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CRISIS: Integrating AIS and Ocean Data Streams Using Semantic Web Standards for Event Detection
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Abstract
Information deluge is still an issue in the maritime environment, creating situations where data are sometimes underutilized or in more extreme cases, not utilized, in the decision-making process. In part, this is due to the high volume of incoming data that are available to the operational community. However, better exploitation of these data streams can be accomplished through techniques that focus on the semantics of the incoming stream, to discover information-based alerts that generate knowledge that is only obtainable when considering the totality of the streams. In this paper, we present an agile data architecture for real-time data representation, integration, and querying situations over heterogeneous data streams using Semantic Web Technologies, with the goal of improved knowledge interoperability. We apply the framework to the maritime ship traffic domain to discover real-time traffic alerts by querying and reasoning across multiple streams.
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Most of the trajectory datasets only record the spatio-temporal position of the moving object, thus lacking semantics and this is due to the fact that this information mainly depends on the domain expert labeling, a time-consuming and complex process. This paper is a contribution in facilitating and supporting the manual annotation of trajectory data thanks to a visual-analytics-based platform named VISTA. VISTA is designed to assist the user in the trajectory annotation process in a multi-role user environment. A session manager creates a tagging session selecting the trajectory data and the semantic contextual information. The VISTA platform also supports the creation of several features that will assist the tagging users in identifying the trajectory segments that will be annotated. A distinctive feature of VISTA is the visual analytics functionalities that support the users in exploring and processing the trajectory data, the associated features and the semantic information for a proper comprehension of how to properly label trajectories.
Information deluge is a continual issue in today's military environment, creating situations where data is sometimes underutilized or in more extreme cases, not utilized, for the decision-making process. In part, this is due to the continuous volume of incoming data that presently engulf the ashore and afloat operational community. However, better exploitation of these data streams can be realized through information science techniques that focus on the semantics of the incoming stream, to discover information-based alerts that generate knowledge that is only obtainable when considering the totality of the streams. In this paper, we present an agile data architecture for real-time data representation, integration, and querying over a multitude of data streams. These streams, which originate from heterogeneous and spatially distributed sensors from different IoT infrastructures and the public Web, are processed in real-time through the application of Semantic Web Technologies. The approach improves knowledge interoperability, and we apply the framework to the maritime vessel traffic domain to discover real-time traffic alerts by querying and reasoning across the numerous streams. The paper and the provided video demonstrate that the use of standards-based semantic technologies is an effective tool for the maritime big data integration and fusion tasks.
A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajecto-ries 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 trajecto-ries. 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 Semi-supervised 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.
The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.
The correlated exploitation of multiple, disparate, heterogeneous data sources offering voluminous archival and streaming data is important to increasing the accuracy of computations concerning the current and future states of moving entities, towards accurate long-term predictions. This is critical to the maritime domain, where real-time tracking of trajectories and early recognition of events related to vessels, together with accurate forecasts of trajectories and events are crucial to safety, cost, dependability, credibility and environmental-friendliness of 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 moving objects at sea as well as a few preliminary suggestions for a successful development of maritime decision-support and forecasting
Processing data streams is increasingly gaining momentum, given the need to process these flows of information in real-time and at Web scale. In this context, RDF Stream Processing (RSP) and Stream Reasoning (SR) have emerged as solutions to combine semantic technologies with stream and event processing techniques. Research in these areas has proposed an ecosystem of solutions to query, reason and perform real-time processing over heterogeneous and distributed data streams on the Web. However, so far one basic building block has been missing: a mechanism to disseminate and exchange RDF streams on the Web. In this work we close this gap, proposing TripleWave, a reusable and generic tool that enables the publication of RDF streams on the Web. The features of TripleWave were selected based on requirements of real use-cases, and support a diverse set of scenarios, independent of any specific RSP implementation. TripleWave can be fed with existing Web streams (e.g. Twitter and Wikipedia streams) or time-annotated RDF datasets (e.g. the Linked Sensor Data dataset). It can be invoked through both pull- and push-based mechanisms, thus enabling RSP engines to automatically register and receive data from TripleWave.
As part of the overarching research goal to assess current and potential maritime information sources for use in maritime defence and security in the Canadian north, we examine whether wide-area surveillance data, as represented by Space-based Automatic Identification System (S-AIS) data, offers sufficient information for surveillance requirements in the Canadian north. If S-AIS data are not sufficient, we address how the additional information provided by Long-Range Identification and Tracking (LRIT) can be used to meet the surveillance requirements. A Systems Tool Kit (STK) simulation scenario is constructed that includes five exactEarth satellites collecting AIS data. Simulated AIS transmitters are positioned at 20 northern Canada ground locations. The results indicate that for each location, two thirds of the eight-day simulation is spent without a satellite within range, when using the five satellites. As the number of satellites decreases, intervals in the range of 80 to 105 minutes, during which there are no AIS messages received, increase in frequency. If the end-user requires vessel location information more often than S-AIS consistently provides, augmenting the S-AIS information with LRIT polling should achieve the desired vessel traffic awareness.
A major challenge in trajectory data analysis is the definition of approaches to enrich it semantically. In this paper, we consider machine learning and context information to enrich trajectory data in three steps: (1) the definition of a context model for trajectory domain; (2) the generation of rules based on that context model; (3) the implementation of a classification algorithm that processes these rules and adds semantics to trajectories. This approach is hierarchical and combines clustering and classification tasks to identify important parts of trajectories and to annotate them with semantics. These ideas were integrated into Weka toolkit and experimented using fishing vessel’s trajectories.
This article defines C-SPARQL, an extension of SPARQL whose distinguishing feature is the support of continuous queries, i.e. queries registered over RDF data streams and then continuously executed. Queries consider windows, i.e. the most recent triples of such streams, observed while data is continuously flowing. Supporting streams in RDF format guarantees interoperability and opens up important applications, in which reasoners can deal with evolving knowledge over time.
C-SPARQL is presented by means of a full specification of the syntax, a formal semantics, and a comprehensive set of examples, relative to urban computing applications, that systematically cover the SPARQL extensions. The expression of meaningful queries over streaming data is strictly connected to the availability of aggregation primitives, thus C-SPARQL also includes extensions in this respect.
Focus on movement data has increased as a consequence of the larger availability of such data due to current GPS, GSM, RFID, and sensors techniques. In parallel, interest in movement has shifted from raw movement data analysis to more application-oriented ways of analyzing segments of movement suitable for the specific purposes of the application. This trend has promoted semantically rich trajectories, rather than raw movement, as the core object of interest in mobility studies. This survey provides the definitions of the basic concepts about mobility data, an analysis of the issues in mobility data management, and a survey of the approaches and techniques for: (i) constructing trajectories from movement tracks, (ii) enriching trajectories with semantic information to enable the desired interpretations of movements, and (iii) using data mining to analyze semantic trajectories and extract knowledge about their characteristics, in particular the behavioral patterns of the moving objects. Last but not least, the article surveys the new privacy issues that arise due to the semantic aspects of trajectories.
Although originally conceived as a conceptual object for modelling knowledge, current ontologies do not make it possible to manipulate spatial knowledge. However, spatial knowledge is an essential component of any modelling specification. This problem provided the motivation for the creation of an expert system driven by an ontology. The system enables experts in the maritime domain to characterise abnormal ship behaviour based on formal semantic properties. Users are able to specify and execute spatial rules that are directly integrated into the ontology and a map interface linked to the ontology displays the results of the inferences obtained.
The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL~2 ontology to describe sensors and observations --- the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.
On a grand scale, visual analytics solutions provide technology that combines the strengths of human and electronic data processing. Visualization becomes the medium of a semi-automated analytical process, where humans and machines cooperate using their respective distinct capabilities for the most effective results. The diversity of these tasks can not be tackled with a single theory. Visual analytics research is highly interdisciplinary and combines various related research areas such as visualization, data mining, data management, data fusion, statistics and cognition science (among others).
The surveillance of large sea areas often generates huge amounts of multidimensional data. Exploring, analyzing and finding anomalous behavior within this data is a complex task. Confident decisions upon the abnormality of a particular vessel behavior require a certain level of situation awareness that may be difficult to achieve when the operator is overloaded by the available information. Based on a visual analytics process model, we present a novel system that supports the acquisition of situation awareness and the involvement of the user in the anomaly detection process using two layers of interactive visualizations. The system uses an interactive data mining module that supports the insertion of the user's knowledge and experience in the creation, validation and continuous update of the normal model of the environment.
An ever-increasing number of applications in critical domains, such as maritime and aviation, generate, collect, manage and process spatio-temporal data related to the mobility of entities. This wealth of data can be exploited for various purposes, towards improving the safety of operations, reducing economical costs, and increasing dependability: The major issue to achieve these objectives is increasing predictability of moving objects’ trajectories and events. To achieve this purpose in a data-driven way we need to exploit in integrated manners data from a variety of disparate and heterogeneous data sources, both streaming and archival, regarding – among other – surveillance, weather, and contextual data. Motivated by this fact, in this paper, we propose a framework for semantic integration of big mobility data with other data sources that are necessary to data analytics tasks, providing a unified representation of such data. Notable features of our framework include the real-time generation of data synopses of moving entities’ trajectories, the efficient and flexible transformation of data from heterogeneous and big data sources in RDF, and the spatio-temporal link discovery between spatio-temporal entities in diverse data sources. The design and implementation of our framework uses big data technologies (Apache Flink and Kafka), and our experimental evaluation demonstrates the efficiency and scalability of the proposed framework using large, real-life datasets.
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 with related events and contextual information. The main contribution of the proposed ontology is the representation of semantic trajectories at different levels of spatio-temporal analysis.
In August 2015, a new seafloor observatory was deployed in Galway Bay, Ireland. The sensors on the observatory platform are connected by fibre-optic cable to a shore station, where a broadband connection allows data transfer to the Marine Institute's data centre. This setup involved the development of a new data acquisition system which takes advantage of open source streaming data solutions developed in response to the Big Data paradigm, in particular the Velocity aspect. This activity merges concepts from the arenas of both Big Data and Internet of Things where data standardisation is not normally considered. This paper considers the architecture implemented to stream marine data from instrument to end user and offers suggestions on how to standardise these data streams.
The “Rio+20” United Nations Conference on Sustainable Development (UNCSD) focused on the "Green economy" as the main concept to fight poverty and achieve a sustainable way to feed the planet. For coastal countries, this concept translates into "Blue economy", the sustainable exploitation of marine environments to fulfill humanity needs for resources, energy, and food. This puts a stress on marine industries to better articulate their processes to gain and share knowledge of different marine habitats, and to reevaluate the data value chains established in the past and to support a data fueled market that is going only to in the near future.
The EXPOSURES project is working in conjunction with the SUNRISE project to establish a new marine information ecosystem and demonstrate how the ‘Internet of Things’ (IoT) can be exploited for marine applications. In particular EXPOSURES engaged with the community of stakeholders in order to identify a new data value chain which includes IoT data providers, data analysts, and harbor authorities. Moreover we integrated the key technological assets that couple OGC standards for raster data management and manipulation and semantic technologies to better manage data assets.
This paper presents the identified data value chain along with the use cases for validating it, and the system developed to semantically reconcile and manage such data collections.
The maritime security domain is challenged by a number of data analysis needs focusing on increasing the maritime situation awareness, i.e., detection and analysis of abnormal vessel behaviors and suspicious vessel movements. The need for efficient processing of dynamic and/or static vessel data that come from different heterogeneous sources is emerged. In this paper we describe how we address the challenge of combining and processing real-time and static data from different sources using ontology-based data access techniques, and we explain how the application of semantic web technologies increases the value of data and improves the processing workflow in the maritime domain.
The availability of streaming information is progressively increasing, thanks to the knowledge management technologies such as ontologies that captures knowledge about this information. The semantic web initiative provides standards such as RDF and OWL, the two most powerful Semantic Web knowledge representation languages. The OWL adds semantics to schemas, and allows for more expressivity than RDF. As far as we know, there are existing querying services, designed for an swering queries over streaming data in RDF format, but there is none for the OWL EL ontological stream, in order to address this short comings for OWL EL ontological streams, in this paper, we focus primarily on the problem of querying and reasoning over OWL ontological streams, specifically how to design and evaluate Continuous SPARQL query over OWL EL ontological streams, in which the queries are evaluated under more expressive semantics of OWL. We address semantic issues, by introducing the add and erase semantics, maintaining relevant erasure for special use. We propose a general and flexible architecture, for querying more expressive OWL ontological streams with the help of a stream reasoner in near real time. This is our first step towards the design and implementation of such a system.
In this paper, we present our collaborative work with the U.S. Coast Guard's Ninth District and Atlantic Area Commands where we developed a visual analytics system to analyze historic response operations and assess the potential risks in the maritime environment associated with the hypothetical allocation of Coast Guard resources. The system includes linked views and interactive displays that enable the analysis of trends, patterns and anomalies among the U.S. Coast Guard search and rescue (SAR) operations and their associated sorties. Our system allows users to determine the potential change in risks associated with closing certain stations in terms of response time, potential lives and property lost and provides optimal direction as to the nearest available station. We provide maritime risk assessment tools that allow analysts to explore Coast Guard coverage for SAR operations and identify regions of high risk. The system also enables a thorough assessment of all SAR operations conducted by each Coast Guard station in the Great Lakes region. Our system demonstrates the effectiveness of visual analytics in analyzing risk within the maritime domain and is currently being used by analysts at the Coast Guard Atlantic Area.
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