Tobias Käfer

Observing Linked Data Dynamics

In this paper, we present the design and first results of the Dynamic Linked Data Observatory: a long-term experiment to monitor the two-hop neighbourhood of a core set of eighty thousand diverse Linked Data documents on a weekly basis. We present the methodology used for sampling the URIs to monitor, retrieving the documents, and further crawling part of the two-hop neighbourhood. Having now run this experiment for six months, we analyse the dynamics of the monitored documents over the data collected thus far. We look at the estimated lifespan of the core documents, how often they go on-line or off-line, how often they change; we further investigate domain-level trends. Next we look at changes within the RDF content of the core documents across the weekly snapshots, examining the elements (i.e., triples, subjects, predicates, objects, classes) that are most frequently added or removed. Thereafter, we look at how the links between dereferenceable documents evolves over time in the two-hop neighbourhood.

Towards declarative programming and querying in a distributed Cyber-Physical System: The i-VISION case

Cyber-Physical Systems that interconnect components of different vendors and knowledge domains bring together a multitude of data schemas and interaction paradigms. In the i-VISION project, we integrate Virtual Reality technologies with Human Factors analysis methods using Semantic Technologies to allow for improved aircraft cockpit design. In this paper, we present the case of the i-VISION project, specifically the software the project develops, describe its architecture and show how we allow for integrated querying by connecting a flight simulator, a Virtual Reality engine, and a Human Factors analysis tool. We use a gateway between the simulator part of the system, which follows the publish/subscribe paradigm, and the Human Factors analysis part, which follows the REST architectural style. We model the data using Semantic Web languages, make the data available using web protocols, and integrate and query the data using a rule engine for Linked Data.

Integrating highly dynamic RESTful linked data APIs in a virtual reality environment.

We demonstrate a Virtual Reality information system that shows the applicability of REST in highly dynamic environments as well as the advantages of Linked Data for on-the-fly data integration. We integrate a motion detection sensor application to remote control an avatar in the Virtual Reality. In the Virtual Reality, information about the user is integrated and visualised. Moreover, the user can interact with the visualised information.

Linked data techniques for the web of things: tutorial

The tutorial covers web technologies for specifying and executing applications involving networked sensors and actuators based on a logical representation of world state and application behaviour.

Dynamics in Linked Data Environments

Linked Data resources represent things in the world, which change over time, i. e. the resources are dynamic. We want to address open questions in building complex applications that consume and interact with Linked Data resources. Applications that consume Linked Data need to know about the dynamics of Linked Data to efficiently obtain fresh data to build their logic on. To address this problem of freshness, we set up a study to observe the dynamics of Linked Data on the Web and provide insights into the dynamics of Linked Data on the Web. Increasingly, the Web not only provides read-only Linked Data resources, but also writeable Linked Data resources. We develop a model for dynamics in the context of writeable Linked Data such that complex applications can be built on this model. We last present an observer that tracks the execution of applications that interact with multiple Linked Data resources.

Exploring the Dynamics of Linked Data

Little is known about the dynamics of Linked Data, primarily because there have been few, if any, suitable collections of data made available for analysis of how Linked Data documents evolve over time. We aim to address this issue. We propose the Dynamic Linked Data Observatory, which provides the community with such a collection, monitoring a fixed set of Linked Data documents at weekly intervals. We have now collected eight months of raw data comprising weekly snapshots of eighty thousand Linked Data documents. Having published results characterising the high-level dynamics of Linked Data, we now wish to disseminate results: we wish to investigate how results from our experiment might benefit the community and what online services and statistics (relating to Linked Data dynamics) would be most useful for us to provide.