Management

They occur in theaters, hallways, reception areas, and festival programs. They accumulate into economic outcomes without being measured directly.The result is an industry that frequently makes decisions with less information than it could possess.

You know how to build classes in Protégé. But do you know when to attach meaning — and when to wait?Volume 4 of "Mastering Ontology Engineering" introduces SKDL: the Semantic Knowledge Development Lifecycle. Seven stages. One discipline. Zero guesswork.From Aristotle to Description Logic. From taxonomy to executable knowledge.This is where pizza -> philosophy — and philosophy -> engineering.

Before you spend time and money fixing a problem, make sure you understand what is causing it.A practical method for finding the real problem before choosing the solution. Learn to examine evidence, test competing explanations, identify the cause worth addressing, and check whether your response works.

A practical eight-stage method for making better decisions when information is incomplete. Learn to separate facts from assumptions, compare realistic options, test key uncertainties, record your reasoning and review the result.

A detailed plan does not always lead to a better decision. Learn how to separate facts from assumptions, identify the unknowns that matter, and test your beliefs before taking action. This short, practical book includes a decision-review worksheet you can use in your own life or work.

Master Operations Research Through Concepts, Techniques, and Practical Problem-SolvingLearn Linear Programming, Simplex Methods, Transportation, Assignment, TSP, Network Analysis, CPM, PERT, Queuing Theory, Game Theory, and Sequencing through structured explanations, numerical examples, and step-by-step problem-solving techniques.

把“我做成过”变成别人能够追问、复核、迁移、更正和长期维护的实践成果。

Bridge the critical gap between Agile delivery and bottom-line business performance. Agile Constraints Management introduces a revolutionary framework that merges Agile adaptability with the Theory of Constraints and Throughput Economics. Stop chasing team-level "velocity" and start driving measurable economic throughput by pinpointing hidden bottlenecks, forecasting with scientific precision, and managing work through outcome-focused MOVEs.

Fragment 1 — Chapter 1, "Introduction": why AI governance is a governance question, not an engineering oneWhen an organization first faces a decision to deploy an AI system — whether a credit-scoring model, an automated resume-screening tool, or a customer-support chatbot — the most common governance mistake is to treat that decision like an ordinary IT project: define the requirements, select a vendor, implement, and hand the system over to operations. Artificial intelligence technologies do rely on software and computing infrastructure, and in that sense they resemble any other IT initiative. But they differ in how they generate risk, and that difference calls for a distinct governance approach rather than a simple extension of familiar project management.Risk-oriented AI governance starts from a different premise: before discussing rollout timelines, budgets, or functional requirements, an organization has to answer the question "what adverse consequences could this system cause, for whom, with what likelihood, and how severe would they be" — and, alongside it, "how much of that harm are we willing to tolerate for the expected benefit." Fragment 2 — Chapter 3, "Trustworthy AI: Seven Characteristics of Trust": why explainability and interpretability are not synonymsConsider an AI system that automatically sorts incoming support tickets by priority. Explainability answers the "how" question: which features of the incoming text (keywords, tone, customer history) contributed to the computed priority score and with what weight — a technical account of the computation mechanism. Interpretability answers the "why" question for this particular ticket in the context of the system's business purpose — that is, whether a high priority score means "this customer is losing money right now" or "this customer is statistically likely to cancel," and whether that meaning matches how a support agent should act on it. The two characteristics support each other but serve different governance needs: explainability matters more to the engineer debugging the model, interpretability matters more to the agent or manager who must act on the system's output without understanding its internal mechanics. Fragment 3 — Chapter 18, "The Running Case": where ISO and NIST illuminate each other's blind spotsPrecisely because NIST explicitly requires, in its own standalone subcategory, a designated authority to deactivate a system, and no direct equivalent exists in Annex A of ISO, an organization that implements only ISO/IEC 42001 without checking it in parallel against the AI RMF risks missing this requirement altogether — it dissolves between the adjacent, but not identical, controls A.6.2.5 (release criteria) and A.3.2 (roles and responsibilities). This is a compelling, concrete example of the argument the book already made back in Chapter 1: the two frameworks do not compete but mutually illuminate each other's blind spots. Fragment 4 — Chapter 18, the book's closing paragraphAI risk management, as this book has shown it, never concludes with a signed document. It concludes — and immediately begins again — with the next MAP cycle, the next internal audit, the next policy review, the next model version, which has to be brought into operation just as thoroughly documented, traceable, and responsibly managed as the one before it.

La IA no toma decisiones: traduce el criterio del profesional que conoce su organización. Primero es el criterio. Hasta después es la herramienta.

You know how to build classes in Protégé. But do you know when to attach meaning — and when to wait?Volume 4 of "Mastering Ontology Engineering" introduces SKDL: the Semantic Knowledge Development Lifecycle. Seven stages. One discipline. Zero guesswork.From Aristotle to Description Logic. From taxonomy to executable knowledge.This is where pizza -> philosophy — and philosophy -> engineering.

Evaluating a novel and original approach to a problem involves many types or organizational structures, forecasting and communication styles.

In a world where every byte has an impact, Sustainable IT, the Right Way turns digital sustainability from slogan into daily practice. No abstract theory: numbers, monitoring, OKRs, and a real action plan. Concrete examples, ready-to-use tools, and the Green Software Foundation's best practices, in operational language for those who want to make a difference today.

Every news feed is full of AI buzzwords: Transformers, tokens, embeddings, context windows, hallucinations, objective functions. Yet most explanations are either dense academic textbooks or empty marketing fluff. ☕ Coffee Break AI is your practical guide to AI. Written in plain English with warm real-world analogies. It breaks down the core mechanisms of AI into 40 bite-sized chapters.

Designing software is only the beginning.The true challenge lies in transforming well-designed systems into reliable, scalable, secure, and maintainable solutions that solve real-world business problems.That is precisely where System Analysis and Design: Concepts, Techniques, and Real-World Applications (Vol-II) begins.