Georgios M. Santipantakis

OBDAIR: Ontology-Based Distributed framework for Accessing, Integrating and Reasoning with data in disparate data sources

Abstract The correlated exploitation of disparate and heterogeneous data sources is important to the efficacy of many analytics tasks. Currently in application domains of major interest, such as in the maritime and aviation domains, available technology provides real time surveillance data from moving entities, which together with archival static data, can be processed in an integrated way to detect complex events and support decision making. The variety of data in disparate sources, the heterogeneity of data formats, as well as the volume of data, make data retrieval, integration, and especially reasoning with these data, challenging tasks. This paper presents an ontology-based distributed framework that addresses conjunctively these challenges: Data retrieval, integration and reasoning with data from heterogeneous static or regularly updated data sources. The proposed OBDAIR framework provides the means to support building scalable data-driven domain-specific applications that support decision-making and problem-solving. This is achieved by processing large volumes of heterogeneous data close to the sources, supporting knowledge generation in a distributed/decentralized but still unified manner. OBDAIR integrates modular ontology representation frameworks and ontology-based data access frameworks: This article presents an instantiation of OBDAIR using the modular ontology representation framework E − SHIQ , and the Ontop ontology-based access system. This OBDAIR instance has been evaluated at recognising important complex events in the maritime domain using real-world data. Experiments show the potential of OBDAIR to detect complex events in large geographic areas with computational efficiency.

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.

The datAcron Ontology for Semantic Trajectories

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.

Ontology-Based Data Integration for Event Recognition in the Maritime Domain

Recent environmental disasters at sea have highlighted the need for efficient maritime surveillance and incident management. Currently, maritime navigation technology automatically provides real time data from vessels, which together with historical data, can be processed in an integrated way to detect complex events and support decision making. Ontology-Based Data Access (OBDA) frameworks, can be employed to access data towards this effort. However the heterogeneity of data in disparate sources make data integration a challenging task. In this paper we report on our efforts to implement a scalable system for integrating data from disparate data sources by means of existing OBDA frameworks and distributed E -- SHIQ knowledge bases, towards supporting complex event recognition. We present two versions of the implemented system, and the lessons learned from this effort.

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.

Accessing and reasoning with data from disparate data sources using modular ontologies and OBDA

This paper proposes a distributed framework for accessing, integrating and reasoning with data from heterogeneous, disparate data sources. The proposed solution combines the E -- SHIQ modular ontology representation framework with the Ontop ontology-based data access (OBDA) technology. Distribution of knowledge allows the treatment of data from disparate sources in an autonomous manner, parallelization of operations, while it allows more efficient reasoning with the data.

RDF-Gen: Generating RDF from Streaming and Archival Data

Recent state-of-the-art approaches and technologies for generating RDF graphs from non-RDF data, use languages designed for specifying transformations or mappings to data of various kinds of format. This paper presents a new approach for the generation of ontology-annotated RDF graphs, linking data from multiple heterogeneous streaming and archival data sources, with high throughput and low latency. To support this, and in contrast to existing approaches, we propose embedding in the RDF generation process a close-to-sources data processing and linkage stage, supporting the fast template-driven generation of triples in a subsequent stage. This approach, called RDF-Gen, has been implemented as a SPARQL-based RDF generation approach. RDF-Gen is evaluated against the latest related work of RML and SPARQL-Generate, using real world datasets.

Taming Big Maritime Data to Support Analytics

This article presents important challenges and progress toward the management of data regarding the maritime domain for supporting analysis tasks. The article introduces our objectives for big data–analysis tasks, thus motivating our efforts toward advanced data-management solutions for mobility data in the maritime domain. The article introduces data sources to support specific maritime situation–awareness scenarios that are addressed in the datAcron [The datAcron project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 687591 (http://datacron-project.eu).] project, presents the overall infrastructure designed for managing and exploiting data for analysis tasks, and presents a representation framework for integrating data from different sources revolving around the notion of semantic trajectories: the datAcron ontology.

Ontology-based data sources' integration for maritime event recognition

Recent environmental disasters in the sea, have highlighted the need for efficient maritime surveillance. Currently, maritime navigation technology automatically provides real time data from vessels, that together with other historical data can be processed in an integrated way to detect complex events and support decision making. Ontology-Based Data Access (OBDA) frameworks can be employed to access data towards this effort. Integration of data is critical, but the heterogeneity and the large amount of data make this a difficult task. In this paper we present two systems that we have implemented using different OBDA frameworks, emphasizing on the semantic integration of data from disparate sources to support complex event recognition. We discuss the features of each system separately and the lessons learned from this effort.

Decomposing Ontologies for the Construction of Distributed Knowledge Bases: The mONTul Method

Ontology modularization methods aim either to extract modules for a given sets of terms, or partition ontologies into sets of modules. Each module should cover a specific subject matter of the theo...