Censuses are one of the most relevant types of statistical data, allowing analyses of the population in terms of demography, economy, sociology, and culture. For finegrained analysis, census agencies publish census microdata that consist of a sample of individual records of the census containing detailed anonymous individual information. Working with microdata from different censuses and doing comparative studies are currently difficult tasks due to the diversity of formats and granularities. In this article, we show that novel data processing techniques can be applied to make census microdata interoperable and easy to access and combine. In fact, we demonstrate how Linked Open Data principles, a set of techniques to publish and make connections of (semi-)structured data on the web, can be fruitfully applied to census microdata. We present a step-by-step process to achieve this goal and we study, in theory and practice, two real case studies: the 2001 Spanish census and a general framework for Integrated Public Use Microdata Series (IPUMS-I).

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This paper presents a novel way to introduce self-configuration and self-optimization autonomic characteristics to algorithmic skeletons using event driven programming techniques. Based on an algorithmic skeleton language, we show that the use of events greatly improves the estimation of the remaining computation time for skeleton execution. Events allow us to precisely monitor the status of the execution of algorithmic skeletons. Using such events, we provide a framework for the execution of skeletons with a very high level of adaptability. We focus mainly on guaranteeing a given execution time for a skeleton, by optimizing autonomically the number of threads allocated. The proposed solution is independent from the platform chosen for executing the skeleton for example we illustrate our approach in a multicore setting, but it could also be adapted to a distributed execution environment.

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