Hi, welcome to this website! If you are reading this text, I suppose you are interested in knowing something either about me or about my work, which is good. As for the former, I'm Florian Daniel, an assistant professor at the Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB) of the Politecnico di Milano in Milan, Italy. As for the latter, my work concentrates on web engineering, mashups, UI-oriented comptuing, service-oriented computing, business process management and crowdsourcing.
The purpose of this site is to allow you to get some more insight into the above and to enable myself to keep track of what's useful to remember. It's a hard endeavor, but I'll try to keep the site as updated as possible. Should you not find what you are looking for, just drop me an email and I'll try to help.
Pattern mining, that is, the automated discovery of patterns from data, is a mathematically complex and computationally demanding problem that is generally not manageable by humans. In this article, we focus on small datasets and study whether it is possible to mine patterns with the help of the crowd by means of a set of controlled experiments on a common crowdsourcing platform. We specifically concentrate on mining model patterns from a dataset of real mashup models taken from Yahoo! Pipes and cover the entire pattern mining process, including pattern identification and quality assessment. The results of our experiments show that a sensible design of crowdsourcing tasks indeed may enable the crowd to identify patterns from small datasets (40 models). The results, however, also show that the design of tasks for the assessment of the quality of patterns to decide which patterns to retain for further processing and use is much harder (our experiments fail to elicit assessments from the crowd that are similar to those by an expert). The problem is relevant in general to model-driven development (e.g., UML, business processes, scientific workflows), in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices, organizational conventions, or technical choices, that modelers can benefit from when designing their own models.