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Developing algorithms for Ambient Intelligence (AmI) environments involves testing algorithms using both simulated environments and user data. This has been traditionally done with simulators. While there are several datasets for Ambient Intelligence most of them have do not have an open format and a way to use them easily afterwards. We implement a software framework on top of an existing hardware platform for writing AmI applications that can also export a data dump consisting of heterogeneous sensor data matching several criteria from an AmI lab. Researchers using this framework can easily write AmI applications on top of shared datasets and easily test several algorithms against each other using real-world-data, a previously impossible endeavour.

International audienceIn the spirit of the Linked Data initiative pioneered by Tim Berners-Lee, we propose a new type of link between entities identified by URIs named kleenk. This new type of link bridges the gap between the classical structured data published as RDF and the semi-structured data formats pushed by social networks such as Facebook and Twitter.Unlike previously published work on RDF and its extensions, our proposal promotes links to a first-class citizenship in the world of a semantic web: a kleenk has a content of itself, can be linked to recursively, and both people and machines can harmoniously collaborate in creating, evaluating and extending them.We discuss the theoretical model of the kleenk and how it can be built on top of existing frameworks such as RDF, and identify the main challenges for adoption of the new model.Finally, we validate our model with two real-world implementations: an online platform for spreading research results between researchers and an ambient intelligence elder tracking scenario where kleenks are used to perform sensor fusion between heterogeneous data sources