Abstract
Enterprise Architecture (EA) is defined as a set of principles, methods, and models that support the design of organizational structures, expressing the different concerns of a company and its IT landscape, including processes, services, applications, and data. One role of EA management is to automate modeling tasks and maintain up-to-date EA models while reality changes. However, EA modeling still relies primarily on manual methods. Contributing to EA modeling automation, EA Mining is an approach that uses data mining techniques for EA modeling and management. It automatically captures existing information in operational databases to generate architectural models and views. This paper presents an ontology for EA Mining that focuses on generating architectural models from API gateway log files. An ontology defines the concepts and relationships among them to uniquely describe a domain of interest and specify the meaning of the terms. API Gateways are information technology components that serve as a facade between information systems and enterprise business partners. The ontology development methodology followed the SABiO process, whereas the Unified Foundational Ontology provided the foundations of the ontology and OntoUML, the ontology modeling language. An experiment in an e-commerce application scenario was conducted to evaluate the theoretical feasibility and applicability of the ontology. Automatic semantic and syntactic validation tools and semi-structured expert interviews were used to confirm the desired ontology properties. This study aims to contribute to the evolution of the knowledge base of EA Management.

Abstract
Organizations in competitive, regulated environments must enhance business processes for efficiency, quality, and compliance while minimizing risks and costs. Process automation solutions play a vital role in achieving these goals, though the variety of tool descriptions creates challenges for compatibility and interoperability. This hinders innovation and competitiveness. The adoption of standard specifications or widely accepted best practices for automation descriptions offers a solution. This research aims to identify a set of best practices to guide process-oriented organizations in evaluating their current automation practices, ensuring alignment and fostering improvements in business process automation.

Abstract
This research is inserted in the context of cybersecurity and specifically in the attribution of Advanced Persistent Threats (APT). The investigation that gave rise to the article studies the MICTIC Framework, validating it and proposing an extension to facilitate the assignment of APTs. In this research, we present the motivation for this proposal and its validation. Also, the MICTIC is presented layer by layer and the extended version is submitted for validation through a survey of around 50 university professors and researchers. Due to the fact the MICTIC by itself has not been validated, we decided to do that in conjunction with the extension proposal. Attribution is very important because lets you know who promoted or who carried out an APT-type attack. On the other hand, just the fact that there are sophisticated Attribution mechanisms can act as a deterrent to future attacks. This research contributes to greater ease in obtaining the Assignment of APTs and consequently in understanding how this type of cybercrime works. so much so that there are few studies on the Assignment of APTs. This study objectively contributes to achieving the APT attribution by combining technological and non-technological techniques. It contributes to achieving computer security environments since an APT Attribution is a high deterrent to an APT group getting uncovered and an Attribution being assigned to it. Typically, cybercriminals who have been identified have stopped operating, whereas the opposite is not true; unidentified actors persist with attacks for a long time. Thus, this study also contributes to the overall maintenance of cybersecurity.

Abstract
In today's rapidly evolving digital landscape, the substantial advance and rapid growth of data presents companies and their operations with a set of opportunities from different sources that can profoundly impact their competitiveness and success. The literature suggests that data can be considered a hidden weapon that fosters decision-making while determining a company's success in a rapidly changing market. Data are also used to support most organizational activities and decisions. As a result, information, effective data governance, and technology utilization will play a significant role in controlling and maximizing the value of enterprises. This article conducts an extensive methodological and systematic review of the data governance field, covering its key concepts, frameworks, and maturity assessment models. Our goal is to establish the current baseline of knowledge in this field while providing differentiated and unique insights, namely by exploring the relationship between data governance, data assurance, and digital forensics. By analyzing the existing literature, we seek to identify critical practices, challenges, and opportunities for improvement within the data governance discipline while providing organizations, practitioners, and scientists with the necessary knowledge and tools to guide them in the practical definition and application of data governance initiatives. © 2024 The Author(s)