When are two entries about a small molecule in different datasets the same? If they have the same drug name, chemical structure, or some other criteria? The choice depends upon the application to which the data will be put. However, existing Linked Data approaches provide a single global view over the data with no way of varying the notion of equivalence to be applied.In this paper, we present an approach to enable applications to choose the equivalence criteria to apply between datasets. Thus, supporting multiple dynamic views over the Linked Data. For chemical data, we show that multiple sets of links can be automatically generated according to different equivalence criteria and published with semantic descriptions capturing their context and interpretation. This approach has been applied within a large scale public-private data integration platform for drug discovery. To cater for different use cases, the platform allows the application of different lenses which vary the equivalence rules to be applied based on the context and interpretation of the links.

We present a novel sensor network query processing architecture that (a) covers all the query optimization phases that are required to map a declarative query to executable code; and (b) does so for a more expressive query language than has heretofore been supported over sensor networks. The architecture is founded on the view that a sensor network truly is a distributed computing infrastructure, albeit a very constrained one. As such, we address the problem of how to develop a comprehensive optimizer for an expressive declarative continuous query language over acquisitional streams as one of finding extensions to a classical distributed query processing architecture that contend with the peculiarities of sensor networks as an environment for distributed computing.