The analysis of software artifacts plays a crucial role in supporting processes aimed at profiling developers.These artifacts, generated through interactions on versioning platforms such as GitHub, incorporate both structured and unstructured data.While existing literature primarily focuses on structured data from commit events, this study explores the potential of leveraging textual data to enhance profiling tasks, analyzing the developer interaction with other team members and/or users.In particular, we propose a Natural Language Processing (NLP) pipeline that utilizes topic modeling and sentiment analysis techniques to process GitHub Issue textual data.This approach has been evaluated using one of the most detailed just-in-time defect prediction datasets.Although the proposed pipeline has the capacity to support a general design of developer-related features that can be thoroughly analyzed using a wide range of approaches, in this paper, we employ clustering approaches for this purpose.The results demonstrate that features related to textual information provide a richer and more accurate representation of developer activities, providing valuable insights into data mining operations within software repositories.

Recently, dishonest sellers are using social platforms to advertise products that can be purchased for free through a refund mechanism, which is based on the writing of five-star fake reviews. The aim is to increase product visibility by influencing their ranking compared to similar products. This mechanism is leading to a significant distortion of e-commerce platforms, eroding trust among customers and sellers. In this paper, we address the problem of identifying fake reviews, aiming to provide an approach for mitigating fraudulent practices that compromise the integrity and transparency of e-commerce platforms. We propose a supervised model tailored for identifying fake reviews for refund purposes and compare its performance with some of the most recent generative models. Since, to the best of our knowledge, no datasets exist in the literature suitable for fake review identification in the process of Purchasing, Requesting reviews, and Refunding a product, we first proposed a new dataset of fake and genuine reviews from Amazon, collected with the help of a domain expert. Then, we defined five other new datasets containing reviews automatically generated by language models. To interact with these models, we designed new prompt approaches specifically tailored to our goal, which exploit the iterative refinement behind these models for improving classification results. Experimental results demonstrated the effectiveness of the supervised model in detecting both types of fake reviews, outperforming state-of-the-art models with improvements ranging from 0.23 to 0.70 in terms of accuracy, precision, and recall. • The requesting reviews and refunding phenomenon (PRP process) is analyzed. • PRP reviews are collected from web pages and then validated by a domain expert. • A BERT-based model is successfully applied for properly identifying PRP fake reviews. • The proposed model is compared with Large Language Models and state-of-the-art models. • New datasets of real and automatically generated PRR reviews have been proposed.

The importance of exploiting profiling metadata, such as Relaxed Functional Dependencies (RFDs), to support advanced data processing tasks, continues to grow also due to the availability of algorithms capable of automatically extracting them from data. Nevertheless, in order to use this type of metadata in real-life contexts, it is also necessary to ensure their correct interpretation of their meaningfulness and their possible evolution over time. To this end, in this paper, we present a new tool that allows visual analysis and explainability of how discovery results evolve according to changes in the data. More specifically, it provides a comprehensive overview of the impact that data changes, by possibly analyzing in-depth affected dependencies and understanding motivations underlying their evolution through a textual explanation. The effectiveness of the proposed tool has been evaluated by conducting a user study, which highlighted RYAN’s capability to yield an intuitive visualization of RFD discovery results and to provide a clear explanation of the reasons that led to the evolution of RFDs.

Intelligent diagnosis processes rely on Artificial Intelligence (AI) techniques to provide possible diagnoses by analyzing patient data and medical information. To make accurate and quick diagnoses, it is possible to use AI tools to efficiently analyze huge amounts of data and find patterns that a clinician might miss. In recent years, new large language models (LLMs), such as ChatGPT and Google BARD, have shown remarkable capabilities in several domains, including intelligent diagnostics. This research aims to compare the performances of ChatGPT and traditional machine learning models for making diagnoses of low- and medium- risk diseases only based on their symptoms. On the basis of our study, we defined four research questions: RQ1) What are the benefits and limitations of using ChatGPT in intelligent diagnosis? RQ2) How do traditional machine learning approaches compare to ChatGPT for intelligent diagnosis? RQ3) How does ChatGPT compare with other LLMs and domain-specific natural language processing models in the intelligent diagnosis tasks?, and RQ4) What are the implications of the predictive models and ChatGPT for healthcare, and how can they be used to support people?. To answer these RQs, we first evaluate the performances of different engines of ChatGPT, also introducing a new prompt engineering methodology specifically tailored for achieving accurate diagnostic outcomes. Moreover, we compare these results with those achieved by different predictive models trained for intelligent diagnosis tasks, i.e., Google BARD, and two domain-specific NLP models. Finally, we propose a new interactive bot available for users that relies on the best-performing models evaluated in the previous steps. The experiments have been conducted using two medical datasets for disease prediction consisting of more than 100 symptoms associated with several diagnoses.

Over the past decade, there have been numerous extensions to the definition of Functional Dependency (fd), culminating in the introduction of Relaxed Functional Dependency (rfd), offering more flexible constraints compared to traditionalfds. This increased flexibility makesrfds well-suited for exploring and profiling data in datasets with lower data quality. However, efficiently identifyingrfds within dynamic data sources presents a significant challenge, as it requires processing an entire dataset from scratch whenever modifications occur. To tackle this problem, incremental discovery algorithms have been defined, but they often suffer when the frequency and the size of batches of updates increase. This article presents a new algorithm, namelyD-IndiBits, relying on a new decentralized architecture to balance the workload that drives the incremental discovery process ofIndiBits, which is based on bitwise operators for computing attribute similarities. Experiments demonstrateD-IndiBits's effectiveness compared tofdandrfddiscovery algorithms on both static and dynamic real-world data. With batches of modifications of sizes 10 k and 100 k,D-IndiBitsis capable of updating the set ofrfds in a few seconds, whereas all other approaches often employ more than 3 hours.

One of the main challenges in data profiling is to efficiently extract metadata from dynamic information sources, by avoiding the processing of the whole dataset from scratch upon modifications. In this paper, we present IndiBits, an algorithm for discovering relaxed functional dependencies (RFDs for short), which represent data relationships relying on approximate matching paradigms. IndiBits is able to dynamically infer and update the RFDs holding on a dataset upon modification operations performed on it. It exploits a binary representation of data similarities, a new validation method, and specific search methods, to dynamically update the set of RFDs, based on previously holding RFDs and the type of modifications performed over data. Experimental results demonstrate the effectiveness of IndiBits on real-world datasets, even in comparison with FD and RFD discovery algorithms in both static and dynamic scenarios.

Today, people of all ages are increasingly using Web platforms for social interaction. Consequently, many tasks are being transferred over social networks, like advertisements, political communications, and so on, yielding vast volumes of data disseminated over the network. However, this raises several concerns regarding the truthfulness of such data and the accounts generating them. Malicious users often manipulate data to gain profit. For example, malicious users often create fake accounts and fake followers to increase their popularity and attract more sponsors, followers, and so on, potentially producing several negative implications that impact the whole society. To deal with these issues, it is necessary to increase the capability to properly identify fake accounts and followers. By exploiting automatically extracted data correlations characterizing meaningful patterns of malicious accounts, in this article we propose a new feature engineering strategy to augment the social network account dataset with additional features, aiming to enhance the capability of existing machine learning strategies to discriminate fake accounts. Experimental results produced through several machine learning models on account datasets of both the Twitter and the Instagram platforms highlight the effectiveness of the proposed approach toward the automatic discrimination of fake accounts. The choice of Twitter is mainly due to its strict privacy laws, and because its the only social network platform making data of their accounts publicly available.

To assess the quality of data it is useful to extract properties and relationships among them. However, exceptions and approximations need be considered in real-world settings. To this end, relaxed FDs (RFDs) are data dependencies accounting for both exceptions and similarities on data, but their discovery is an extremely complex problem, also due to the necessity of specifying similarity and validity thresholds. The RFD discovery algorithm presented in this paper exploits the concept of dominance to automatically derive similarity thresholds. The discovery performances and the effectiveness of the proposed algorithm are assessed through a comparative evaluation with state-of-art approaches.

With the advent of Big Data there is an increasing necessity to incrementally mine information from data originating from sensors and other dynamic sources. Thus, it is necessary to devise algorithms capable of mining useful information upon possible evolutions of databases. Among these, there are certainly data profiling info, such as functional dependencies (fd for short), which are particularly useful for data integration and for assessing the quality of data. The incremental scenario requires the definition of search strategies and validation methods able to analyze only the portion of the dataset affected by the last changes. In this paper, we propose a new validation method, which exploits regular expressions and compressed data structures to efficiently verify whether a candidate fd holds on an updated version of the dataset. Experimental results demonstrate the effectiveness of the proposed method on real-world datasets adapted for incremental scenarios, also compared with a baseline incremental fd discovery algorithm.

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.

Nowadays, the human influence often depends on the number of followers that an individual has in his/her own social media context. To this end, the presence of fake accounts is one of the most relevant problems and can potentially have a big impact on many real life and business activities. Fake followers are dangerous for social platforms, since they may alter concepts like popularity and influence, which might yield a strong impact on economy, politics, and society. Thus, it is necessary to devise new methodologies enabling the possibility to identify and characterize fake accounts. This work presents a novel technique to discriminate real accounts on social networks from fake ones. The technique exploits knowledge automatically extracted from big data to characterize typical patterns of fake accounts. We empirically evaluated the proposed technique on the Twitter social network, and achieved significant results in terms of discrimination capabilities.

The availability of big data collections, together with powerful hardware and software mechanisms to process them, gives nowadays the possibility to learn useful insights from data, which can be exploited for multiple purposes, including marketing, fault prevention, and so forth. However, it is also possible to learn important metadata that can suggest how data should be manipulated in several advanced operations. In this paper, we show the potentiality of learning from data by focusing on the problem of relaxing the results of database queries, that is, trying to return some approximated answer to a query when a result for it is unavailable in the database, and the system will return an empty answer set, or even worse, erroneous mismatch results. In particular, we introduce a novel approach to rewrite queries that are in disjunctive normal form and contain a mixture of discrete and continuous attributes. The approach preprocesses data collections to discover the implicit relationships that exist among the various domain attributes, and then uses this knowledge to rewrite the constraints from the failing query. In a first step, the approach tries to learn a set of functional dependencies from the data, which are ranked according to special mechanisms that will successively allow to predict the order in which the extracted dependencies have to be used to properly rewrite the failing query. An experimental evaluation of the approach on three real data sets shows its effectiveness in terms of robustness and coverage.

Functional dependencies (fds) express important relationships among data, which can be used for several goals, including schema normalization and data cleansing. However, to solve several issues in emerging application domains, such as the identification of data inconsistencies or patterns of semantically related data, it has been necessary to relax the fd definition through the introduction of approximations in data comparison and/or validity. Moreover, while fds were originally specified at design time, with the availability of massive data and computational power many algorithms have been devised to automatically discover them from data, including algorithms for discovering some types of relaxed fds. In this paper we present a technique that exploits lattice-based algorithms for the discovery of fds from data, in order to detect relaxed fds. Moreover, we introduce an algorithm to determine a proper distance threshold for a given relaxed fd holding over the entire database.

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.

One of the problems arising upon the evolution of a database schema is that some queries and views defined on the previous schema version might no longer work properly. Thus, evolving a database schema entails the redefinition of queries and views to adapt them to the new schema. Although this problem has been mainly raised in the context of traditional information systems, solutions to it are also advocated in other database-related areas, such as Data Integration, Web Data Integration, and Data Warehouses. The problem is a critical one, since industrial organizations often need to adapt their databases and data warehouses to frequent changes in the real world. In this article, we provide a survey of existing approaches and tools to the problem of adapting queries and views upon a database schema evolution; we also propose a classification framework to enable a uniform comparison method among many heterogeneous approaches and tools.

End-user development (EUD) is drawing an increasing attention due to the necessity of users to frequently extend and personalize their applications. In particular, EUD in the context of Web (EUDWeb) is focusing on technologies capable of supporting development tasks that the end-user feels more complex. However, although the specification and implementation of access control is perceived as a particularly complex task, little efforts have been made to support it within current EUDWeb environments. Thus, in this paper we propose an EUDWeb framework and tool for the specification and the generation of web applications embedding access control mechanisms. We extended a previous mockup-based EUDWeb approach, by introducing visual assistance mechanisms enabling the specification of role-based access control policies, and their integration within the application logic. The usability of the proposed framework has been evaluated by means of a user study, in which we have shown that a group of heterogeneous end-users could proficiently use the proposed framework to develop meaningful web applications, some of which including access control functionalities.

Despite many innovative systems supporting the data integration process, designers advocate more abstract metaphors to master the inherent complexity of this activity. In fact, the visual notations provided in many modern data integration systems might run into scale up problems when facing the integration of big data sources. Thus, higher level visual notations and automatic schema mapping mechanisms might be the key factors to make the data integration process more tractable. In this paper we present the Conceptual Data Integration Language (CoDIL), a visual language providing conceptual level visual mechanisms to manipulate and integrate data sources, together with a formalization of the language icon operators by means of ALCN Description Logic. The formalization allowed us to define the logic-level semantics of CoDIL, providing reasoning rules for validating the correctness of a data integration process and for generating the logic-level reconciled schema.

End-user development (EUD) is receiving increasing attention, due to the necessity of frequent extensions and personalizations of applications. In particular, EUDWeb technologies have focused on the support of web development tasks which are generally perceived to be complex by end-user developers. However an area of neglect within current EUDWeb environments is the support for the specification and implementation of access control, although it is perceived as a particularly complex task. Thus, in this paper we propose an EUDWeb approach and tool for the specification and generation of web applications embedding access control mechanisms. We extended a previous mockup-based EUDWeb approach by introducing visual assistance mechanisms enabling the specification of role-based access control policies and their plugging within the application logic.