AtomsMasher: Personalised Context-Sensitive Automation for the Web

This paper introduces AtomsMasher, an environment for creating reactive scripts that can draw upon widely heterogeneous information to automate common information-intensive tasks. AtomsMasher is enabled by the wealth of user-contributed personal, social and contextual information that has arisen from Web2.0 social networking content sharing and micro-blogging sites. Starting with existing web mashup tools and end-user automation, we describe new challenges in achieving reactive behaviours: deriving a consistent representation that can be used to predictably drive discrete action from a multitude of noisy, incomplete and inconsistent data sources. Our solution employs a mix of automatic and user-assisted approaches to build a common internal representation in RDF, which is used to provide a simplified programming model that lets Web2.0 programmers succinctly specify behaviours in terms of high level relationships between entities and their current contextual state. We highlight the advantages and limitations of this architecture, and conclude with ongoing work towards making the system more predictable and understandable, and accessible to non-programmers