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Semantic Integration of Real-Time Heterogeneous Data Streams for Ocean-related Decision Making
Abstract and Figures
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.
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... Therefore, IF-1 influences interoperability by treating the interconnectivity layer of the systems through the interfaces that enable the use of services (Agostinho et al. 2011;Bicocchi et al. 2018;Billaud et al. 2015;Camara et al. 2010;Chituc et al. 2009;Cuenca et al. 2015;Diallo et al. 2010;Guédria et al. 2015;Jardim-Goncalves et al. 2010;Klischewski et al. 2011;Navigli and Velardi 2005;Ostadzadeh et al. 2015;Touzi et al. 2008;Whitman and Panetto 2006;Yaacoubi et al. 2006). Mordecai et al. (2016), Vargas et al. (2018), Weichhart et al. (2016), Chapurlat and Daclin (2012), Song et al. (2011), Neaga and Henshaw (2010), Mantzana and Koumaditis (2010), Naudet et al. (2010), Chituc et al. (2009), Mills andRuston (1990) include metadata information (e.g., structural, syntactic, and semantic data formats) that facilitate the integration of information around the heterogeneity of data models (Chituc et al. 2009;Cornu et al. 2012;Diallo et al. 2010;Kalatzis et al. 2019;Kazemzadeh et al. 2010;Wang et al. 2010), ontologies (Dividino et al. 2018;Lima et al. 2022;Mazzetti et al. 2022;Zarour et al. 2011), meta-model (Cornu et al. 2012), and frameworks (Anderson and Boxer 2008;Mazzetti et al. 2022). However, for this information supply to be carried out with better precision, some studies defend the need for technical functions and procedures by which the responsible professionals can benefit (Agostinho and Jardim-Goncalves 2009;Billaud et al. 2015;Chapurlat and Daclin 2012;Chituc et al. 2009;Mordecai et al. 2016;Muller 2009;Soares and Amaral 2014;Zeinali et al. 2016). ...
... Thus, the goal of linking the data managed by the constituent IS is to provide comprehensive data summaries and visualisations that can support technicians, professionals, managers, politicians, etc., in their decision-making process. Thus, formal representation of knowledge, as ontologies have been developed to provide technical information of the IT elements involved (IF-2) that support decisional models (Agostinho and Jardim-Goncalves 2009;Dividino et al. 2018;Jardim-Goncalves et al. 2010;Kalatzis et al. 2019;Kazemzadeh et al. 2010;Lima et al. 2022;Mazzetti et al. 2022;Rosener et al. 2004;Weichhart et al. 2016;Zarour et al. 2011). In Weichhart et al. (2016) and Zarour et al. (2011), authors propose approaches based on agents, an ontology of concepts and relations allowing organizational interoperability, and a P2P-based architecture to allow the interoperability among distributed heterogeneous systems, and the sharing of information towards decision making. ...
... In Dividino et al. (2018), it is defined an ontology for ocean-related decisionmaking in military environments, in which data comes in the form of continuous streams from multiple IS; this ontological data architecture is used for real-time data representation, integration, and querying over a multitude of heterogeneous data streams and visualisations. Authors in Kalatzis et al. (2019) treat the semantic interoperability for IoT platforms in support of decision making, by providing a solution towards the support of uniform data exchange and adapts, extends data model, API standards enabling, and the use of IoT data analytics and intelligent decision making. ...
The original version of this chapter was inadvertently published with the incorrect spelling of the author as Rodrigo Pereirados Santos in the online version of this book. This has now been corrected as Dr. Rodrigo Pereira dos Santos.
... Therefore, IF-1 influences interoperability by treating the interconnectivity layer of the systems through the interfaces that enable the use of services (Agostinho et al. 2011;Bicocchi et al. 2018;Billaud et al. 2015;Camara et al. 2010;Chituc et al. 2009;Cuenca et al. 2015;Diallo et al. 2010;Guédria et al. 2015;Jardim-Goncalves et al. 2010;Klischewski et al. 2011;Navigli and Velardi 2005;Ostadzadeh et al. 2015;Touzi et al. 2008;Whitman and Panetto 2006;Yaacoubi et al. 2006). Mordecai et al. (2016), Vargas et al. (2018), Weichhart et al. (2016), Chapurlat and Daclin (2012), Song et al. (2011), Neaga and Henshaw (2010), Mantzana and Koumaditis (2010), Naudet et al. (2010), Chituc et al. (2009), Mills andRuston (1990) include metadata information (e.g., structural, syntactic, and semantic data formats) that facilitate the integration of information around the heterogeneity of data models (Chituc et al. 2009;Cornu et al. 2012;Diallo et al. 2010;Kalatzis et al. 2019;Kazemzadeh et al. 2010;Wang et al. 2010), ontologies (Dividino et al. 2018;Lima et al. 2022;Mazzetti et al. 2022;Zarour et al. 2011), meta-model (Cornu et al. 2012), and frameworks (Anderson and Boxer 2008;Mazzetti et al. 2022). However, for this information supply to be carried out with better precision, some studies defend the need for technical functions and procedures by which the responsible professionals can benefit (Agostinho and Jardim-Goncalves 2009;Billaud et al. 2015;Chapurlat and Daclin 2012;Chituc et al. 2009;Mordecai et al. 2016;Muller 2009;Soares and Amaral 2014;Zeinali et al. 2016). ...
... Thus, the goal of linking the data managed by the constituent IS is to provide comprehensive data summaries and visualisations that can support technicians, professionals, managers, politicians, etc., in their decision-making process. Thus, formal representation of knowledge, as ontologies have been developed to provide technical information of the IT elements involved (IF-2) that support decisional models (Agostinho and Jardim-Goncalves 2009;Dividino et al. 2018;Jardim-Goncalves et al. 2010;Kalatzis et al. 2019;Kazemzadeh et al. 2010;Lima et al. 2022;Mazzetti et al. 2022;Rosener et al. 2004;Weichhart et al. 2016;Zarour et al. 2011). In Weichhart et al. (2016) and Zarour et al. (2011), authors propose approaches based on agents, an ontology of concepts and relations allowing organizational interoperability, and a P2P-based architecture to allow the interoperability among distributed heterogeneous systems, and the sharing of information towards decision making. ...
... In Dividino et al. (2018), it is defined an ontology for ocean-related decisionmaking in military environments, in which data comes in the form of continuous streams from multiple IS; this ontological data architecture is used for real-time data representation, integration, and querying over a multitude of heterogeneous data streams and visualisations. Authors in Kalatzis et al. (2019) treat the semantic interoperability for IoT platforms in support of decision making, by providing a solution towards the support of uniform data exchange and adapts, extends data model, API standards enabling, and the use of IoT data analytics and intelligent decision making. ...
Pervasive Information Systems (PIS) can be seen as Information Systems (IS) deployed everywhere, going beyond the traditional frontiers of organizations. In this context, they can be considered as Systems-of-Information Systems (SoIS), which are an emerging classification of arrangements of managerial and operationally independent IS. Despite the evident importance and recurrent need for interoperability among IS, the management of interoperability links and their adjustment at a suitable level is still challenging, particularly considering the independence of IS. Given that context, we aim to bring the IS community the discussion about the importance of technical, human, and organizational factors beyond just integration among systems, around interoperability in the domain of PIS, seen as SoIS, to support their decision-making processes. We present these factors as potential issues to explain how practices around interoperability need a synergy of efforts beyond technical decisions and propose some guidelines for the design of interoperability links in PIS, seen as SoIS. We report results of a deep study about factors that potentially influence the establishment of interoperability links among IS to form PIS, seen as SoIS, and support their decision-making processes.
... In terms of information deluge in recent years, [24] presented an agile data architecture (CRISIS) for real-time data representation of multi-source heterogeneous ocean data streams with semantic web technologies in 2018. Later, [25] presented an reorganized and enhanced version of [24], including an isolation of functionalities to utilize multi-source querying and the discovery of alarms. ...
... In terms of information deluge in recent years, [24] presented an agile data architecture (CRISIS) for real-time data representation of multi-source heterogeneous ocean data streams with semantic web technologies in 2018. Later, [25] presented an reorganized and enhanced version of [24], including an isolation of functionalities to utilize multi-source querying and the discovery of alarms. Wang et al. [26] designed a formalized geographic knowledge representation (GeoKG) that describes the evolution of spatiotemporal data. ...
... In this survey, we systematically illustrate the processing methods on ocean spatiotemporal data. We discuss about data representation Semantic primitive invention Situated data management Janowicz et al. [16] Semantic extension SDI CRISIS [24,25] Semantic web tech Knowledge interoperability GeoKG [26] Ontology design Representation OEDO [26] Ontology design Representation, querying Bittner [34] Axiomatic formalisation Geo-entity relations YAGO2 [36] Ontology extension Representation, integration Timely YAGO [38] Knowledge extraction Representation, querying Vasseur et al. [39] Ontology design Integration Hornsby et al. [41] Semantic primitive Representation, reasoning Kurte et al. [40] Ontology design Disaster monitoring Neumaier [42] Entity extension Representation, querying Grenon [43] Ontology design Representation, reasoning Carstensen [44] Ontology design, selective attention Representation Kauppinen et al. [45] Ontology design Semantic ambiguation SWETO [47] Ontology design Entity extraction Jayawardhana et al. [49] Semantic mapping Entity extraction Rule-based [51,52] Supervise learning Relation extraction Kernel-based [53] Supervise learning Relation extraction Chen et al. [54] Semi-supervised learning Relation extraction Lu et al. [55] Unsupervised learning Relation extraction Zhou et al. [57] Data cleaning Knowledge fusion Raffaeta et al. [59] Ontology design Knowledge computing Batsakis [61] Ontology design Knowledge computing methods, design and construction of ocean knowledge graphs. Main methods on spatiotemporal data representation and knowledge graph construction are summarized in the table below (Table 1). ...
Ocean data exhibits interesting yet human critical features affecting all creatures around the world. Studies on Hydrology and Oceanology become the root of many disciplines, including global resource management, macro economy, environment protection, climate predictions, etc, which motivates our further exploration on the underlying feature behind the ocean data. However, with high dimensionality, large quantities, heterogeneous sources, and especially, the spatiotemporal manner, the diversity between the specific knowledge required and massive data chunk puts forward unique challenges in data representation and knowledge mining, effectively. This paper tends to provide a summary of studies on these issues, including the data representation, data processing, knowledge discovery, and algorithms on finding unique patterns on ocean environment changes, such as temperature, tide height, waves, salinity, etc. In detail, we comprehensively discuss about ocean spatiotemporal data processing techniques. We further summarize related representation works on ocean spatiotemporal data, the construction of a ocean knowledge graph, and the management of ocean spatiotemporal data. At last, we combine and compare the collection of the evolution and multiple state-of-the-arts on ocean spatiotemporal data processing.
... In terms of information deluge in recent years, [24] presented an agile data architecture (CRISIS) for real-time data representation of multi-source heterogeneous ocean data streams with semantic web technologies in 2018. Later, [25] presented an reorganized and enhanced version of [24], including an isolation of functionalities to utilize multi-source querying and the discovery of alarms. ...
... In terms of information deluge in recent years, [24] presented an agile data architecture (CRISIS) for real-time data representation of multi-source heterogeneous ocean data streams with semantic web technologies in 2018. Later, [25] presented an reorganized and enhanced version of [24], including an isolation of functionalities to utilize multi-source querying and the discovery of alarms. Wang et al. [26] designed a formalized geographic knowledge representation (GeoKG) that describes the evolution of spatiotemporal data. ...
... Method & Type Contributes in SWEET [21] Ontology extension Representation, querying SEW [22] Semantic primitive invention Situated data management Janowicz et al. [16] Semantic extension SDI CRISIS [24,25] Semantic web tech Knowledge interoperability GeoKG [26] Ontology design Representation OEDO [26] Ontology design Representation, querying Bittner [34] Axiomatic formalisation Geo-entity relations YAGO2 [36] Ontology extension Representation, integration Timely YAGO [38] Knowledge extraction Representation, querying Vasseur et al. [39] Ontology design Integration Hornsby et al. [41] Semantic primitive Representation, reasoning Kurte et al. [40] Ontology design Disaster monitoring Neumaier [42] Entity extension Representation, querying Grenon [43] Ontology design Representation, reasoning Carstensen [44] Ontology design, selective attention Representation Kauppinen et al. [45] Ontology design Semantic ambiguation SWETO [47] Ontology design Entity extraction Jayawardhana et al. [49] Semantic mapping Entity extraction Rule-based [51,52] Supervise learning Relation extraction Kernel-based [53] Supervise learning Relation extraction Chen et al. [54] Semi-supervised learning Relation extraction Lu et al. [55] Unsupervised learning Relation extraction Zhou et al. [57] Data cleaning Knowledge fusion Raffaeta et al. [59] Ontology design Knowledge computing Batsakis [61] Ontology design Knowledge computing We declare that authors have no known competing interests or personal relationships that might be perceived to influence the discussion reported in this paper. ...
Ocean data exhibits interesting yet human critical features affecting all creatures around the world. Studies on Hydrology and Oceanology become the root of many disciplines, including global resource management, macro economy, environment protection, climate predictions, etc, which motivates our further exploration on the underlying feature behind the ocean data. However, with high dimensionality, large quantities, heterogeneous sources, and especially, the spatiotemporal manner, the diversity between the specific knowledge required and massive data chunk puts forward unique challenges in data representation and knowledge mining, effectively. This paper tends to provide a summary of studies on these issues, including the data representation, data processing, knowledge discovery, and algorithms on finding unique patterns on ocean environment changes, such as temperature, tide height, waves, salinity, etc. In detail, we comprehensively discuss about ocean spatiotemporal data processing techniques. We further summarize related representation works on ocean spatiotemporal data, the construction of a ocean knowledge graph, and the management of ocean spatiotemporal data. At last, we combine and compare the collection of the evolution and multiple state-of-the-arts on ocean spatiotemporal data processing.
... Within the scope of their study that was published in [35], Dividino and colleagues presented a framework for the integration of data. This framework was built with the goal of managing the many data streams that are produced by the several types of Internet of Things infrastructure, namely maritime and marine sensors. ...
Complex data systems are incapable of processing large data volumes, rendering the task of retrieving pertinent information unattainable. The advent of the Internet has amplified the significance of accessible and readily available information. Additionally, it receives support from the World Wide Web Consortium (W3C) and global organizations responsible for establishing web standards, such as Web Ontology, Inc. It expands the functionality of the website to facilitate the retrieval, integration, and transmission of information. In recent years, several major organizations have shown a strong inclination towards using semantic technologies for the purpose of collecting Big Data. Undoubtedly, there are other advantages of integrating this into the Creative. It enhances the ability of end-users to manage data from many repositories, focuses on changing the corporate environment and the user experience, and incorporates individual definitions and integrates several data sources. Furthermore, the market’s evolving expectations and contemporary organizational practices require an adaptable but all-encompassing information strategy. Integration of data warehousing may be achieved by the use of scattered corporate ontologies. This study explores the impact of the Semantic Web on enhancing the intelligence of Big Data. It analyses the obstacles and opportunities associated with the integration of Big Data with the Semantic Web.
... In distributed stream processing, heterogeneous data streams arriving at remote sites need to be shipped to the processing site for join processing. In data streams, the underlying data structure and type may change over time [15], [16] which needs to be process on real-time basis. Semi-structured/un-structured data streams may be heterogeneous in two main aspects: first they may be observing different set of attributes, second their format needs not to be same: Raw data, XML, VOLUME 11, 2023 This work is licensed under a Creative Commons Attribution 4.0 License. ...
Heterogeneity is the key aspect of complex networks and smart devices for using it as nature of live streams. The heterogeneous stream-disk join is a significant research topic in real-time processing applications because it can directly affect the data analytics. Multiple issues, including stream loss, scalability, disk access cost, and data accuracy, should be considered during heterogeneous stream-disk join transformation. In this work we overcome these issues by introducing a distributed heterogeneous stream-disk join architecture DHSDJArch which can prevent stream data loss as well as maintaining balance between heterogeneous distributed data sources and accuracy of stream-disk join. A four phased distributed architecture is proposed for the multi-objective optimization to transform heterogeneous incomplete stream. To prevent stream loss, configuration of log retention is proposed based on the characteristics of distributed event streaming platform (
DESP
). Specifically, two transformations are proposed to pre-process heterogeneous streams and to join pre-processed stream with distributed disk data by performing real-time disk access while compensating the differences between data sources and streaming application, respectively. We conduct comprehensive experimental study on real datasets to verify the performance of proposed architecture in terms of accuracy, log retention policy, scaling, stability and cloud data storage.
... A significant issue is that the data sources related to Covid-19 are heterogeneous, static, and broad in scope. So many heterogeneous and stationary data sources create situations where data is sometimes under-utilized or, in more extreme cases, not used for the decision-making process [8]. Another vital issue of Covid-19 is to provide semantic (machine understandable) representation of data from various exciting fields such as research, health, resources, drugs, and treatment. ...
This paper presents the Coronavirus Disease Ontology (CovidO), a superset of the available Coronavirus (COVID-19) ontologies, including all the possible dimensions. CovidO consists of an ontological network of thriving distinct dimensions for storing coronavirus information. CovidO has 175 classes, 169 properties, 4141 triples, 645 individuals with 264 nodes and 308 edges. CovidO is based on standard input of coronavirus disease data sources, activities, and related sources, which collects and validates records for decision-making used to set guidelines and recommend resources. We present CovidO to a growing community of artificial intelligence project developers as pure metadata and illustrate its importance, quality, and impact. The ontology developed in this work addresses grouping the existing ontologies to build a global data model.KeywordsCoronavirus ontologyOntology learningSemantic metadataSemantic webCOVID-19
... They introduce the syntax of the query language with context enrichment SESQL (Semantically Enriched SQL), and that proposal does not deal with data streaming integration. [Dividino et al. 2018] present a semantic data stream integration approach. Stream data sources are pre-mapped in JSON-LD (Json for Linking Data), which is similar to our solution. ...
Applications capable of integrating data from historical and streaming sources can make the most contextualized and enriched decision-making. However, the complexity of data integration over heterogeneous data sources can be a hard task for querying in this context. Approaches that facilitate data integration, abstracting details and formats of the primary sources can meet these needs. This work presents a framework that allows the integration of streaming and historical data in real-time, abstracting syntactic aspects of queries through the use of SQL as a standard language for querying heterogeneous sources. The framework was evaluated through an experiment using relational datasets and real data produced by sensors. The results point to the feasibility of the approach.
... As an example, two minutes of data capture from all sources in 2017 is estimated to be approximately equivalent in volume to one year of data capture from the year 2000 [4]. Volumes now being ingested into these systems can reach 60M reports /day [3] and as a result research exploring new distribution and semantic methods for querying and distributing the information is being conducted [5][6]. ...
Coastal nations monitor maritime activities in the interest of defence, security, and safety. This form of monitoring typically occurs at operations centers that visualize the maritime environment by creating a Recognized Maritime Picture (RMP) covering a particular area of interest. The creation of this picture changed drastically with the introduction of the Automatic Identification System (AIS). AIS messages are known to contain numerous types of errors and in April 2020 a unique error was found in the data stream. This error consisted of messages indicating the appearance of over 200 vessels in the North Atlantic taking part in a yacht race when in fact no race or physical ships existed. The following work explores the application of various machine learning (ML) techniques to help identify these types of fabricated AIS messages. Specific ML techniques were explored including: K-means clustering, Decision Tree (DT), Random Forest (RF), Feed-Forward Neural Networks (FNN), Support Vector Machines (SVM), and One-Class Support Vector Machines (One-SVM). The results showed that DT, RF, and FNN best identified the fabricated AIS messages with F1 scores greater than 93 percent on the test data.
... The representation of semantic trajectory data in RDF enables the inference of new knowledge and the publication of data as linked open data (LoD). The CRISIS system is an example of an application that deals with trajectory data streams and uses an RDF graph that semantically represents the marine data received from several sensors [22]. Baquara 2 is another example of a conceptual framework that analyzes and semantically enriches trajectories by using a customizable process [7]. ...
The use of location-based sensors has increased exponentially. Tracking moving objects has become increasingly common, consolidating a new field of research that focuses on trajectory data management. Such trajectories may be semantically enriched using sensors and social media. This enables a detailed analysis of trajectory behavior patterns. One of the problems in this field is the search for a semantic trajectory database that is flexible and adaptable; flexibility in the sense of retrieving trajectories that are closest to the user’s query and not just based on exact matching. Adaptability refers to adjusting to different types of semantic trajectories. This article proposes a new approach for representing and querying semantic trajectories based on text-processing techniques. Furthermore, we describe a framework, called SETHE (SEmantic Trajectory HuntEr), that performs similarity queries on semantically enriched trajectory databases. SETHE can be adapted according to the aspect types posed in user queries. We also presented an evaluation of the proposed framework using a real dataset, and compare our results with those of state-of-the-art approaches.
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.
With the development of the marine scientific research, a large number of marine data and information are stored in many research institutions. It is important to share the data to guide further exploration about marine, but there are some problems in sharing them such as inconsistent data format, unclear term classification and non-uniform concept definition. Therefore, marine ecology knowledge ontology is proposed to solve the problems. Firstly, analyzed the complexity of marine ecosystems and proposed ontology construction model; secondly, based on role theory and the use of ontology editor tool Hozo, marine ecological knowledge ontology was built; finally, described the applications based on the ontology.
The Semantic Sensor Network (SSN) ontology is an ontology for describing sensors and their observations, the involved procedures, the studied features of interest, the samples used to do so, and the observed properties, as well as actuators. SSN follows a horizontal and vertical modularization architecture by including a lightweight but self-contained core ontology called SOSA (Sensor, Observation, Sample, and Actuator) for its elementary classes and properties. With their different scope and different degrees of axiomatization, SSN and SOSA are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Web of Things. Both ontologies are described below, and examples of their usage are given.
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.
This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.
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.