Abstract (EN)

A major issue that arises when designing scientific experiments(i.e., workflows) is that of identifying the modules (which are of-ten “black boxes”), that are suitable for performing the steps of theexperiment. To assist scientists in the task of identifying suitablemodules, semantic annotations have been proposed and used to de-scribe scientific modules. Different facets of the module can be de-scribed using semantic annotations. Our experience with scientistsfrom modern sciences such as bioinformatics, biodiversity and as-tronomy, however, suggests that most of semantic annotations thatare available are confined to the description of the domain of inputand output parameters of modules. Annotations specifying the be-havior of the modules, as to the tasks they play, are rarely specified.To address this issue, we argue in this paper that data examples arean intuitive and effective means for understanding the behavior ofscientific modules. We present a heuristic for automatically gener-ating data examples that annotate scientific modules without rely-ing on the existence of the module specifications, and show throughan empirical evaluation that uses real-world scientific modules theeffectiveness of the heuristic proposed.The data examples generated can be utilized in a range of scientificmodule management operations. To demonstrate this, we presentthe results of two real-world exercises that show that: (i) Data ex-amples are an intuitive means for human users to understand thebehavior of scientific modules, and that (ii) data examples are aneffective ingredient for matching scientific modules.