need: [[need analysis]] based on [[gal need analysis]] sol: [[🧠🤜1331need_sol]] implemented by: [[iter(lift, project)]] ### 📜2🗄️📽️ 1. using the mapping function from 📜2🗄️ paper to table you understood in 2, please give me a 🗄️table for the paper heimann93_challengerOrgStr.pdf. just as a reference, nasa_ab.png is my reflection of the heimann93_challenger paper. 2. based on the two tables which fully captures the paper, i wish to project them on the hyperplane generated by angie's math model with the goal of expanding its generality. 3. based on the two tables which fully captures the paper, i wish to **📽️**project them on the hyperplane generated by angie's math model with the goal of expanding its generality. --- ## usecase **📽️PROJECT** to normal Although database normalization and the normal distribution emerged from distinct historic contexts—Codd’s 1970s work on relational databases to reduce data redundancy versus 19th-century mathematicians like Gauss and Laplace studying probability and errors—they both share an underlying drive to **reduce complexity**. Database normalization refines a sprawling schema into smaller, related tables, clarifying relationships and minimizing anomalies; the normal distribution condenses a myriad of random phenomena into a single, elegant bell curve shaped by the central limit theorem. Despite their independent evolutions, each concept underscores a universal principle of simplification—whether it’s structuring data more coherently or summarizing countless random variables under one unifying probability model. --- **⛓️(📽️, 🏋️)Mediate** In practice, the interplay of **PROJECT** and **LIFT** iterates between **individual** and **collective** perspectives. At the **individual** level, we log a more permissive “value statistic,” where a low bar (false positive ≫ false negative) drives quick pattern recognition and tentative acceptance. At the **societal** level, we adopt a high bar (false positive ≪ false negative) to prioritize robust validation. This cyclical negotiation—toggling between local vantage and communal standards—helps refine which insights deserve projection into simpler models and which nuances must be lifted up for deeper examination. --- **🏋️LIFT** to abnormal In the **LIFT** phase, we **expand** or **reinterpret** a model to accommodate new constraints or anomalies uncovered by that ongoing local-to-global dialogue. Whether adding tables and constraints in database normalization or recognizing heavier tails in statistical distributions, **lifting** means stepping beyond a neat snapshot to capture subtleties we once overlooked. From there, a subsequent **project** cycle can compress these enriched insights back into a more cohesive framework—balancing clarity with fidelity, one iteration at a time.