In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility data sets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.

Motivated by the global unrest related to the COVID-19 pandemic, this demo paper presents a system for acquisition of COVID-related data from different, public sources, and interlinking under a common semantic data model at a fine level of granularity. The integrated data set contains data from several European countries, which come in different schemata, formats, granularity, and data integration acts as a facilitator towards querying data from different sources, joint data analysis, and identifying correlations at varying geographical level. Moreover, our work shows how such an integrated data set can be exploited to answer complex questions for the pandemic, also in combination with other data sets via federated queries. © 2021 Copyright held by the owner/author(s).

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.

We present a big data framework for the prediction of streaming trajectory data, enriched from other data sources and exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. To meet this goal, we follow a multi-step methodology. First, we efficiently compress surveillance data in an online fashion, by constructing trajectory synopses that are spatio-temporally linked with streaming and archival data from a variety of diverse and heterogeneous data sources. The enriched stream of trajectory synopses is stored in a distributed RDF store, supporting data exploration via SPARQL queries. The enriched stream of synopses along with the raw data is consumed by trajectory prediction algorithms that exploit mined patterns from the RDF store, namely medoids of (sub-) trajectory clusters, which prolong the horizon of useful predictions. The framework is extended with offline and online interactive visual analytics tool to facilitate real world analysis in the maritime and the aviation domains.

We present a stream reasoning system for monitoring vessel activity in large geographical areas. The system ingests a compressed vessel position stream, and performs online spatio-temporal link discovery to calculate proximity relations between vessels, and topological relations between vessel and static areas. Capitalizing on the discovered relations, a complex activity recognition engine, based on the Event Calculus, performs continuous pattern matching to detect various types of dangerous, suspicious and potentially illegal vessel activity. We evaluate the performance of the system by means of real datasets including kinematic messages from vessels, and demonstrate the effects of the highly efficient spatio-temporal link discovery on performance.

Ontology modularization concerns about extracting ontology units from ontologies and partitioning large ontologies to possibly interdependent ontology units. Each unit specifies a specific context for performing ontology maintenance, evolution and reasoning tasks, which nevertheless has to be combined with chunks of tasks performed in other units. The modularization task is affected by assumptions concerning the mutual relations between the domains covered by distinct units, as well as by the expressiveness of the language used for specifying knowledge in units and for connecting distinct units. This paper presents a tool for partitioning SHIQ ontologies into units. These units can be combined using subjective class-to-class correspondences, as well as by inter-unit link properties that can be subjected to cardinality restrictions, existential and universal quantifiers, be hierarchically related and be transitive. While the modularization algorithm incorporated in this tool implements a specific partitioning method, the underlying representation framework provides a range of modularization possibilities, from units connected via class correspondences, to highly intertwined units, combined with class correspondences and inter-unit properties associated with restrictions.