Efficient Data Management and Statistics with Zero-Copy Integration

Statistical analysts have long been struggling with evergrowing data volumes. While specialized data management systems such as relational databases would be able to handle the data, statistical analysis tools are far more convenient to express complex data analyses. An integration of these two classes of systems has the potential to overcome the data management issue while at the same time keeping analysis convenient. However, one must keep a careful eye on implementation overheads such as serialization. In this paper, we propose the in-process integration of data management and analytical tools. Furthermore, we argue that a zero-copy integration is feasible due to the omnipresence of C-style arrays containing native types. We discuss the general concept and present a prototype of this integration based on the columnar relational database MonetDB and the R environment for statistical computing. We evaluate the performance of this prototype in a series of micro-benchmarks of common data management tasks

Deployment of RDFa, Microdata, and Microformats on the Web – A Quantitative Analysis

More and more websites embed structured data describing for instance products, reviews, blog posts, people, organizations, events, and cooking recipes into their HTML pages using markup standards such as Microformats, Microdata and RDFa. This development has accelerated in the last two years as major Web companies, such as Google, Facebook, Yahoo!, and Microsoft, have started to use the embedded data within their applications. In this paper, we analyze the adoption of RDFa, Microdata, and Microformats across the Web. Our study is based on a large public Web crawl dating from early 2012 and consisting of 3 billion HTML pages which originate from over 40 million websites. The analysis reveals the deployment of the different markup standards, the main topical areas of the published data as well as the different vocabularies that are used within each topical area to represent data. What distinguishes our work from earlier studies, published by the large Web companies, is that the analyzed crawl as well as the extracted data are publicly available. This allows our findings to be verified and to be used as starting points for further domain-specific investigations as well as for focused information extraction endeavors

IL-31 expression by inflammatory cells is preferentially elevated in atopic dermatitis

Interleukin-31 (IL-31) is a recently discovered cytokine expressed in many human tissues, and predominantly by activated CD4+ T cells. IL-31 signals through a heterodimeric receptor consisting of IL-31 receptor alpha (IL-31RA) and oncostatin M receptor beta (OSMR). Earlier studies have shown involvement of IL-31 and its receptor components IL-31RA and OSMR in atopic dermatitis, pruritus and Th2-weighted inflammation at the mRNA level. The aim of this study was to investigate IL-31 protein expression in skin of such conditions. Immunohisto-chemical staining for IL-31, IL-31RA and OSMR was performed in formalin-fixed paraffin-embedded biopsy specimens. IL-31 expression was increased in the inflammatory infiltrates from skin biopsies taken from subjects with atopic dermatitis, compared with controls (p ≤ 0.05). IL-31, IL-31RA and OSMR protein immunoreactivity was not increased in biopsies from subjects with other Th2-weighted and pruritic skin diseases. Our results confirm, at the protein level, the relationship between IL-31 expression and atopic dermatitis. Our results do not support a general relationship between expression of IL-31/IL-31R and pruritic or Th2-mediated diseases