The pre-Hadoop era of data processing represents a pivotal phase in the evolution of modern computational ecosystems, setting the stage for the Big Data revolution. For decades, organizations across industries-from aviation and manufacturing to departmental operationsinvested heavily in era-specific data processing solutions to derive actionable insights critical for business growth and success. Progressing from manual file systems to relational databases and eventually data warehouses, these systems addressed fundamental challenges in data storage, processing, and retrieval. However, they faced persistent barriers in scalability, heterogeneity, and real-time analytics, often leading to the phenomenon of information overload. This paper examines the genesis of Big Data concepts, highlighting the descriptive "Vs" of volume, velocity, variety, and veracity that defined both the pre-Hadoop systems and the modern era. It explores the limitations of traditional systems, their impact on strategic decision-making, and mitigation efforts through investments in OLAP technologies. Furthermore, the study emphasizes the enduring influence of legacy systems on emerging solutions like Hadoop, AI-integrated analytics, edge computing, and distributed architectures. By linking historical paradigms to contemporary advancements, this discussion provides insights into the dynamic trajectory of data engineering and trends shaping the future of information processing. .

The rapid evolution of software development practices, coupled with the increasing prominence of open-source software (OSS) projects, has positioned versioning systems as invaluable repositories of information for analysis. This paper delves into the factors that shape software development activities by examining documented methodologies and tools used to mine and analyze OSS repositories. Through a detailed review of approaches addressing developer contributions, code modifications, and collaborative workflows, we identify key influences on productivity, code quality, and team dynamics. Our exploration includes an analysis of analytics techniques and tools, such as CVSSearch and CVSAnalY, that facilitate a deeper understanding of developer behaviour and project evolution. Furthermore, we discuss the implications of these insights for enhancing software engineering practices across both OSS and proprietary environments. This paper provides a conceptual framework that connects theoretical insights with practical approaches, offering actionable strategies to improve processes, foster collaboration, and support learning in software development. Ultimately, the findings hold potential value not only for practitioners aiming to optimize workflows but also for educators leveraging OSS environments to teach and evaluate software engineering concepts.

The increasing prevalence of digital learning platforms and collaborative open-source software development has resulted in abundant data capturing learning behaviors. This paper investigates the intersection of process mining and learning analytics to understand how students and contributors engage in Massive Open Online Courses (MOOCs) and Free/Libre Open-Source Software (FLOSS) communities. Building on a diverse set of studies, we explore methods to extract, analyze, and interpret learning patterns from event logs, with a focus on bridging methodologies across these domains. We also incorporate perspectives from broader fields of data analytics and educational research to propose an integrated framework for optimizing engagement and retention in diverse online learning environments. By leveraging advanced process discovery, conformance checking, and machine learning techniques, this study offers novel insights into educational data mining and collaborative learning practices. Our findings have implications for the design of adaptive learning systems and the future of digital education.

The increasing integration of artificial intelligence (AI), learning analytics, and process mining has paved the way for enhancing the interpretability of educational data. This paper explores how these emerging technologies can synergize to improve the transparency and effectiveness of learning analytics, particularly in Massive Open Online Courses (MOOCs) and Free/Libre Open-Source Software (FLOSS) communities. A major challenge lies in the "blackbox" nature of AI models, which can hinder trust and adoption, especially in educational contexts. To address this, we delve into the role of explainable AI (XAI) in providing insights into learner behaviors while retaining the predictive power of complex machine learning algorithms. By reviewing state-of-the-art studies and synthesizing methodologies from process mining, AI, and learning analytics, this paper seeks to present a comprehensive framework for leveraging these technologies to make educational data more accessible and interpretable. The findings point toward future directions for combining process discovery techniques, explainability approaches, and educational data mining to create adaptive and transparent learning environments.

Process mining has emerged as a vital tool for analyzing and optimizing workflows in various domains. However, traditional process mining approaches often lack transparency and interpretability, limiting their effectiveness in addressing complex decision-making and process variations. Explainable Process Mining (XPM) bridges this gap by integrating principles of Explainable Artificial Intelligence (XAI) into process mining techniques, providing clearer insights into the "why" and "how" of process behaviors. This paper proposes a novel framework for XPM, emphasizing AI transparency principles. Using learning analytics in Free/Libre Open Source Software (FLOSS) repositories as a use case, the study highlights the potential of XPM to uncover decision-making pathways and enhance stakeholder understanding of learning processes. Building on Mukala et al.'s foundational work (2015-2024), the framework is applied to FLOSS event logs, offering actionable insights for educators and community organizers to optimize learning environments. The contributions include an interpretable process mining approach, extended learning process models, and practical guidelines for fostering effective knowledge exchange in collaborative settings.

The growing reliance on digital systems for learning has led to an abundance of data that captures both the learning contexts and underlying processes. Learning Analytics (LA), an emerging field, utilizes innovative tools to extract meaningful insights from this data. Digital platforms like Massive Open Online Courses (MOOCs) and Free/Libre Open-Source Software (FLOSS) repositories offer unique perspectives on individual and collaborative learning. This paper explores personal constructivism in MOOCs by analyzing students' learning behaviors and their impact on performance, while also examining social constructivism in FLOSS repositories to better understand collaborative learning dynamics. By applying process mining techniques, specifically conformance alignments, and cluster analysis, we investigate how patterns of student engagement in MOOCs correlate with their learning outcomes. Our findings show that consistent engagement with videos, following a specific sequence, and minimizing time gaps between video views leads to higher performance, while irregular engagement often results in suboptimal outcomes. Additionally, we explore FLOSS repositories to examine how community interactions-such as discussions and collaborative contributions-foster knowledge construction and social learning. This study integrates insights from both personal and social constructivism to provide a comprehensive understanding of learning behaviors in MOOCs and FLOSS environments. The findings offer valuable implications for course design and community management strategies aimed at optimizing learning experiences and addressing diverse needs in digital education.