Compact Trip Representation over Networks

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46049-9_23[Abstract] We present a new Compact Trip Representation ( CTRCTR ) that allows us to manage users’ trips (moving objects) over networks. These could be public transportation networks (buses, subway, trains, and so on) where nodes are stations or stops, or road networks where nodes are intersections. CTRCTR represents the sequences of nodes and time instants in users’ trips. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array ( CSACSA ), which provides both a compact representation and interesting indexing capabilities. We also represent the temporal component of the trips, that is, the time instants when users visit nodes in their trips. We create a sequence with these time instants, which are then self-indexed with a balanced Wavelet Matrix ( WMWM ). This gives us the ability to solve range-interval queries efficiently. We show how CTRCTR can solve relevant spatial and spatio-temporal queries over large sets of trajectories. Finally, we also provide experimental results to show the space requirements and query efficiency of CTRCTR .Ministerio de Economía y Competitividad; TIN2013-46238-C4-3-RMinisterio de Economía y Competitividad; TIN2013-47090-C3-3-PMinisterio de Economía y Competitividad; IDI-20141259Ministerio de Economía y Competitividad; ITC-20151305Ministerio de Economía y Competitividad; ITC-20151247Xunta de Galicia; GRC2013/053Chile.Fondo Nacional de Desarrollo Científico y Tecnológico; 1140428Chile. Instituto de Sistemas Complejos de Ingeniería ; FBO 1

Methodology for integrated socio-economic assessment of offshore platforms : towards facilitation of the implementation of the marine strategy framework directive

In this paper a Methodology for Integrated Socio-Economic Assessment (MISEA) of the viability and sustainability of different designs of Multi-Use Offshore Platforms (MUOPs) is presented. MUOPs are designed for multi-use of ocean space for energy extraction (wind power production and wave energy), aquaculture and transport maritime services. The developed methodology allows identification, valuation and assessment of: the potential range of impacts of a number of feasible designs of MUOP investments, and the likely responses of those impacted by the investment project. This methodology provides decision-makers with a valuable decision tool to assess whether a MUOP project increases the overall social welfare and hence should be undertaken, under alternative specifications regarding its design, the discount rate and the stream of net benefits, if a Cost-Benefit Analysis (CBA) is to be followed or sensitivity analysis of selected criteria in a Multi-Criteria Decision Analysis (MCDA) framework. Such a methodology is also crucial for facilitating of the implementation of the Marine Strategy Framework Directive (MSFD adopted in June 2008) that aims to achieve good environmental status of the EU's marine waters by 2020 and to protect the resource base upon which marine-related economic and social activities depend. According to the MSFD each member state must draw up a program of cost-effective measures, while prior to any new measure an impact assessment which contains a detailed cost-benefit analysis of the proposed measures is required

The horse in European history, 1550-1900

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Towards Trajectory Data Warehouses

Data warehouses have received the attention of the database community as a technology for integrating all sorts of transactional data, dispersed within organisations whose applications utilise either legacy (non-relational) or advanced relational database systems. Data warehouses form a technological framework for supporting decision-making processes by providing informational data. A data warehouse is defined as a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management of decision-making process

Towards Trajectory Data Warehouses

Data warehouses have received the attention of the database community as a technology for integrating all sorts of transactional data, dispersed within organisations whose applications utilise either legacy (non-relational) or advanced relational database systems. Data warehouses form a technological framework for supporting decision-making processes by providing informational data. A data warehouse is defined as a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management of decision-making process

T-Warehouse: Visual OLAP Analysis on Trajectory Data

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. © 2010 IEEE