We present a framework for modeling visual notations and for generating the corresponding visual programming environments. The framework can be used for modeling the diagrammatic notations of software development methodologies, and to generate visual programming environments with CASE tools functionalities. This is accomplished through an underlying modeling process based on the visual notation syntactic model of eXtended Positional Grammars (XPG, for short), and the associated parsing methodology, XpLR. In particular, the process requires the modeling of the basic elements (visual symbols) of a visual notation, their syntactic properties, the relations between them, the syntactic rules to formally define the set of feasible visual sentences, and a set of semantic routines performing additional checks and translation tasks. Such a process is completely supported by the VLDesk system, which enables the automatic generation of an editor for drawing visual sentences, as well as a processor for their recognition, parsing, and translation into other notations.The proposed framework also provides the basis for the definition of a meta-CASE technology. In fact, we can customize the generated visual programming environment in terms of the supported visual notation, its syntactic properties, and the translation rules. We have used this framework to model several diagrammatic notations used in software development methodologies, including those of the Unified Modeling Language.

With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (FDs). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through FDs, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed FDs (RFDs) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose DOMINO, a new discovery algorithm for RFDs that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring RFDs. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm.