Hybrid queries over symbolic and spatial trajectories: a usage scenario

Symbolic trajectories is a novel data model recently proposed for the modeling and querying of temporally annotated sequences of symbolic descriptions, representing e.g. transportation means, places of interest, and so forth. Unlike geometric trajectories, symbolic trajectories capture the thematic dimension of movement. In this demonstration, we illustrate a practical approach to the querying of hybrid trajectories, combining the symbolic and geometric dimension in a multidimensional trajectory. The system runs on the Secondo moving object database. The multi-dimensional trajectories are obtained from the GeoLife dataset

Symbolic trajectories and application challenges

Describing the location history of moving objects exclusively in geometric terms is no longer sufficient, whereas more expressive data models capturing the complexity and heterogeneity of movement data are needed. Following this trend, the data model of symbolic trajectories has been recently proposed for the representation of content-rich trajectories in databases. The model provides a simple notation and a powerful and fully operational pattern-based query language for trajectory matching and rewriting. In this paper, we overview the key features of the model and sketch two applications cases, the former regarding the integration of heterogeneous mobility data (GPS and transportation modes), the latter the representation of migration patterns in animal ecology. The goal is to show the flexibility of the model and, at the same time, to prospect possible directions of research

Determination of crustal fluid residence times using nucleogenic 39Ar

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate the problem of evaluating the top k distinguished “features” for a “cluster” based on weighted proximity relationships between the cluster and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. Combining a standard multi-step paradigm with new lower and upper proximity bounds, we presented an efficient algorithm to solve the problem. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm