Hypermedia-based discovery for source selection using low-cost linked data interfaces

Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness

Astro-WISE: Chaining to the Universe

The recent explosion of recorded digital data and its processed derivatives threatens to overwhelm researchers when analysing their experimental data or when looking up data items in archives and file systems. While current hardware developments allow to acquire, process and store 100s of terabytes of data at the cost of a modern sports car, the software systems to handle these data are lagging behind. This general problem is recognized and addressed by various scientific communities, e.g., DATAGRID/EGEE federates compute and storage power over the high-energy physical community, while the astronomical community is building an Internet geared Virtual Observatory, connecting archival data. These large projects either focus on a specific distribution aspect or aim to connect many sub-communities and have a relatively long trajectory for setting standards and a common layer. Here, we report "first light" of a very different solution to the problem initiated by a smaller astronomical IT community. It provides the abstract "scientific information layer" which integrates distributed scientific analysis with distributed processing and federated archiving and publishing. By designing new abstractions and mixing in old ones, a Science Information System with fully scalable cornerstones has been achieved, transforming data systems into knowledge systems. This break-through is facilitated by the full end-to-end linking of all dependent data items, which allows full backward chaining from the observer/researcher to the experiment. Key is the notion that information is intrinsic in nature and thus is the data acquired by a scientific experiment. The new abstraction is that software systems guide the user to that intrinsic information by forcing full backward and forward chaining in the data modelling.Comment: To be published in ADASS XVI ASP Conference Series, 2006, R. Shaw, F. Hill and D. Bell, ed

Security-oriented infrastructures for social simulation

The JISC-funded National e-Infrastructure for Social Simulation (NeISS) project aims to develop and provide new services to social scientists and public/private sector policymakers interested in “what-if” questions that have an impact upon society and can be tackled through social simulation. For the first what-if question, a traffic simulation modelling how congestion will affect routes within a city or region projected across a time-span of decades has been identified. This paper describes the work that has been done in implementing a secure, user-oriented environment that provides seamless access to relevant nationally significant data sets such as the 2001 Census and demographic transition statistics from the British Household Panel Survey (BHPS) , and a Population Reconstruction Model (PRM) simulator, which simulates a population of individuals or households based upon these data sets