical Processing (OLAP). The data cube paradigm has been extended to support spatial [4] and (raw) trajectory DWs [7,8,18], involving spatial, temporal, and thematic dimensions as well as spatial, spatio-temporal, and numerical measures. A DW model for semantically-enriched mobility data, called Mob-warehouse, was proposed in [20] to enrich trajectory data with domain knowledge by following the so-called 5W1H model (Who, Where, When, What, Why, How).…”

“…Thus, the algorithm first uses the inverted file to deal with textual constraints (lines [2][3][4][5][6][7][8].…”

A Semantic Model for Movement Data Warehouses

“…In [20] such a framework has been used to examine traffic data, in combination with tools for the visual analysis of spatiotemporal data. Then, in [12], that framework has been given a solid theoretical foundation, and it has been generalized to allow tailoring according to the specifics of the movement data. Other proposals extend spatial data warehouses to include in the model a temporal dimension for dynamic spatial data (e.g, [7]).…”

“…In addition, some (sequences of) points can be associated with annotations (e.g., textual contents of posts, travel diary notes) that may help to describe the movement (e.g., visited places and events, goals of stops and moves). However, developing a multidimensional model with rich semantics in order to realize the potential of movement data analysis in a MDW is an open research challenge [17,23,20,11,24,5,12,3]. This paper proposes a collection of constructs compatible with description logics and semantic Web standards [8] to semantically describe movement data and support powerful information analyses.…”

Lupa espacio-temporal: Una herramienta para el análisis visual de trayectorias en una bodega de datos

“…Leonardi et al [20] definen una BDT y un marco de trabajo [21] donde las trayectorias son tratadas como medidas y abordan el problema de su agregación y visualización, p. ej. "¿cuántas trayectorias pasaron por una determinada zona?".…”