An ontology to semantically declare and describe functions

Applications built on top of the Semantic Web are emerging as a novel solution in different areas, such as decision making and route planning. However, to connect results of these solutions -i.e., the semantically annotated data - with real-world applications, this semantic data needs to be connected to actionable events. A lot of work has been done (both semantically as non-semantically) to describe and define Web services, but there is still a gap on a more abstract level, i.e., describing interfaces independent of the technology used. In this paper, we present a data model, specification, and ontology to semantically declare and describe functions independently of the used technology. This way, we can declare and use actionable events in semantic applications, without restricting ourselves to programming language-dependent implementations. The ontology allows for extensions, and is proposed as a possible solution for semantic applications in various domains

Republishing OpenStreetMap’s roads as linked routable tiles

Route planning providers manually integrate different geo-spatial datasets before offering a Web service to developers, thus creating a closed world view. In contrast, combining open datasets at runtime can provide more information for user-specific route planning needs. For example, an extra dataset of bike sharing availabilities may provide more relevant information to the occasional cyclist. A strategy for automating the adoption of open geo-spatial datasets is needed to allow an ecosystem of route planners able to answer more specific and complex queries. This raises new challenges such as (i) how open geo-spatial datasets should be published on the Web to raise interoperability, and (ii) how route planners can discover and integrate relevant data for a certain query on the fly. We republished OpenStreetMap's road network as "Routable Tiles" to facilitate its integration into open route planners. To achieve this, we use a Linked Data strategy and follow an approach similar to vector tiles. In a demo, we show how client-side code can automatically discover tiles and perform a shortest path algorithm. We provide four contributions: (i) we launched an open geo-spatial dataset that is available for everyone to reuse at no cost, (ii) we published a Linked Data version of the OpenStreetMap ontology, (iii) we introduced a hypermedia specification for vector tiles that extends the Hydra ontology, and (iv) we released the mapping scripts, demo and routing scripts as open source software

Public transit route planning through lightweight linked data interfaces

While some public transit data publishers only provide a data dump – which only few reusers can afford to integrate within their applications – others provide a use case limiting origin-destination route planning api. The Linked Connections framework instead introduces a hypermedia api, over which the extendable base route planning algorithm “Connections Scan Algorithm” can be implemented. We compare the cpu usage and query execution time of a traditional server-side route planner with the cpu time and query execution time of a Linked Connections interface by evaluating query mixes with increasing load. We found that, at the expense of a higher bandwidth consumption, more queries can be answered using the same hardware with the Linked Connections server interface than with an origin-destination api, thanks to an average cache hit rate of 78%. The findings from this research show a cost-efficient way of publishing transport data that can bring federated public transit route planning at the fingertips of anyone

Generating public transport data based on population distributions for RDF benchmarking

When benchmarking RDF data management systems such as public transport route planners, system evaluation needs to happen under various realistic circumstances, which requires a wide range of datasets with different properties. Real-world datasets are almost ideal, as they offer these realistic circumstances, but they are often hard to obtain and inflexible for testing. For these reasons, synthetic dataset generators are typically preferred over real-world datasets due to their intrinsic flexibility. Unfortunately, many synthetic dataset that are generated within benchmarks are insufficiently realistic, raising questions about the generalizability of benchmark results to real-world scenarios. In order to benchmark geospatial and temporal RDF data management systems such as route planners with sufficient external validity and depth, we designed PODiGG, a highly configurable generation algorithm for synthetic public transport datasets with realistic geospatial and temporal characteristics comparable to those of their real-world variants. The algorithm is inspired by real-world public transit network design and scheduling methodologies. This article discusses the design and implementation of PODiGG and validates the properties of its generated datasets. Our findings show that the generator achieves a sufficient level of realism, based on the existing coherence metric and new metrics we introduce specifically for the public transport domain. Thereby, PODiGG provides a flexible foundation for benchmarking RDF data management systems with geospatial and temporal data