Parallel and Distributed Processing of Spatial Preference Queries using Keywords

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Big Data Management and Analytics for Mobility Forecasting in datAcron

The exploitation of heterogeneous data sources offering very large historical and streaming data is important to increasing the accuracy of operations when analysing and predicting future states of moving entities (planes, vessels, etc.). This article presents the overall goals and big data challenges addressed by datAcron on big data analytics for time-critical mobility forecasting

An architecture for the design of context-aware conversational agents

Proceedings of: 8th International Conference on Practical Applications of Agents and Multiagent Systems, Salamanca, Spain, April 26-28, 2010.In this paper, we present a architecture for the development of conversational agents that provide a personalized service to the user. The different agents included in our architecture facilitate an adapted service by taking into account context information and users specific requirements and preferences. This functionality is achieved by means of the introduction of a context manager and the definition of user profiles. We describe the main characteristics of our architecture and its application to develop and evaluate an information system for an academic domain.CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02- 02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.Publicad

Contextualized Analysis of Movement Events

For understanding the circumstances, causes, and consequences of events that may happen during movement (e.g., harsh brake, sharp turn), it is necessary to analyze event context. The context includes dynamic attributes of the moving objects before and after the event and external context elements such as other moving objects, weather, terrain, etc. To explore events in context, we propose an analytical workflow including event contextualization, context pattern detection, and exploration of the spatio-temporal distribution of the detected patterns. The approach involves clustering of events based on the similarity of their contexts and interactive visual techniques for exploration of the distribution of the clusters in time, geographic space, and multidimensional attribute space. In close collaboration with domain experts, we apply our method to real-world vehicle trajectories with the purpose of identifying and investigating potentially dangerous driving behaviors

Hot Spot Analysis over Big Trajectory Data

Hot spot analysis is the problem of identifying statistically significant spatial clusters from an underlying data set. In this paper, we study the problem of hot spot analysis for massive trajectory data of moving objects, which has many real-life applications in different domains, especially in the analysis of vast repositories of historical traces of spatio-temporal data (cars, vessels, aircrafts). In order to identify hot spots, we propose an approach that relies on the Getis-Ord statistic, which has been used successfully in the past for point data. Since trajectory data is more than just a collection of individual points, we formulate the problem of trajectory hot spot analysis, using the Getis-Ord statistic. We propose a parallel and scalable algorithm for this problem, called THS, which provides an exact solution and can operate on vast-sized data sets. Moreover, we introduce an approximate algorithm (aTHS) that avoids exhaustive computation and trades-off accuracy for efficiency in a controlled manner. In essence, we provide a method that quantifies the maximum induced error in the approximation, in relation with the achieved computational savings. We develop our algorithms in Apache Spark and demonstrate the scalability and efficiency of our approach using a large, historical, real-life trajectory data set of vessels sailing in the Eastern Mediterranean for a period of three years. Document type: Conference objec