Recently, there has been a renovated interest in functional dependencies due to the possibility of employing them in several advanced database operations, such as data cleaning, query relaxation, record matching, and so forth. In particular, the constraints defined for canonical functional dependencies have been relaxed to capture inconsistencies in real data, patterns of semantically related data, or semantic relationships in complex data types. In this paper, we have surveyed 35 of such functional dependencies, providing a classification criteria, motivating examples, and a systematic analysis of them.

Trigger-Action platforms are systems that enable users to easily define, in terms of conditional rules, custom behaviors concerning Internet-of-Things (IoT) devices and web services. Unfortunately, although these tools stimulate the creativity of users in building automation, they may also introduce serious risks for the users. Indeed, trigger-action rules can lead to the possibility of users harming themselves, for example by unintentionally disclosing non-public information, or unwillingly exposing their smart environment to cyber-threats. In this paper, we propose to use Natural Language Processing (NLP) techniques to detect automation rules, defined within Trigger-Action IoT platforms, that potentially violate the security or privacy of the users. The proposed NLP-based models capture the semantic and contextual information of the trigger-action rules by applying classification techniques to different combinations of rule’s features. We evaluate the proposed solution with the mainstream trigger-action platform, namely IFTTT, by training the NLP models with a dataset of 76,741 rules labeled by using an ensemble of three semi-supervised learning techniques. The experimental results demonstrate that the model based on BERT (Bidirectional Encoder Representations from Transformers) obtains the highest performances when trained on all features, achieving average Precision and Recall values between 88% and 93%. We also compare the achieved performances with those of a baseline system implementing information flow analysis.

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.