A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluation

A tensor-based approach for big data representation and dimensionality reduction

PublishedJournal Article© 2013 IEEE. Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction

3DGraCT: A Grammar-Based Compressed Representation of 3D Trajectories

This version of the manuscript has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-00479-8_9[Abstract]: Much research has been published about trajectory management on the ground or at the sea, but compression or indexing of flight trajectories have usually been less explored. However, air traffic management is a challenge because airspace is becoming more and more congested, and large flight data collections must be preserved and exploited for varied purposes. This paper proposes 3DGraCT, a new method for representing these flight trajectories. It extends the GraCT compact data structure to cope with a third dimension (altitude), while retaining its space/time complexities. 3DGraCT improves space requirements of traditional spatio-temporal data structures by two orders of magnitude, being competitive for the considered types of queries, even leading the comparison for a particular one.This work was funded in part by EU H2020 MSCA RISE BIRDS: 690941; MINECO-AEI/FEDER-UE: TIN2016-78011-C4-1-R; MINECO-CDTI/FEDER-UE CIEN IDI-20141259; MINECO-CDTI/FEDER-UE CIEN IDI-20150616; MINECO-CDTI/FEDER-UE INNTERCONECTA ITC-20161074; Xunta de Galicia/FEDER-UE ED431C 2017/58 and ED431G/01.Xunta de Galicia; ED431C 2017/58Xunta de Galicia; ED431G/0

Mobility Data Mining for Rural and Urban Map-Matching

Ajalis-ruumiliste andmete kogumine on hoogustunud erinevates rakendustes ja seadmetes. Globaalne positsiooneerimise süsteem (GPS) on kõige populaarsem viis asukoha teave saamiseks. Kaardipunktide vastavusse seadmine on konseptsioon, mis püüab GPS andmeid trajektooris viia vastavusse reaalse teedevõrguga. GPS andmete suurim probleem tuleneb andmete mõõtmis-ja kogumisvigadest ja nende parandamine on suur väljakutse. Käesoleva lõputöö eesmärk on arendada andmete töötlusvoo ja visualiseerimise raamistik muutmaks GPS punktid loogilisteks trajektoorideks ja vigaste GPS punktide asukohtade parandamiseks. Selle eesmärgi saavutamiseks tutvustatakse uut lähenemist trajektooride mustrite leidmiseks.The functionality of gathering spatio-temporal data has seen increasing usage in various applications and devices. The Global Positioning System (GPS) is a satellite navigation system which is mostly used for gathering location information. Map-matching is the procedure of matching trajectories from a sequence of raw GPS data points to the appropriate road networks. GPS data errors are one of the biggest problems and correcting them is a big challenge. The main goal of this thesis work is to build a data pipeline and visualization framework for turning raw GPS data to trajectories and correcting erroneous GPS points by new map-matching approach. For achieving the goal a new approach for trajectory pattern mining is introduced

Lupa espacio-temporal: Una herramienta para el análisis visual de trayectorias en una bodega de datos

This paper presents a visual tool to facilite the trajectory analysis and the discovery of spatio-temporal patterns in a trajectory data warehouse (TDW). The proposed tool is a spatio-temporal magnifying glass that allows analysts to focus on a specific region, where several trajectories have ocurred and to delect, according to some parameters spsecified by the analyst through a graphical interface, e.g.the closeness relationship between trajectories of between a trajectory and its surrounding sites. In this paper, we propose and formally define derived closeness was the enrichment of a TDW model in order to allow the formulation of more expressive queries and to support the visualization aspect of the proposed tool. Although experiments that are more exhaustive are required, our results evidence some spatio-temporal patterns that demonstrate the convenience and advantages of our tool.En este artículo se presenta una herramienta visual para facilitar el análisis de trayectorias y el descubrimiento de patrones espacio-temporales a partir de una bodega de datos de trayectorias (BDT). La herramienta propuesta, una lupa espacio-temporal, permite que el analista se enfoque en una determinada región donde han ocurrido varias trayectorias y permite detectar, según ciertos parámetros especificados por el analista a través de una interfaz gráfica, p. ej. la relación de cercanía de una trayectoria con otras o con los sitios a su alrededor. En el artículo se proponen y definen formalmente las relaciones de cercanía derivadas entre trayectorias y entre trayectorias y sitios. Una contribución adicional fue el enriquecimiento de un modelo de una BDT con el fin de permitir la formulación de consultas más expresivas y apoyar el aspecto de visualización de la herramienta propuesta. Aunque se requieren experimentos más exhaustivos, los resultados evidenciaron algunos patrones espacio-temporales que demuestran la conveniencia y la utilidad de la herramienta

MOVING OBJECTS MANAGEMENT FOR LOCATION-BASED SERVICES

Ph.DDOCTOR OF PHILOSOPH

Map-based interaction with trajectory data

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2017With the increasing popularity of location based services and mobile tracking technologies, the collection of large amounts of spatio-temporal data became an increasingly common, easier, and more reliable task. In turn, this has emphasized the possibility of analysing georeferenced information, particularly associated with human trajectory data, to identify and understand movement patterns and activities, ultimately, supporting decision making in various contexts. In order to properly analyse and understand the spatio-temporal and the thematic properties associated with these data, adequate visualization techniques are needed. Due to the spatial properties of trajectories, map-based techniques, such as 2D static maps or 3D space-time cubes (STCs) are considered as essential tools for their visualization. However, despite the increasing number of visualization systems, the study regarding their usability, alongside the role of the human user, sometimes with a limited background in data visualization and analysis, are often neglected. In addition to the somewhat disperse, and sometimes even contradictory, results in the literature, these factors, ultimately, emphasize the lack of knowledge to support the choice of particular visualizations, and their design, in different types of tasks. This dissertation addresses these issues through three main sets of contributions, focusing on inexperienced users, in terms of data visualization and analysis: i) the characterization of the dis/advantages of existing map-based techniques (2D static maps and STCs), depending on the types of visual analysis tasks and the focus of the analysis; ii) the improvement of existing visualization techniques, either through the inclusion of additional spatial cues within the STC, or combining both types of techniques in various ways; and iii) the identification of design guidelines for trajectory data visualization, describing various considerations/criteria for the selection of different map-based visualization techniques and their possible interactive features

Trajectory indexing and retrieval

The traveling history of moving objects such as a person, a vehicle, or an animal have been exploited in various applications. The utility of trajectory data depends on the effective and efficient trajectory query processing in trajectory databases. Trajectory queries aim to evaluate spatiotemporal relationships among spatial data objects. In this chapter, we classify trajectory queries into three types, and introduce the various distance measures encountered in trajectory queries. The access methods of trajectories and the basic query processing techniques are presented as another component of this chapter