Visual analytics of flight trajectories for uncovering decision making strategies

In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences

United we stand: improving sentiment analysis by joining machine learning and rule based methods

In the past, we have succesfully used machine learning approaches for sentiment analysis. In the course of those experiments, we observed that our machine learning method, although able to cope well with figurative language could not always reach a certain decision about the polarity orientation of sentences, yielding erroneous evaluations. We support the conjecture that these cases bearing mild figurativeness could be better handled by a rule-based system. These two systems, acting complementarily, could bridge the gap between machine learning and rule-based approaches. Experimental results using the corpus of the Affective Text Task of SemEval ’07, provide evidence in favor of this direction. 1

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

Coping with floods: impacts, preparedness and resilience capacity of Greek micro-, small- and medium-sized enterprises in flood-affected areas

Purpose: This paper aims to investigate aspects of flood experience, attitudes and responses of micro-, small- and medium-sized enterprises (MSMEs) in Greece and to indicate a typology of strategies associated with their relative effort to build flood resilience capacity. Design/methodology/approach: A qualitative study protocol was used, based on pertinent literature that considers how business entities withstand, adapt and/or recover from non-linear climate change impacts, natural hazards and extreme weather. Data was obtained by conducting semi-structured interviews with 82 MSMEs’ owners-managers who had recently experienced flooding. Findings: The study reports limited activities of MSMEs towards flood resilience capacity despite the threat of relevant disasters. Findings suggest that most owners-managers of these enterprises are not adequately preparing their businesses for the impacts of flooding. Research limitations/implications: The findings call for multi-level and dynamic perspectives to be examined in assessing MSME resilience capacity to floods. It is attitudinal, managerial, organisational, behavioural and regulatory (as well as other institutional) factors that merit further investigation. Such an investigation would allow a better understanding as to whether these factors hinder or enable conditions for microeconomic flood preparedness and resilience as well as how they may interact with each other or create feedback loops. Practical implications: The study carries managerial implications and policy recommendations in terms of nurturing opportunities towards awareness-raising campaigns for reducing deficits in managerial knowledge and competencies. It also encapsulates practical implications in terms of emphasising supporting mechanisms from key institutional stakeholders to allow MSMEs scan available options they have in effectively reinforcing the business premises from the forces of rising waters. Originality/value: Most of the related studies have examined flood impacts, responses and/or resilience capacity at the household- or community-level. Empirical work that is conducted to ascertain how MSMEs cope with flooding remains thin on the ground. In response to this, the current study and the typology of MSMEs’ strategic postures that are suggested seek to contribute to this under-researched topic

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

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

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