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

SOWL QL: Querying Spatio - Temporal Ontologies in OWL

We introduce SOWL QL, a query language for spatio-temporal information in ontologies. Buildingupon SOWL (Spatio-Temporal OWL), an ontology for handling spatio-temporal information in OWL, SOWL QL supports querying over qualitative spatio-temporal information (expressed using natural language expressions such as “before”, “after”, “north of”, “south of”) rather than merely quantitative information (exact dates, times, locations). SOWL QL extends SPARQL with a powerful set of temporal and spatial operators, including temporal Allen topological, spatial directional and topological operations or combinations of the above. SOWL QL maintains simplicity of expression and also, upward and downward compatibility with SPARQL. Query translation in SOWL QL yields SPARQL queries implying that, querying spatio-temporal ontologies using SPARQL is still feasible but suffers from several drawbacks the most important of them being that, queries in SPARQL become particularly complicated and users must be familiar with the underlying spatio-temporal representation (the “N-ary relations” or the “4D-fluents” approach in this work). Finally, querying in SOWL QL is supported by the SOWL reasoner which is not part of the standard SPARQL translation. The run-time performance of SOWL QL has been assessed experimentally in a real data setting. A critical analysis of its performance is also presented

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