interop-erability (IOP) challenges, which are crucial when combining air quality data from differ-ent sources as well as linking them to other datasets such as traffic or weather data. We addressed the different IOP levels, namely the legal, organisational, technical and seman-tic level. Linked Data facilitates IOP on both a technical and a semanseman-tic level. Context information, such as temperature, humidity and spatial information, enriches air quality data by re-using existing machine-readable RDF vocabularies. The principles of Linked Data make the data self-describing and machine-readable, which allows autonomous agents to reason on the sensor data. Linked Data builds upon the architecture of the World Wide Web and uses typed links between data entities from disparate sources, described using the Resource Description Framework.
Figure 25.Overview of the benchmark with the query response time (latency) of Linked Time Series Server, publishing the average sensor values in a time interval (hour) that has ended (b4).
The benchmark showed that the Linked Time Series approach lowers the cost for publishing air quality data and raises the availability because of a better caching strategy.
The results of the benchmark ascertain that—with an increasing number of clients—the LTS API has a lower CPU load than the FIWARE QL API. Additionally, as the load of the LTS is constant for historic data, the cost becomes predictable, which is crucial for public bodies that need to determine their expenses beforehand. Building on the strength of the World Wide Web—by using HTTP caching on the level of clients and servers—created not only a cost–benefit but also contributed to the stability of the air quality endpoints. The FIWARE QL API starts “sputtering” at a load of 400 clients (Figure24), in contrast with the LDF interface that still obtains good results at 10,000 requests (Figure25).
The bandwidth of the LDF endpoint is slightly higher, but the number of resources a single request consumes—such as the load on the CPU and memory—is significantly lower, due to the cache re-use. The cache re-use is the ratio of items that are requested more than once from the cache instead of consuming server resources. The literature is consistent with our findings from limiting the server interface that becomes more scalable due to Web caching [65,69]. The scenario that calculates the average sensor values in a time interval that has ended demonstrates that a part of the workload is shifted to the client but is still acceptable for the client. A non-measurable benefit is that the client can evaluate any given query on the client side, without having to rely on server-side functionality other than downloading the right fragments. Next, the Open World Assumption (OWA) becomes applicable: more data can always be downloaded in order to obtain a more precise answer [89,90].
We discussed that the challenges related to linked open sensor data time series are not limited to the volume and velocity of the time series but also to their variety interoperability (IOP) challenges, which are crucial when combining air quality data from different sources as well as linking them to other datasets such as traffic or weather data. We addressed the different IOP levels, namely the legal, organisational, technical and semantic level. Linked Data facilitates IOP on both a technical and a semantic level. Context information, such as temperature, humidity and spatial information, enriches air quality data by re-using existing machine-readable RDF vocabularies. The principles of Linked Data make the data self-describing and machine-readable, which allows autonomous agents to reason on the sensor data. Linked Data builds upon the architecture of the World Wide Web and uses typed links between data entities from disparate sources, described using the Resource Description Framework.
Based on the above, we have three recommendations about archivability, indexing and interoperability for future research. First, it is crucial to ensure that time series are
Data2021,6, 93 27 of 32
still accessible and usable for future generations, as they are valuable for research (e.g., on environmental changes). Therefore, a strategy that outlines which subsets of the data should be preserved in order to reduce the storage cost of the data in a digital archive needs to be defined. Second, research should consider a more dynamic method to fragment and index different types of time series, considering the available budget of the publisher.
Finally, on the level of interoperability, future research should explore how to bridge between the NGSI—that redefines a knowledge representation in its own—and the existing semantic assets.
Although our results are promising, there are limitations to our research. First, as the scenarios are limited, further validation of this method in a large variety of use cases and different types of sensor data time series is necessary to extrapolate our conclusions.
Second, we should evaluate this method for real-time data streams.
During this research, we determined extra challenges related to cross-domain interop-erability and architectural flexibility, which we discuss in the next paragraph.
5.2. Railway Infrastructure Data
Similar to public authorities publishing sensor-based data, ERA seeks to publish the European railway infrastructure data in an interoperable way to maximize re-use by different applications serving diverse use cases. To increase interoperability, the ERA follows the Linked Data principles and publishes a knowledge graph containing the different elements that conform to the railway infrastructure, while including semantic annotations.
We showed that publishing railway infrastructure data is feasible with an LDF-based approach, bringing the same cost-efficiency and scalability benefits as the ones measured by our benchmark on air quality sensor data. In this case, we applied the same principles of well-known vector tiling strategies for geospatial data, but we further enriched the tiles with semantic annotations based on hypermedia description vocabularies (hydra and TREE). Such annotations enable client applications to interpret the data interfaces and to traverse the underlying knowledge graph.
Moreover, our proposed data publishing approach enables route calculations to be performed on the client side, while aiming to increase the cacheability of the server re-quests and including semantic annotations describing the data interfaces via hypermedia controls [91]. This architectural design allows for greater flexibility in client application design at the cost of increased complexity for application implementation. Developers are able to implement and customize any kind of algorithm and business logic, tailored to their use cases. This is in contrast to the dedicated server-side solution systems that limit the application to the supported capabilities and features.
6. Conclusions
In this article, we presented insights on the implementation of a sustainable method for publishing open data, in a specific sensor data time series on air quality and railway infrastructure data.
Our study demonstrates how Linked Data can supportinteroperabilityat the technical and semantical level for an air quality time series. Linked Data principles not only provide interoperability towards external stakeholders but also foster a more sustainable and cost-effective architecture. We have shown that exposing Linked Data Fragments for a sensor data time series lowers the publishers’ server cost compared with the FIWARE QuantumLeap API. By monitoring the query response time of the client, we showed that the Linked Time Series (LTS) interface can support more clients (10,000 versus 400 clients with historic data), while having a lower latency. Especially for the time series queries where the time interval has ended, HTTP caching lowers the server CPU cost significantly.
As such, the LTS interface can serve as a valuable extension of the FIWARE stack to provide scalabilitywhen publishing data on the Web.
The pilot on railway infrastructure data shows that this architectural approach—which shifts the data integration to the client—facilitatesflexibility. Clients are able to perform any further processing on the data to support specific use cases. In our case, the client implements a shortest path finding algorithm, which can be tailored and adjusted for more specific needs unlike traditional server-side APIs, which limit clients to their supported features and algorithms. Moreover, context can be added at the client level without the need for rewiring the server, which often affects the entire ecosystem. However, such architectural design imposes a heavier burden on the clients, which may be reflected as poorer performance on query solving tasks. Optimized caching strategies are required to improve the overall performance of the addressed use cases.
The main difference between the two different explored use cases, lies in the nature of the data fragmentations that were applied to each case. On the one hand, for the air quality data, a time-based fragmentation was applied, which was aligned with the nature of the data and with the query requirements that needed to be supported. On the other hand, for the railway infrastructure data, we applied a geospatial fragmentation that again suited the query requirements brought forth by the use case we wanted to support.
Besides the difference in fragmentation criteria, both approaches are built following the same architectural principles, where caching plays a fundamental role in reducing the operational costs of data interfaces. We, thus, showed how this approach can be applied to independent domains and over different types of data and demonstrated how data providers can develop a sustainable method for publishing open data.
According to President von der Leyen, common data spaces are an enabler for in-novation and new jobs [8]. Interoperability within and between European common data spaces will be crucial to avoid data silos and increase the re-use of data [92,93]. We hope that insights from this article can speed-up the process of opening datasets by public and private organisations on a Web-scale and can catalyse the European Data Strategy. As such, our contributions, which build upon the principles of Linked Open Data, can be valuable for governments, organisations and researchers wanting to publish interoperable data on a Web-scale in a cost-efficient and flexible way.
Author Contributions:Conceptualization, R.B. and D.V.L.; methodology, E.V. and R.B.; software, B.V.d.V. and J.R.M.; validation, R.B. and B.V.d.V.; writing—original draft preparation, R.B., D.V.L. and J.R.M.; writing—review and editing, P.C., P.M. and M.V.C.; visualization, R.B. and J.R.M.; supervision, P.M., S.L. and E.M. All authors have read and agreed to the published version of the manuscript.
Funding:This research received no external funding.
Institutional Review Board Statement:Not applicable.
Informed Consent Statement:Not applicable.
Acknowledgments:The research activities presented in this article were funded by imec—IDLab Ghent University, Belgium and imec City of Things, Belgium. We also would like to thank Quincy Oeyen from the Agency for Facility Operations (responsible for the Digital Archive Flanders), Christophe Stroobants from the Flanders Environment Agency, Ziggy Vanlishout from the Dig-ital Flanders Agency and Philippe Michiels from imec City of Things for their valuable insights. We also extend our gratitude to the team at ERA for facilitating access to their data. Finally, we would like to thank Esther De Loof and Arthur Macpherson for proofreading this manuscript.
Conflicts of Interest:The authors declare no conflict of interest.
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