ANALYTiC: An Active Learning System for Trajectory Classification

Soares, Amílcar; Renso, Chiara; Matwin, Stan

doi:10.1109/mcg.2017.3621221

Cited by 32 publications

(18 citation statements)

References 21 publications

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“…Azure and Redi Galić [43] 2016 D Flink Soares et al [45] 2017 D Solr Zhang et al [46] 2017 D Spark Alarabi et al [47] 2018 D Hadoop+HDFS Dividino et al [48] 2018 D Apache Jena Nikitopoulos et al [49] 2018 D Spark+Redis Soares et al [50] 2019 D MongoDB Mello et al [33] 2019 D Rendezvous Table 2 shows some systems that use spatial databases such as PostgreSQL, together with the spatial expansion Postgis, and Oracle. More recent works have adopted Big Data technologies, as this is the new trend because of the large volume of trajectory data that is produced by sensors and social media.…”

Section: Dmentioning

confidence: 99%

“…The system named VISTA [50] presents a tool with visual analytics functionalities that support the users: (i) in exploring and processing trajectory data; and (ii) in creating features and semantic information, to guide the user to comprehend how to label trajectories properly. Another system that also assigns trajectory annotations is ANALYTiC [45], which uses machine-learning algorithms to infer semantic annotations about trajectory data. In that article, a semantic annotation, or label, is any contextual information related to the trajectory, for example: activity information such as walking, studying, driving, or fishing.…”

Section: Semantic Trajectoriesmentioning

confidence: 99%

“…Some systems only adopt a label for trajectory [45], and others allow annotation for each part of the trajectory: point, segment and entire trajectory. The column Semantic Annotation from Table 3 highlights the semantic annotation allowed for each part of the trajectory.…”

Section: Semantic Trajectoriesmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Big Data for Trajectory Analytics

Almeida

Baptista

Andrade

et al. 2020

IJGI

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Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.

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Section: Dmentioning

confidence: 99%

Section: Semantic Trajectoriesmentioning

confidence: 99%

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A Survey on Big Data for Trajectory Analytics

Almeida

Baptista

Andrade

et al. 2020

IJGI

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“…Motivated by the abundance of AIS data and the rich information that they contain, a lot of data mining applications have been developed by researchers on top of historic AIS data, ranging from the automatic detection of events [1,2] to the prediction of future vessel position [3] and estimation of the time of arrival at the port [4]. In a parallel line of research, several works on trajectory data analytics have contributed platforms for the visualisation of vessel trajectories [5,6], efficient handling of large volumes of such spatio-temporal data streams [7,8] and methodologies for the abstraction of AIS data collected for a period and a region [9].…”

Section: Introductionmentioning

confidence: 99%

A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification

et al. 2019

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The problem of unmanned supervision of maritime areas has attracted the interest of researchers for the last few years, mainly thanks to the advances in vessel monitoring that the Automatic Identification System (AIS) has brought. Several frameworks and algorithms have been proposed for the management of vessel trajectory data, which focus on data compression, data clustering, classification and visualization, offering a wide variety of solutions from vessel monitoring to automatic detection of complex events. This work builds on our previous work in the topic of automatic detection of Search and Rescue (SAR) missions, by developing and evaluating a methodology for classifying the trajectories of vessels that possibly participate in such missions. The proposed solution takes advantage of a synthetic trajectory generator and a classifier that combines a genetic algorithm (GENDIS) for the extraction of informative shapelets from training data and a transformation to the shapelets’ feature space. Using the generator and several SAR patterns that are formally described in naval operations bibliography, it generates a synthetic dataset that is used to train the classifier. Evaluation on both synthetic and real data has very promising results and helped us to identify vessel SAR maneuvers without putting any effort into manual annotation.

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“…Such approach offers a balance between methods which are entirely supervised, where the user precisely defines the splitting criteria, and unsupervised, where the method infers a good splitting based on a cost function. We observe that when the segmentation is semantic-based (e.g., representing the activity of the moving object), in contrast to the geometric-based segmentation (e.g., the speed of the object), the need for manually annotated trajectories is crucial: minimizing the number of these humanlabeled trajectories, as stated in [8], is fundamental to keep this task feasible.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Approach for the Semantic Segmentation of Trajectories

Soares

Times

Renso

et al. 2018

2018 19th IEEE International Conference on Mobile Data Management (MDM)

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A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajectories into homogeneous segments based on some criteria. Although trajectory segmentation has been the object of several approaches in the last decade, a proposal based on a semi-supervised approach remains inexistent. A semi-supervised approach means that a user labels manually a small set of trajectories with meaningful segments and, from this set, the method infers in an unsupervised way the segments of the remaining trajectories. The main advantage of this method compared to pure supervised ones is that it reduces the human effort to label the number of trajectories. In this work, we propose the use of the Minimum Description Length (MDL) principle to measure homogeneity inside segments. We also introduce the Reactive Greedy Randomized Adaptive Search Procedure for semantic Semisupervised Trajectory Segmentation (RGRASP-SemTS) algorithm that segments trajectories by combining a limited user labeling phase with a low number of input parameters and no predefined segmenting criteria. The approach and the algorithm are presented in detail throughout the paper, and the experiments are carried out on two real-world datasets. The evaluation tests prove how our approach outperforms state-of-the-art competitors when compared to ground truth.

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ANALYTiC: An Active Learning System for Trajectory Classification

Cited by 32 publications

References 21 publications

A Survey on Big Data for Trajectory Analytics

A Survey on Big Data for Trajectory Analytics

A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification

A Semi-Supervised Approach for the Semantic Segmentation of Trajectories

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