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.

Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data.

NoSQL stores are used extensively for scalable storage and efficient querying of large spatio-temporal data collections in modern applications. Yet, despite their popularity, NoSQL systems have two main limitations when confronted with spatio-temporal data: (a) they do not offer optimized indexing methods, and (b) they still rely on heterogeneous languages and lack of standardization in data access, a situation bearing resemblance to the narrative of the tower of Babel. To address these limitations, we propose NoDA, a system for scalable querying of spatio-temporal data stored in different NoSQL stores in a unified way. NoDA relies on an abstraction layer that consists of data access operators with clear semantics, that provides a unified view of the underlying NoSQL stores. Furthermore, NoDA offers spatio-temporal operators that are internally implemented in an efficient way, by taking into advantage the individual features of each NoSQL store. Capitalizing on the query operators, NoDA provides a declarative interface based on a SQL-like language, allowing users to query different NoSQL stores using SQL. Our experiments demonstrate that NoDA significantly improves the performance of spatio-temporal querying over different types of NoSQL stores.

In this demo paper, we present a system prototype, called NoDA, that unifies access to NoSQL stores, by exposing a single interface to big data developers. This hides the heterogeneity of NoSQL stores, in terms of different query languages, non-standardized access, and different data models. NoDA comprises a layer positioned on top of NoSQL stores that defines a set of basic data access operators (filter, project, aggregate, etc.), implemented for different NoSQL engines. The provision of generic data access operators enables a declarative interface using SQL as query language. Furthermore, NoDA is extended to provide more complex operators, such as geospatial operators, which are only partially supported by NoSQL stores. We demonstrate NoDA by showcasing that the exact same query can be processed by different NoSQL stores, without any modification or transformation whatsoever.

In this paper, we propose NoDA, an abstraction layer consisting of spatio-temporal data access operators, which is used to access NoSQL storage engines in a unified way. NoDA alleviates the burden from big data developers of learning the query language of each NoSQL store, and offers a unified view of the underlying NoSQL store. Our approach is inspired by the equivalent paradigm of drivers (such as JDBC) in the relational database world, where the application code is indifferent to the exact underlying database engine. Still, the challenges in the NoSQL world are manifold, because of the lack of standardization in data access. We focus on the specific case of mobility data, and show how spatial and spatio-temporal operators, such as range queries and k-nearest neighbor, are supported in a unified way. Moreover, we present challenges and solutions for supporting spatial and spatio-temporal data in NoSQL stores.

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.

Abstract We present a system for online composite event recognition over streaming positions of commercial vehicles. Our system employs a data enrichment module, augmenting the mobility data with external information, such as weather data and proximity to points of interest. In addition, the composite event recognition module, based on a highly optimised logic programming implementation of the Event Calculus, consumes the enriched data and identifies activities that are beneficial in fleet management applications. We evaluate our system on large, real-world data from commercial vehicles, and illustrate its efficiency.