The problem frame method typically uses domain knowledge in order to demonstrate that a software system can satisfy the requirements of stakeholders by specifying how machine relates to stakeholders' problems. Qualitatively, satisfiability discourse can guide a software engineer to make early decisions on what the right solution is to the right problem. However, mobile apps deployed to app stores often not only need to accommodate millions of individual users whose requirements have subtle differences, but also may change at runtime under varying application contexts. Requirements of such apps can no longer be analyzed qualitatively to cover all situations. Big data analysis through deep learning has been increasingly adopted in practice to replace deep requirements analysis. Although effective in making statistically sound decisions, the conclusions of pure big data analysis are merely a set of unexplainable parameters, which cannot be used to show that individual users' requirements are satisfied, nor can they reliably validate the trustworthiness and dependability in terms of security and privacy. After all, training with more datasets could only improve statistical significance, but cannot prevent software systems from the malicious exploitation of outliers. This paper attempts to follow Jackson's teaching of abstract goal behaviors as intermediate between requirements and software domains, and proposes an algebraic approach to analyzing the consequences of probabilistic software behavior models, so as to circumvent some blind spots of purely data-driven approaches. Through examples in security and privacy areas, the challenges and limitations to big data software requirement analysis are discussed.