FACHBEITRAG Unleashing XQuery for Data-Independent Programming

an SQL equivalent for XML data, but its roots in functional programming make it also a perfect choice for processing almost any kind of structured and semi-structured data. Apart from standard XML processing, however, advanced language features make it hard to efficiently implement the complete language for large data volumes. This work proposes a novel compilation strategy that provides both flexibility and efficiency to unleash XQuery’s potential as data programming language. It combines the simplicity and versatility of a storage-independent data abstraction with the scalability advantages of set-oriented processing. Expensive iterative sections in a query are unrolled to a pipeline of relational-style operators, which is open for optimized join processing, index use, and parallelization. The remaining aspects of the language are processed in a standard fashion, yet can be compiled anytime to more efficient native operations of the actual runtime environment. This hybrid compilation mechanism yields an efficient and highly flexible query engine that is able to drive any computation from simple XML transformation to complex data analysis, even on non-XML data. Experiments with our prototype and stateof-the-art competitors in classic XML query processing and business analytics over relational data attest the generality and efficiency of the design

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)