Complex engineering projects are increasingly exposed to dynamic, cascading, and interdependent risks. Risk analysis in these projects has evolved, diverging into two dominant paradigms: data-driven approaches, which conceptualize risk as latent statistical patterns extracted from empirical data, and knowledge-driven approaches, which encode heuristics, expert judgment, and institutional knowledge. To critically examine these paradigms, this study conducts a systematic review, classifying modeling approaches into six categories and systematically analyzing their foundational assumptions, technical modeling characteristics, and application contexts. Through this synthesis, it is observed that data-driven approaches offer scalability and predictive power, yet key challenges include interpretability and the lack of standardized benchmark datasets. Knowledge-driven approaches, on the other hand, provide transparency and domain alignment, yet challenges remain in their adaptability, transferability and heavy reliance on experts. Despite their differences, both paradigms commonly rely on a static formulation of risk, which does not capture dynamic feedback, escalation, and evolving interdependencies. In response, the review outlines a future research agenda that emphasizes evidence-support structures (e.g. , knowledge graphs ), dynamic project representations (e.g. , digital twins with retrieval-augmented reasoning ), and temporal-systemic formulations (e.g. , dynamical systems ). Advancing these directions can enable the development of next-generation risk frameworks that are more adaptive, explainable, and transferable across complex project environments.

In a cloud environment, a cloud broker plays a vital role as an intermediary between cloud customers and providers, resolving issues and facilitating negotiations to balance customer preferences and provider profits. Over the past few years, numerous research articles have either directly or indirectly examined this area. Conducting a Systematic Mapping Study (SMS) on cloud brokerage is highly motivating as it offers a high-level overview of the research landscape, identifying trends, topics, gaps, and patterns within this dynamic and crucial field. This article presents an SMS conducted to categorize existing research, highlight underexplored areas, and map out the evolution and current state of cloud brokerage, providing valuable insights for researchers and practitioners. The SMS identified 91 relevant and reputable search spaces (journals and conferences) and 634 high-quality articles published from 2009 to 2022. Furthermore, we formulated and addressed eight significant research questions to clarify various aspects of the cloud broker field. The extracted information from the selected articles is included in a supplementary file, available online, offering valuable insights for research teams and developers interested in this domain.