Increasing maritime situation awareness via trajectory detection, enrichment and recognition of events

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results

Big data analytics for time critical maritime and aerial mobility forecasting

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics

Maritime data integration and analysis: Recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems

The datAcron Ontology for the Specification of Semantic Trajectories

As the number of moving objects increases, the challenges for achieving operational goals w.r.t. the mobility in many domains that are critical to economy and safety emerge dramatically. In domains such as air traffic management, this dictates a shift of operations’ paradigm from location based, as it is today, to trajectory based, where trajectories are turned into “first-class citizens”. Additionally, the increasing amount of data from heterogenous and disparate data sources implies the need for advanced analysis methods that require exploiting spatio-temporal mobility data in appropriate forms and at varying levels of abstraction. All these call for an in-principle way for organising integrated views of mobility data, with trajectories playing the main role. In this paper, we propose an ontology for modelling semantic trajectories, integrating spatio-temporal information regarding mobility of objects, at multiple, interlinked levels of abstraction. Our work builds upon a comprehensive framework that identifies fundamental spatio-temporal data types and specific conversions among these types. We validate the ontological specifications towards satisfying the needs of visual analysis tasks in the complex air traffic management domain, using real-world data

MARITIME DATA INTEGRATION AND ANALYSIS: RECENT PROGRESS AND RESEARCH CHALLENGES

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems. Document type: Articl

Forest Observatory: a resource of integrated wildlife data

We propose the Forest Observatory, a linked datastore, to represent knowledge from wildlife data. It is a resource that semantically integrates data silos and presents them in a unified manner. This research focuses on the forest of the Lower Kinabatangan Wildlife Sanctuary (LKWS) in Sabah, Malaysian Borneo. In this region, wildlife research activities generate a variety of Internet of Things (IoT) data. However, due to the heterogeneity and isolation of such data (i.e., data created in different formats and stored in separate locations), extracting meaningful information is deemed time-consuming and labour-intense. One possible solution would be to integrate these data using semantic web technologies. As a result, data entities are transformed into a machine-readable format and can be accessed on a single display. This study created a semantic data model to integrate heterogeneous wildlife data. Our approach developed the Forest Observatory Ontology (FOO) to lay the foundation for the Forest Observatory. FOO modelled the IoT and wildlife concepts, established their relationships, and used these features to link historical datasets. We evaluated FOO’s structure and the Forest Observatory using pitfalls scanners and task-based methods. For the latter, a use case was assigned to the Forest Observatory, querying it before and after reasoning. The results demonstrated that our Forest Observatory provides precise and prompt responses to complex questions about wildlife. We hope our research will aid bioscientists and wildlife researchers in maximising the value of their digital data. The Forest Observatory can be expanded to include new data sources, replicated in various wildlife sanctuaries, and adapted to other domains

Multiple-Aspect Analysis of Semantic Trajectories

This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification

SFNCS: A Framework for Assessment of Spatio-Temporal Visualization Methods

Movement analysis is complex due to many different factors: different forms of data, different levels of precision, strongly influenced by context, for which diverse sets of tasks require different visualizations and algorithmic approaches. There is a vast scope of previous work that researches, for diverse tasks, several approaches to visualization designs and data processing methods. The scope of tasks, potential visualization methods, and data processing that is yet to research is vast. To help reach a higher precision when describing contributions of researchers, we define a framework that characterizes information, from its recording into data to the way it is presented to the user and the terms used to communicate about it for evaluations. Within this thesis, we explain how our original research scope directed us from establishing the current state of the art for visualization methods for movement analysis while accounting for context into a characterization of visualizations, data processing methods, and communication approaches. This results in the framework that is the main contribution of our thesis. This thesis also presents several studies that refine our understanding of the impact of data complexity over diverse tasks, using precise terms. We also discuss how our system can be used to set up and analyze studies based on vague terms. Furthermore, we discuss the strength and weaknesses of existing designs for exploration tasks of contextually rich data movement, and potential design approaches to investigate in future work. These discussions include the tasks for which the designs could be most useful and how they fit within different characterizations of information and data

Specification of Semantic Trajectories Supporting Data Transformations for Analytics: The datAcron Ontology

Motivated by real-life emerging needs in critical domains, this paper proposes a coherent and generic ontology for the representation of semantic trajectories, in association to related events and contextual information, to support analytics. The main contribution of the proposed ontology is twofold: (a) The representation of semantic trajectories at varying, interlinked levels of spatio-Temporal analysis, (b) enabling data transformations that can support analytics tasks. The paper presents the ontology in detail, in connection to other well-known ontologies, and demonstrates how data is represented at varying levels of analysis, enabling the required data transformations. The benefits of the representation are shown in the context of supporting visual analytics tasks in the air-Traffic management domain