Practical Data Management Techniques for Vehicle Tracking Data
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A conceptual view on trajectories
Data & Knowledge Engineering
Aggregation languages for moving object and places of interest
Proceedings of the 2008 ACM symposium on Applied computing
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Dynamic modeling of trajectory patterns using data mining and reverse engineering
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A method for predicting future location of mobile user for location-based services system
Computers and Industrial Engineering
ROOTS, The ROving Objects Trip Simulator
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
The design of distributed real-time video analytic system
Proceedings of the first international workshop on Cloud data management
A Semantic Approach for the Modeling of Trajectories in Space and Time
ER '09 Proceedings of the ER 2009 Workshops (CoMoL, ETheCoM, FP-UML, MOST-ONISW, QoIS, RIGiM, SeCoGIS) on Advances in Conceptual Modeling - Challenging Perspectives
A conceptual data model for trajectory data mining
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Implementing a qualitative calculus to analyse moving point objects
Expert Systems with Applications: An International Journal
Event-based semantic visualization of trajectory data in urban city with a space-time cube
VIS '10 Proceedings of the 3rd WSEAS international conference on Visualization, imaging and simulation
Data semantics in location-based services
Journal on Data Semantics III
A continuous reverse skyline query processing considering the mobility of query objects
IDCS'12 Proceedings of the 5th international conference on Internet and Distributed Computing Systems
A generic data model for moving objects
Geoinformatica
Direction-preserving trajectory simplification
Proceedings of the VLDB Endowment
Intelligent Data Analysis
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Urban areas get more and more congested everyday due to the increasing number of moving vehicles. This imposes the need for efficient analysis, modeling, and processing of traffic data. Moreover, the extraction of additional information about traffic conditions, optional routes and the possible prediction of troublesome situations, such as traffic jams, becomes necessary. In this work, we describe the analysis, pre-processing, modeling, and storage techniques for trajectory data that constitute a Moving Object Database (MOD). MOD is the backbone of the 驴驴驴驴驴驴驴驴驴 ("PATH-FINDER" in Greek) system, which specifically focuses on extracting further information about the movement of vehicles in the Athens municipal area. Based on real-world requirements, we initially analyse the traffic data and make modeling decisions to capture these requirements in a MOD. We then design MOD focusing on the spatiotemporal concepts, relations and restrictions among the characteristic concepts of the system 驴 namely, the vehicles, trajectories, and roads. Furthermore, specific, innovative pre-processing, design, and storage techniques for the trajectory data in MOD are given. Then, we present the architecture of 驴驴驴驴驴驴驴驴驴; its core components are the characteriser, cluster finder, and associator, which are used to perform data extraction in MOD. A mining language to accommodate typical data extraction queries is presented, in terms of syntax and semantics. Answers to characteristic, complex questions on MOD, which are based on real-world data about traffic in the Athens Metropolitan Area, show the applicability of the approach.