ABSTRACT Artificial intelligence and machine learning (ML) now shape decisions in healthcare, finance and security, but they can reproduce historical prejudice and inequality. Bias in training data and in model implementation can amplify harm, especially for racial and gender minorities. Despite sustained research on fairness, mitigation in real‐world systems remains uneven, in part because stakeholders lack a shared and precise grasp of core notions, including bias, prejudice, discrimination and fairness. As a result, technical interventions are sometimes adopted without consistent conceptual grounding and reporting. This article addresses that problem by providing a knowledge base that aligns key concepts with empirical evidence and lifecycle stages. We conduct a scoping review to map sources of bias across the ML lifecycle and to identify forms of prejudice and discrimination associated with the use of sensitive attributes. We synthesize qualitative and quantitative evidence and introduce a conceptual model for organizing these findings. Our contributions are threefold: a refined lifecycle taxonomy of bias sources that introduces two additional types and spans all development stages; the explicit treatment of cognitive bias as a cross‐cutting meta‐bias; and an analysis of prejudice and discrimination that compiles a legally grounded catalogue of sensitive attributes and discusses their concepts and issues. Together, these results provide an integrated view of where and how bias emerges, and they support future research, evaluation and governance work on fairness in ML.

ABSTRACT Introduction As aging populations grow and caregiving systems strain, social robots are increasingly proposed to assist in older‐adult care. Yet, acceptance among caregivers, particularly in underrepresented regions such as Brazil, remains poorly understood. Objectives This exploratory study investigates how Brazilian caregivers perceive the utility, advantages, disadvantages, and overall acceptance of social robots in older‐adult care, accounting for demographic background, workload conditions, and prior familiarity. Method An exploratory cross‐sectional survey was administered to 94 Brazilian caregivers (formal and informal) after exposure to a realistic video demonstration of the Robios social robot. The questionnaire assessed preferences, attitudes, and acceptance across 10 structured items and 3 multiple‐choice blocks. Exploratory statistical analyses used Spearman correlations, Mann–Whitney U tests, chi‐square tests, and the Jonckheere–Terpstra trend test, with Holm‐adjusted p values for multiple comparisons. Results Caregivers prioritised functionalities related to safety monitoring and medication reminders, while expressing concerns about maintenance, technical failures, and online security. Perceived workload, rather than objective hours or caregiving experience, was positively associated with two acceptance indicators. Prior awareness of social robots was not clearly associated with acceptance in this sample. No significant differences emerged between formal and informal caregivers. Evaluation Unlike prior studies concentrated in high‐income, more institutionalised contexts, this research provides an initial systematic analysis of caregiver acceptance in Brazil's middle‐income, familistic, and predominantly informal care ecosystem. The findings challenge assumptions about demographic predictors and emphasise situational, perceptual, and cultural drivers, highlighting a collaborative caregiver‐assistant view of social robots in this context. Conclusion In this Brazilian setting, acceptance of social robots appears to be shaped less by background variables and more by perceived usefulness and relief under demanding conditions. These exploratory results inform user‐centred design and policy strategies for deploying socially adaptive robotic systems in eldercare and motivate future confirmatory studies in diverse cultural settings.

ABSTRACT Introduction The global aging population leads to increased demand for professional caregivers and innovative assistive technologies. Traditional aids such as canes and hearing devices have long supported older adults, but emerging solutions involving robotics and AI open new opportunities for enhanced care and independence. Objectives This study aimed to design and evaluate an assistive solution that integrates a social robot and a virtual assistant to support older adults in managing medical treatments and daily schedules. Methods An assistive system was developed combining a social robot and a virtual assistant. Its potential was assessed through an exploratory evaluation involving seven older adults who interacted with the solution in simulated care and schedule management scenarios. Data were collected through structured interviews to capture participants' perceptions and experiences. Results The developed solution supported effective interaction between users and the technologies, despite minor usability challenges during initial use. Participants were generally able to complete tasks such as medication reminders, appointment management, and basic conversational interactions, although some required occasional assistance or clarification. Evaluation The participants expressed positive feedback regarding usability and perceived usefulness. The combined use of social robots and virtual assistants was considered intuitive and supportive, especially in reducing cognitive load and fostering adherence to treatment routines. Conclusion The integrated assistive solution presents a promising approach to supporting older adults' independence and well‐being. By combining social presence with functional assistance, it contributes to bridging the gap between human‐centered care and technological innovation.

Process mining encompasses a series of tasks aimed at automatically unveiling knowledge about business processes from event logs registered in underlying information systems deployed in organizations. As well as numerous machine learning approaches, process mining approaches often require a vector space as input. However, the choice of the representational scheme to map event log information to a vector space sig-nificantly influences the quality of the results. This mapping poses challenges due to the diverse information in event logs and the intricate relationships within a business process. Relying solely on automated approaches may overlook relevant information, necessitating the incorporation of domain knowledge from external sources. Unfortunately, this incorporation introduces complexity. To address these inherent issues in constructing adequate vector spaces for process mining, this paper proposes a novel approach leveraging graph embedding to organize process-related information. To this end, we present a novel and highly flexible graph structure to represent process-related information that is then mapped to a dense vector space by applying the metapath2vec algorithm. The resulting dense vector space was compared to traditional vector spaces in an exploratory study, in which we solved the trace clustering task. We employ the N3 measure to assess the quality of the clusters and to verify whether domain knowledge is adequately represented in the vector spaces. The results demonstrate a superior potential of the dense vector spaces obtained via graph embedding to adequately organize the information to be submitted to the trace clustering task.

Abstract Process models, e.g., BPMN models, may represent how companies in an ecosystem interact with each other. However, the business model of the same ecosystem, e.g., expressed by an $$e^{3}value$$ e 3 v a l u e model, is often left implicit. This hinders the proper analysis of the ecosystem at the business level, and more specifically financial assessment, for which process models are less appropriate. Therefore, the question is if we can somehow derive $$e^{3}value$$ e 3 v a l u e models from BPMN models. This would not only allow for proper business model analysis but would also facilitate business model mining, similar to the success of process mining. However, although an $$e^{3}value$$ e 3 v a l u e model and BPMN model represent the same ecosystem, their perspectives differ significantly. Therefore an automated derivation of an $$e^{3}value$$ e 3 v a l u e model from a BPMN seems not to be feasible, but we can assist the $$e^{3}value$$ e 3 v a l u e model designer with practical guidelines. We explore and test our guidelines in two real-world settings, we then analyze and evaluate its application to better understand their limitations and how to improve them.

Process mining encompasses a series of tasks aimed at discovering knowledge about business processes from event logs underlying information systems deployed in organizations. Considering real-world business processes, high-complexity issues often prevent process mining techniques from producing satisfactory results. Business processes’ complexity arises from: (i) high behavioral variability as presented in unstructured processes, e.g. knowledge intensive processes, in which decisions commonly dependent on human actions; (ii) data volume, as it can reach big data levels in organizations with high-volume operations. Trace clustering can support mitigating high-complexity related issues. The process instance profiles resulting from trace clustering divide a complex problem into smaller and simpler ones. However, interpreting clustering results frequently requires decision-making and reasoning that might benefit from domain experts’ knowledge. Especially, in trace clustering-based process mining tasks, domain experts involvement enable results evaluation from the business process perspective. In this paper, a proposal for a trace clustering results visualization is presented. This visualization strategy supports evaluation from a business process perspective, enabling human-in-the-loop strategies. In order to illustrate the usefulness and appropriateness of the visualization, we present three use cases modeled on real-world event logs.

Although process model discovery has been extensively investigated over the past two decades, existing discovery methods are still not considered fully satisfactory. One problem is the difficulty of discovering accurate process models, achievable with both high recall (or fitness) and high precision, particularly for real-world event logs. This paper introduces a process discovery method, namely X-Processes, based on genetic algorithms, which aims to optimize accuracy through the F-Score calculated between recall and precision. Although genetic algorithms have been used to discover process models, such methods also have limitations as do other non-genetic algorithms-based methods. Experimental results for 12 real-world event logs show the accuracy of the process models discovered by X-Processes is higher than those of six other state-of-the-art discovery methods, including one also based on genetic algorithms. Besides accuracy, X-Processes delivers sound process models. Although its execution time is longer than the other compared discovery methods, X-Processes emerges as a solution when the need for a highly accurate process model outweighs the hunger for agility.

Improving judicial performance has become increasingly relevant to guarantee access to justice for all, worldwide. In this context, technology-enabled tools to support lawsuit processing emerge as powerful allies to enhance the justice efficiency. Using electronic lawsuit management systems within the courts of justice is a widespread practice, which also leverages production of big data within judicial operation. Some jurimetrics techniques have arisen to evaluate efficiency based on statistical analysis and data mining of data produced by judicial information systems. In this sense, the process mining area offers an innovative approach to analyze judicial data from a process-oriented perspective. This paper presents the application of process mining in a event log derived from a dataset containing business lawsuits from the Court of Justice of the State of Sao Paulo, Brazil - the largest court in the world - in order to analyze judicial performance. Although the results show these lawsuits have an ad hoc sequence flow, process mining analysis have allowed to identify most frequent activities and process bottlenecks, providing insights into the root causes of inefficiencies.

In recent years, machine learning has been used for data processing and analysis, providing insights to businesses and policymakers. Deep learning technology is promising to further revolutionize this processing leading to better and more accurate results. Current trends in information and communication technology are accelerating widespread use of web services in supporting a service-oriented architecture (SOA) consisting of services, their compositions, interactions, and management. Deep learning approaches can be applied to support the development of SOA-based solutions, leveraging the vast amount of data on web services currently available. On the other hand, SOA has mechanisms that can support the development of distributed, flexible, and reusable infrastructures for the use of deep learning. This paper presents a literature survey and discusses how SOA can be enabled by as well as facilitate the use of deep learning approaches in different types of environments for different levels of users.

Smart toys raises new concerns for parents and researchers. Children are more likely to share sensitive data and are unaware or rarely care about online risks. Parents play a relevant role in protecting the children, and parental control tools are necessary to take control and properly manage their child's data, according to their preferences. However, current tools neither meet parental needs nor are compliant with a standard for toy makers. We present a study of requirements for the development of a parental control tool for smart toys.

Process model discovery can be approached as an optimization problem, for which genetic algorithms have been used previously. However, the fitness functions used, which consider full log traces, have not been found adequate to discover unstructured processes. We propose a solution based on a local analysis of activity transitions, which proves effective for unstructured processes, most common in organizations. Our solution considers completeness and accuracy calculation for the fitness function.

The concept of children's toys has undergone many changes over the years, evolving from simple physical products to toys that add elements of the digital world using software and hardware components. This evolution has raised concerns about potential child privacy issues regarding the use of smart toys. A smart toy consists of a physical component connected to a computer system with online services to enhance the functionality of a traditional toy. This type of toy is still not widely known in Brazil and hence the opinion of Brazilian consumers regarding the acceptance of this technology when it is widespread in this country is not known yet. This paper aims to present the results of an evaluation about the perception of potential Brazilian consumers about issues involving children's privacy with the use of smart toys and whether this technology would be accepted when available in the Brazilian toy market. Semi-structured interviews were conducted with 14 participants producing data that were analyzed through the content analysis technique. The results showed concern on the part of parents when their children are connected to the internet. Moreover, parental control in smart toys would be well accepted by these potential consumers.

Techniques of functional size measurement are easily found in the literature, however, in the evaluation process of these techniques is not always approached which makes its validity questionable. The evaluation of the Business Process Point Analysis (BPPA) technique is the object of study of this article that aims to consistently evaluate its reproducibility and accuracy, identifying its limitations. BPPA was proposed so that project managers can systematically estimate the functional size of a business process automation project. Thus, this article presents the execution of a quasi-experiment realized with 58 graduate and postgraduate students, who measured the functional size of three business process models. The results of this experiment present the low reproducibility and accuracy of the technique as well as its limitations.

Few approaches allow assertive estimates for ticket completion time in incident management.The accuracy level of prediction models depends on how useful the used attributes are.Moreover, to effectively use computational resources, a canonical attribute subset must be used.This paper proposes two automated attribute selection methods to build prediction model.A filter method and two wrapper search techniques were combined with annotated transition systems to automate attribute selectors applied to a real-life incident management process.The results show that the wrapper method surpassed human experts' decision making.

In this paper, specific practices are proposed for better managing Cyber-physical Sytems (CPS) projects, called CPS-PMBOK approach.CPS-PMBOK is based on the Project Management Institute's PMBOK body of knowledge.It is focused on the integration, scope, human resource and stakeholder knowledge areas; which were chosen considering a systematic literature review conducted to identify the main CPS challenges.

Modern techniques widely applied in data mining, including computational intelligence and machine learning, have been fairly neglected in process mining. We conducted an exploratory study to use artificial neural networks to extract knowledge from an unstructured process in the distance learning domain. We discuss some possible benefits and limitations regarding the mining of unstructured processes. Results suggest that applying either classical process mining or modern data mining techniques would result in significant benefits for this domain. Our work helps to guide new studies related to the application of modern mining techniques in process mining.

The large amount of information in electronic contracts hampers their establishment due to high complexity. An approach inspired in software product line and based on feature modeling was proposed to make this process more systematic through information reuse and structuring. To allow the coverage assessment of this and other similar approaches, this paper presents a set of requirements that should be met by an approach in order to obtain success in supporting the process of Web services negotiation and contracting. By assessing the feature-based approach in relation to the proposed requirements, it was showed that the approach does not allow the price of services and of quality of services (QoS) attributes to be considered in the negotiation and included in the electronic contract. Thus, this paper also presents an extension of such approach in which prices and price types associated to Web services and QoS levels are applied. An extended toolkit prototype is also presented as well as an experimentation example of the proposed approach.

Business processes are important assets to demonstrate an organization competitiveness degree. Business Process Management (BPM) includes activities that enable the modeling, execution and analysis of business processes. Recently, the association of BPM, the service oriented computing and the Internet technology have broadened the scope of BPM from intra-organizational interchange of services to inter-organizational cooperation. This requires better support to the BPM framework including means to facilitate electronic contract establishment. BPM is one of the potential domains to which Product Line (PL) concepts and techniques can be applied. This paper presents an approach to support e-contract negotiation based on feature modeling. The approach is one of the steps of a broader research scope which aims at designing a framework to enable reuse throughout BPM activities. An example of the application of the proposed approach within the context of a Telecomm company is shown and results are discussed.

Electronic contracts describe inter-organizational business processes in terms of supply and consumption of electronic services (commonly Web services). The establishment of e-contracts in a particular business domain usually involves a set of well-defined common and variable properties. These properties are not fully exploited by the existing e-contract establishment approaches. Feature modeling is a software engineering technique that has been widely used for capturing and managing commonalities and variabilities of product families in the context of software product line. This paper presents a feature-based approach to support Web services e-contract (WS-contract) establishment. The approach aims at improving the information structure and reuse of WS-contracts, including the QoS attributes. Features are used to represent possible WS-contract elements in order to drive WS-contract template instantiation, thus acting as a configuration space manager. A toolkit named FeatureContract was developed to automatically support the proposed approach. A case study was carried out within the telecom context to show the approach feasibility.