The flow of data generated from low-cost modern sensing technologies and wireless telecommunication devices enables novel research fields related to the management of this new kind of data and the implementation of appropriate analytics for knowledge extraction. In this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place when loading a moving object database with sampled location data originated e.g. from GPS recordings, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.
Trajectory Database (TD) management is a relatively new topic of database research, which has emerged due to the explosion of mobile devices and positioning technologies. Trajectory similarity search forms an important class of queries in TD with applications in trajectory data analysis and spatiotemporal knowledge discovery. In contrast to related works which make use of generic similarity metrics that virtually ignore the temporal dimension, in this paper we introduce a framework consisting of a set of distance operators based on primitive (space and time) as well as derived parameters of trajectories (speed and direction). The novelty of the approach is not only to provide qualitatively different means to query for similar trajectories, but also to support trajectory clustering and classification mining tasks, which definitely imply a way to quantify the distance between two trajectories. For each of the proposed distance operators we devise highly parametric algorithms, the efficiency of which is evaluated through an extensive experimental study using synthetic and real trajectory datasets.
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 on mobility data.
Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification. We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuri ng efficiency) and real (for assessing effectiveness) trajectory datasets. In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for "hands-on" analysis for trajectory data
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Technological advances in sensing technologies and wireless telecommunication devices enable research fields related to the management of trajectory data. 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. In this paper, the authors demonstrate a framework that transforms the traditional data cube model into a trajectory warehouse. As a proof-of-concept, the authors implement 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 on mobility data.
SEISMO-SURFER is a tool for collecting, querying and mining seismic data being developed in Java programming language using Oracle database system. The objective is to combine recent re search trends and results in the fields of spatial and spatio-temporal databases, data warehouses and data mining, as well as well established visualization techniques for geographical information. The database of the tool is automatically updated from remote sources while existing possibilities allow the querying on different earthquakes parameters, the analysis of the data for extraction of useful information and the graphical representation of the results via maps, charts etc.In the present work, we extend SEISMO-SURFER to include macroseismic data collected by the Geodynamic Institute and filled in a relative database. More specifically, the seismic parameters of the strong earthquakes, stored into the SEISMO-SURFER database, are linked to the macroseismic intensities observed at different sites. Administrative information for each site, local surface geol ogy, tectonic lines, damage photographs and detailed descriptions from newspapers are also in cluded.University of Piraeus and Geodynamic Institute are working together to continuously update and develop SEISMO-SURFER, concerning the data included, the variety of parameters stored and the mining algorithms supported for exploiting knowledge. I INTRODUCTIONAiming to develop a prototype software in order to combine recent research trends and results within the areas of spatial and spatio-temporal databases, data warehouses and extraction of new knowledge from large databases (data mining), lead to the development of the SEISMO-SURFER tool (Theodoridis 2003). SEISMO-SURFER is an example of cooperation between scientists of Informatics (especially those who involved in the information and knowledge management) and of Geophysics.As seismological data are multidimensional, they need to be stored and recovered by special techniques, more complex compared to those used for the traditional alphanumerical data. Under this point of view, spatial entities referred to temporal periods or temporal moments referred to lay ers of geographical information are under investigation within the frame of Database Management Systems. Furthermore data warehouse techniques are used in order to unify different sources of seismological data (available through Internet, for example). A user might ask information about the most destructive earthquakes in Europe during the last 20 years, or to limit his/her question in Greece only (drill-down operation) or to extent it world widely (roll-up operation). On the other hand, different layers of thematic information can be included, like geological maps, tectonic maps, popu lation maps etc, in order for the user to search for possible relations between the grade of damage and the epicentral distance or the distance of the damaged cities from the seismogenic fault, or be tween the damage and the dominant geology etc.From the above mentioned simple exa...
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