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

Maintaining consistent results of continuous queries under diverse window specifications

Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolvingd -yet restricted- set of tuples and thus provide timely and incremental results. Although sliding windows get frequently employed in many user requests, additional types like partitioned or landmark windows are also available in stream processing engine

Prioritized evaluation of continuous moving queries over streaming locations

Existing approaches to the management of streaming positional updates generally assume that all active user requests have equal importance, ignoring the possibility of any priorities concerning delivery of results in mission-critical mobile applications. Query prioritization could be assigned either explicitly after users' preferences or implicitly by the processing engine itself to better regulate system load. In this work, we specifically examine priority-based evaluation of ranked continuous range queries against locations of moving objects streaming into a central processor. We define a versatile model with alternative scoring functions for deciding evaluation strategies adaptable to the relative importance of queries and the current distribution of objects. We also propose a processing mechanism enhanced with ranked priorities, which exploits shared computation and enables critical requests to receive response more frequently than less demanding ones. P, comprehensive experimental study with performance results offers concrete evidence that such a scheme is capable of efficiently handling numerous moving queries of varying priorities and spatial extents with minimal system overhead

Monitoring orientation of moving objects around focal points

We consider a setting with numerous location-aware moving objects that communicate with a central server. Assuming a set of focal points of interest, we aim at continuously monitoring object orientations and hence detect situations where many objects get closer to or move away from any such site. Towards this goal; we propose a streaming approach that delegates part of the processing to objects, which relay positional updates upon significant deviations at their course. The central processor maintains the changing distribution of current object headings around each focal point and may issue alerts once it observes many objects moving along a direction (e.g., increased northbound traffic near the stadium). To efficiently answer such navigational queries, we introduce a novel access method that indexes object headings influencing a specific site. Furthermore, we extent this scheme to examine trajectory movements around sites over the recent past. Experimental results verify that this framework is able to cope with scalable numbers of objects at reduced communication cost, while offering instant notification of important trends along diverse directions for multiple focal points

Managing trajectories of moving objects as data streams

The advent of modern monitoring applications, such as location-based services, presents several new challenges when dealing with continuously evolving spatiotemporal information. Frequent updates in the positions of moving objects, unexpected fluctuations in data volume and the requirement for real-time responses to continuous spatiotemporal queries indicate the limitations of traditional database systems. We attempt to model management of moving objects with the underlying assumption that their trajectories are essentially continuous, time- varying and possibly unbounded data streams. We propose a basic framework for managing trajectory streams, and suggest the introduction of enhanced constructs for advanced query capabilities. Our first experience with querying moving objects in two data stream prototype systems is promising for the feasibility and extensibility of this approach

Multiplexing trajectories of moving objects

Continuously tracking mobility of humans, vehicles or merchandise not only provides streaming, real-time information about their current whereabouts, but can also progressively assemble historical traces, i.e., their evolving trajectories. In this paper, we outline a framework for online detection of groups of moving objects with approximately similar routes over the recent past. Further, we propose an encoding scheme for synthesizing an indicative trajectory that collectively represents movement features pertaining to objects in the same group. Preliminary experimentation with this multiplexing scheme shows encouraging results in terms of both maintenance cost and compression accuracy

Managing big trajectory data: Online processing of positional streams

As smartphones and GPS-enabled devices proliferate, location-based services become all the more important in social networking, mobile applications, advertising, traffic monitoring, and many other domains. Managing the locations and trajectories of numerous people, vehicles, vessels, commodities, and so forth must be efficient and robust, since this information must be processed online and should provide answers to users' requests in real time. In this geostreaming context, such long-running con¬tinuous queries must be repeatedly evaluated against the most recent positions relayed by moving objects, for instance, reporting which people are now moving in a specific area or finding friends closest to the current location of a mobile user. In essence, modern processing engines must cope with huge amounts of streaming, transient, uncertain, and heterogeneous spatiotemporal data, which can be characterized as big trajectory data. In this chapter, we examine Big Data processing techniques over frequently updated locations and trajectories of moving objects. Rapidly evolving tra¬jectory data pose several research challenges with regard to their acquisition, storage, indexing, analysis, discovery, and interpretation in order to be really useful for intel-ligent, cost-effective decision making. Indeed, the Big Data issues regarding volume, velocity, variety, and veracity also arise in this case. Thus, we foster a close synergy between the established stream processing paradigm and spatiotemporal properties inherent in motion features. Taking advantage of the spatial locality and tempo¬ral timeliness that characterize each trajectory, we present methods and heuristics from our recent research results that address such problems. We highlight certain aspects of big trajectory data management through several case studies. Regarding volume, we suggest single-pass algorithms that can summarize each object's course into succinct, reliable representations. To cope with velocity, an amnesic trajectory approximation structure may offer fast, multiresolution synopses by dropping details from obsolete segments. Detection of objects that travel together can lead to trajec¬tory multiplexing, hence reducing the variety inherent in raw positional data. As for veracity, we discuss a probabilistic method for continuous range monitoring against user locations with varying degrees of uncertainty, due to privacy concerns in geo¬social networking. Last, but not least, as we are heading toward a next-generation framework in trajectory data management, we point out interesting open issues that may provide rich opportunities for innovative research and applications