The article examines how large‑language‑model AI can automate data‑warehouse modeling tasks—generating SQL, designing schemas, handling ETL, and tracing lineage—while highlighting current pain points, practical limitations, and four emerging trends that will reshape the role of data engineers over the next few years.
Domain-Driven Design (DDD) centers software architecture on business domains, breaking complex problems into clear subdomains, defining bounded contexts, ubiquitous language, and key building blocks such as entities, value objects, and aggregates, while guiding layered architecture and microservice decomposition for maintainable, collaborative systems.
This article builds a simple additive model to explain how interview order influences graduate exam scores through reference bias and evaluator fatigue, analyzes their combined impact on candidates of different ability levels, and offers practical advice for applicants despite institutional safeguards.
This article provides a comprehensive guide to data warehouse modeling, covering the distinction between entities and dimensions, how to define grain and merge scope, fact integration, the special role of the DWS layer, business module and subject‑area division, and practical solutions to common modeling pitfalls.
This article builds a step‑by‑step probability model of the 2026 Chinese Spring Festival train‑ticket rush, quantifying the impact of system capacity, user delay, pre‑filled orders, waiting‑list strategies and third‑party tools, and offers data‑driven tips to raise the chance of success.
This article offers a step‑by‑step, experience‑driven guide to data‑warehouse modeling, covering entity vs dimension, grain alignment, fact merging, DWS layer design, business module vs subject‑area mapping, and four typical pitfalls with concrete solutions to help practitioners build robust, business‑centric warehouses.
A recent video showing CO₂ levels rising from 880 ppm to over 2000 ppm inside a high‑speed train cabin sparked analysis that uses fluid‑mechanics and mass‑conservation modeling to explain why passengers often feel sleepy, comparing standards, physiological impacts, and ventilation efficiency.
This article applies mathematical modeling—including logistic regression, dynamic programming, game theory, survival analysis, and network theory—to quantify promotion dynamics in Chinese workplaces, revealing age constraints, sector‑specific weight differences, and optimal investment strategies across career stages.
This article explains UML class diagrams, covering their purpose, core elements, how classes, interfaces, and various relationships are represented, and provides a practical example of a simple music‑streaming system to illustrate the concepts.
This article explains the purpose and components of UML use case diagrams, illustrates how they capture system actors and interactions, and provides a concrete example of a mobile recharge app, while also inviting readers to follow the author's architecture notes for deeper learning.
This article explains how modeling—by abstracting key variables, defining boundaries, ensuring logical consistency, and testing against reality—helps us simplify complexity, make better decisions, and gain certainty in an uncertain, information‑overloaded world.
In this reflective case study, a first‑time undergraduate shares how competing in the 2025 China University Big Data Challenge—predicting Shanghai‑Shenzhen 300 index component movements—deepened his understanding of structured time‑series data processing, algorithm adaptability, iterative model optimization, and the broader value of data‑driven problem solving.
The article explores how over‑reliance on proven modeling routines can create superficial results, warns of the danger of formalism, and offers philosophical perspectives—phenomena vs. essence, dialectics, critical thinking, and abstraction—to deepen and rejuvenate mathematical modeling practices.
Model innovation thrives on six key strategies—problem transformation, variable reshaping, merging mechanisms with data, expanding objectives, multi‑agent modeling, and solvability design—each linking mathematical tools to real‑world needs to create more realistic, efficient, and robust decision‑support models.
This article explores the true nature of modeling as a way of abstracting, structuring, computing, and validating complex systems, outlines its four essential stages, discusses philosophical foundations, domain variations, and common pitfalls, and emphasizes its role as both a tool for understanding and shaping the world.
Exploring how the evolution of mathematical thought—from ancient Egyptian land surveys to Newtonian mechanics—mirrors the core steps of problem‑driven modeling, this article shows how historical breakthroughs illustrate the problem‑method‑result chain, offering modern modelers a rich source of examples and insights.
This guide presents a systematic approach to improve data warehouse modeling by enhancing model completeness, increasing reuse through unified dimensions and services, and enforcing naming and governance standards, supported by quantitative goals, practical strategies, and real‑world case studies.
The article explains how mathematical modeling compresses complex real‑world problems into clear, operable structures, enabling powerful inference through induction, deduction, and inversion, and demonstrates its impact with case studies in urban bike sharing and personalized education.
This article argues that successful programming and data‑science projects begin with clear problem definition, logical planning, and simple models before any code is written, emphasizing thinking over tools to ensure transparent, maintainable solutions.
The article explains how our habitual reliance on linear thinking produces systematic prediction errors across domains such as sports, learning, finance, biology, epidemiology, and urban growth, and it offers concrete non‑linear examples and strategies for adopting more accurate, system‑level models.
The article explores how stories function as simplified, abstract models of reality, comparing narrative structures to formal modeling, showing how extracting assumptions from tales enhances critical thinking, and arguing that giving models a story-like context makes them more relatable and persuasive.
The article analyzes why large, unstructured codebases hinder AI assistance, explains how PlantUML can provide clear, modular, and version‑controlled designs to focus AI context, and offers practical steps for integrating PlantUML with AI coding tools to improve productivity and code quality.
This article explains how turning everyday tasks into structured checklists serves as an accessible entry point to modeling, illustrating the process with travel packing, year‑end reviews, and house‑buying decisions, and outlining a three‑step thinking method to boost logical and quantitative problem‑solving.
This essay explains that every modeling choice rests on hidden assumptions that encode the modeler's values, showing how different assumptions lead to different perspectives, ethical trade‑offs, and ultimately influence real‑world decisions across domains such as education, insurance, and public policy.
Discover a practical five‑step framework for turning everyday observations into clear, testable models, learn how to quantify phenomena, map variable relationships, build input‑process‑output structures, match them to known patterns, and validate their predictive power for smarter decision‑making.
The article explores how people manipulate evaluation systems by redefining variables, adjusting weights, and shifting perspectives, turning losses into perceived wins, and reveals the psychological and statistical biases that create this illusion, urging more honest, multi‑dimensional, transparent modeling for genuine assessment.
This article explains how adopting a modeling mindset—structuring real‑world problems, quantifying variables, and applying simple optimization and scheduling techniques—can bring order to everyday tasks such as time management, consumer choices, interpersonal communication, health habits, and life planning.
This article surveys eleven open‑source diagram‑as‑code and visual modeling tools—including Graphviz, PlantUML, D2, Gaphor, Mermaid, ArchiMate, Modelio, Diagrams.net, Excalidraw, tldraw, and Cerbos PDP—detailing their key features, strengths, and suitability for different software architecture scenarios.
This article explains the core concepts and methods of sensitivity analysis, distinguishes local and global approaches, outlines the simple variation method, and demonstrates how to construct and interpret a tornado chart—complete with a real‑world example of market factors affecting sales—providing clear guidance for robust model evaluation.
The article reflects on the true nature of technology, describing it as a dynamic, recursive creative system that evolves through combinatorial innovation, recursion, and the capture of natural phenomena, and illustrates these mechanisms with examples from mathematics modeling and historical technological breakthroughs.
This article explains how to build a business model diagram for an SDK from a design perspective, covering the definition of business model diagrams, their purpose, expression methods, layered architecture, derivation principles, a six‑step modeling process, and evaluation criteria to ensure clarity, correctness, and extensibility.
This comprehensive guide walks readers through the entire system design process, covering problem definition, key concepts, system analysis, modeling techniques, architectural thinking, design tools, requirements gathering, domain‑driven and use‑case modeling, design principles, patterns, and practical architecture delivery.
The article argues that modeling is a process of understanding and exploration rather than mere problem‑solving, introduces a “27‑Method” framework spanning time, space, and analogy, advocates progressive, elegant models, emphasizes the equal partnership of data and models, and links modeling to philosophical principles.
This article explains how adopting a global perspective—coordinating system structure, multiscale analysis, and visualization—helps modelers avoid local optima, understand complex dynamics, and build comprehensive models that reveal core drivers and long‑term insights.
The 2024 HiMCM competition releases two modeling challenges—Problem A asks teams to develop a quantitative model for selecting Olympic sports for the 2032 Brisbane Games based on IOC criteria, while Problem B requires assessing the environmental impact of high‑performance computing and proposing sustainable solutions.
The article argues that effective mathematical modeling relies on uncovering a problem's essence with minimal assumptions, emphasizing simplicity, careful validation, and a three‑step process—understanding key variables, building a compact model, and rigorous testing—to deliver robust, practical solutions.
The article examines modern software development challenges, explains why thorough design and modeling are essential before coding, and outlines three key practices—business modeling, business process analysis, and system modeling—along with class analysis techniques to improve collaboration, reduce rework, and build robust architectures.
The article explains the main UML architecture diagram types—class, sequence, component, deployment, use‑case, state, and activity—outlines how to choose the appropriate diagram, and details when each should be applied during requirement analysis, high‑level design, and detailed design phases of software development.
This article explains why software engineers should perform design and modeling—such as business modeling, process analysis, and system modeling—before writing code, highlighting how these practices align development with business goals, improve collaboration, and reduce costly rework caused by changing requirements.
This article explains Domain-Driven Design (DDD), its key concepts such as bounded contexts, ubiquitous language, domain models and layered architecture, traces its evolution, highlights recent trends like Event Storming and CQRS, and draws parallels to technical team management practices for handling complexity, communication, iteration, and strategic focus.
Cellular automata, a discrete mathematical modeling tool built from simple cells, grids, states, neighborhoods, and rules, can simulate complex phenomena such as traffic flow, forest fires, and urban development, demonstrating how local interactions generate emergent patterns and offering insights for scientific research and practical applications.
This article explains Domain‑Driven Design (DDD) from its origins to practical modeling, compares it with MVC, outlines business and personal benefits, discusses drawbacks, details strategic and tactical concepts, and walks through a real‑world VOC product example using event‑storming and four‑color modeling.
Domain‑Driven Design aligns software architecture with business needs by establishing a shared language, clear bounded contexts, and strategic‑tactical patterns—entities, value objects, aggregates, services, and events—while using techniques such as Event Storming and Four‑Color Modeling to visualize domains, guide micro‑service boundaries, and produce maintainable, adaptable systems.
This comprehensive guide walks you through StarRocks production deployment, covering capacity planning, hardware specs, environment setup, table modeling, partitioning, bucketing, index selection, data import best practices, query optimization, monitoring, and resource‑group configuration, all backed by concrete SQL examples and configuration commands.
This article compares System Dynamics and Discrete Event Simulation, outlines their typical applications, lists commercial and open‑source tools for each method, and explains how these modeling approaches can improve decision‑making for complex systems.
This guide explains the seven core UML diagrams—class, sequence, component, deployment, use‑case, state, and activity—detailing when and how to use each across requirement analysis, architecture design, and detailed design to produce clear, actionable software design documentation.
This article explains how to model and implement a marketing reverse domain that handles coupon and asset recovery during refunds, using domain‑driven design, rule engines, and service layers, complete with state‑machine diagrams, code snippets, and practical mapping configurations.
Ximalaya’s deep advertising conversion optimization advances from shallow to deep billing models by integrating OCPC dual‑bidding, full‑channel data assistance, and real‑time crowd premium to overcome data sparsity, long conversion delays, and cold‑start challenges, boosting advertisers’ ROI while managing platform risk and guiding future ROI‑protected bidding.
Domain-Driven Design (DDD) is a methodology for tackling software complexity by aligning business, system, and organizational structures through strategic and tactical modeling, using techniques such as metaphors, layering, refinement, abstraction, bounded contexts, and a ubiquitous language, illustrated with real-world Tencent video system case studies.
This article explains Domain-Driven Design as a methodological approach for handling software complexity by aligning business, system, and organizational structures through modeling, detailing its core concepts, goals, key methods, and real‑world case studies from large‑scale systems.
This article explains what an optimization solver is, defines key terminology, walks through simple equation‑based examples like the chicken‑rabbit problem, expands to real‑world scenarios such as ad allocation and employee benefits, and outlines common solution methods and how to choose appropriate solvers.
This article explains the purpose, key elements, and appropriate usage scenarios of the main UML model diagrams—including class, sequence, component, deployment, use‑case, state, and activity diagrams—to help software engineers select and apply the right diagram at each stage of system design.
EventStorming is a rapid, lightweight group modeling technique that accelerates domain understanding by having cross‑functional teams collaboratively identify domain events, commands, aggregates, and bounded contexts, enabling faster, shared domain models and reducing reliance on individual DDD expertise.
This article outlines the end‑to‑end process of designing, building, and governing a data‑warehouse model for the new commercial WOS system, covering business research, data‑domain division, multi‑layer architecture, modeling methods, practical case studies, governance challenges, and improvement strategies.
This article provides a systematic overview of Domain-Driven Design, explaining its origins, core concepts, modeling principles, strategic and tactical design steps, service decomposition patterns, and practical recommendations for applying DDD in complex backend systems.
This comprehensive guide explains software methods—including business modeling, requirement analysis, domain modeling, UML relationships, and design principles—illustrating how systematic modeling improves product quality, reduces costs, and enhances engineers' professional growth.
This article details NetEase Yanxuan’s exploration and practice of a data warehouse modeling platform, covering background, current challenges, a comprehensive solution, step‑by‑step implementation, and the resulting improvements in model standardization, automation, and business value.
This article provides a systematic overview of Domain-Driven Design, explaining its origins, core concepts, modeling approaches, strategic and tactical design steps, service decomposition principles, and practical examples to help developers understand and apply DDD in complex software projects.
Platform engineering at scale requires unified self‑service abstractions, domain‑specific languages like KCL, divide‑and‑conquer monorepo structures, robust modeling and automation, and a collaborative culture, as demonstrated by Ant Group’s KusionStack implementation that supports thousands of projects with a sub‑one‑to‑nine platform‑to‑developer ratio.
The article explains Domain‑Driven Design as a method for managing software complexity, outlines its meta‑models and limitations, and demonstrates a full problem‑domain analysis for Bilibili’s OGV business—including stakeholder value‑demand analysis, process and use‑case modeling, scenario definition, ubiquitous language creation, and sub‑domain classification—while noting that implementation details will be covered later.
This article explains ArchiMate's basic views—including composition, support, collaboration, and implementation perspectives—by describing each viewpoint's purpose, focus, and example tables for composition, support, collaboration, and implementation views, as well as physical and layered views.
This article explains ArchiMate's basic views and the four categories of viewpoints—Composition, Support, Collaboration, and Implementation—detailing their focus areas, examples, and the specific technical and technology‑usage perspectives for enterprise architecture modeling.
This article explains ArchiMate basic views—including composition, support, collaboration, and implementation viewpoints—detailing their purpose, concerns, and the elements they address, and provides examples of application usage and implementation‑deployment views with stakeholder information.
The article explains the purpose, stakeholders, concerns, scope, and key elements of the Organization View and Business Process Collaboration View in ArchiMate, illustrating how these viewpoints model enterprise structures and business process dependencies for better design and decision‑making.
This guide introduces ArchiMate views, explains their role in modeling enterprise architecture, outlines the 23 official example viewpoints grouped into basic, motivation, strategy, and implementation & migration categories, and provides detailed descriptions of basic viewpoints such as composition, support, collaboration, and implementation.
The article explains how environment and location diagrams map applications to physical sites, defines key UML/BPMN EAP elements such as headquarters, site locations, servers, workstations, applications, interactive components, and class relationships, and illustrates these concepts with an Archimate diagram while also providing community links.
The article explains how introducing a well‑defined business identity into a mid‑platform enables finer‑grained extensibility, deployment, monitoring, and stability, and it shows practical modeling methods, open‑source examples, and a value‑stream‑based reference model for architects seeking to reduce cost, increase efficiency, and foster innovation.
The article explains the purpose and structure of application communication diagrams, describes SOA‑oriented component layering, introduces the UML/BPMN EAP profile and Archimate concepts, and provides a detailed list of application component types for enterprise architecture modeling.
This article explains the fundamentals of Domain‑Driven Design, distinguishes strategic from tactical design, outlines core DDD elements such as domains, aggregates and bounded contexts, and demonstrates how four‑color modeling can be applied to real‑world e‑commerce microservice architectures.
This article explains how architecture remains a vital part of agile software development, covering agile‑first approaches, lifecycle‑wide modeling, ownership roles, scaling strategies, demand‑driven design, multi‑view modeling, and practical tips for communicating and evolving architecture without over‑building.
This article explains what reference models are, outlines business, technical, and information reference model types, and provides practical guidance on representing them consistently using ArchiMate concepts within enterprise architecture to facilitate reuse, communication, and impact analysis.
The article argues that while mathematics is essential for modeling, solving real-world problems requires a blend of disciplines—biology, computation, writing, and more—highlighting that interdisciplinary collaboration, rather than any single field, is the true driver of effective solutions.
This article explains the fundamentals of statistics with a model‑centric approach, covering data collection, exploratory data analysis, descriptive statistics, visualization, and the three‑step Bayesian modeling process—including hypothesis formulation, model fitting, and evaluation—while emphasizing simplicity and practical programming tools such as Python.
This curated list groups recent model articles for students, covering variation models, time‑series prediction, machine‑learning techniques, deep‑learning architectures, and gray‑system forecasting, each with direct links to the original Chinese resources.
Data warehouse modeling organizes data into layered structures—ODS, DWD, DWS, and ADS—to improve performance, reduce costs, ensure data quality, enable traceability, simplify maintenance, and support both batch and real‑time analytics, while outlining best practices for ETL processes and schema design.
This curated list groups recent articles on change and prediction models, covering topics such as war dynamics, population, infectious diseases, differential equations, regression, time‑series analysis, and various machine‑learning techniques, providing quick access to each resource.
Reporting the process and outcomes of a modeling project, whether orally or in writing, helps you reflect on your work, sharpen communication abilities, and gain fresh perspectives that improve future modeling thinking.
This article offers a comprehensive post‑competition reflection on HiMCM 2021, covering modeling workflow, programming preparation, paper writing tips, teamwork practices, topic selection, paper quality levels, team case studies, essential modeling skills, and the six stages of the modeling process.
This article explains the importance of layering in data warehouse modeling, outlines the four ETL steps, describes common pitfalls, presents a typical technical stack, and details each warehouse layer (ODS, DWD, DWS, ADS) along with best‑practice naming conventions and implementation tips for big‑data environments.
This article explains what models are, distinguishes between physical (real) and theoretical (abstract) models, explores simulation and scale models, and details how mathematical models are constructed and used to represent real‑world phenomena.
This article presents an end‑to‑end intelligent testing framework that mines development‑stage risk dimensions, conducts admission risk assessment, extracts multi‑dimensional activity data such as coverage metrics, and applies model‑based risk evaluation to guide quality‑assurance decisions and improve release safety.
This article explains how statistics—through data collection, exploratory analysis, descriptive metrics, visualization, and model‑centric inference—provides a framework for understanding and predicting phenomena, emphasizing the role of programming (e.g., Python) and Bayesian modeling principles such as simplicity and the Occam razor.
When multivariate functions are hard to solve analytically, heuristic methods such as simulated annealing, genetic algorithms, particle swarm and bee algorithms—drawn from physical and biological processes—can approximate optimal solutions and even simulate complex phenomena like a student’s multidimensional development over time.
This article describes a systematic model that enumerates all possible city‑county home page switching scenarios in the 58App, identifies conflicting popup configurations, and proposes a concise feature‑flag protocol to eliminate popup conflicts while reducing client‑side logic and operational overhead.
This article explains how the traditional linear regression assumption can be relaxed by using link functions to transform nonlinear outputs into linear responses, enabling more flexible generalized linear models for probabilities and count data.
This guide explains the theory behind Vector Autoregression (VAR) models, outlines the complete modeling workflow—including data preparation, stationarity and cointegration testing, lag order selection, parameter estimation, stability diagnostics, and impulse‑response and variance‑decomposition analysis—and provides a full Python implementation with code examples.
This article outlines Snowball's data warehouse background, compares major modeling approaches such as ER, dimensional, DataVault and Anchor models, describes the current challenges of their dimensional model, and details the OneData methodology—including OneModel, OneID, and OneService—along with its practical implementation, results, and future plans.
This article explains the three main types of stationary time‑series models—AR, MA, and ARMA—detailing their definitions, back‑shift operator notation, polynomial representations, and the essential stationarity and invertibility conditions required for valid modeling.
This article explains the fundamentals of software design documentation, showing how UML modeling—class, sequence, component, deployment, use‑case, state, and activity diagrams—captures domain problems and system structure across requirement, high‑level, and detailed design phases, enabling architects to communicate intent to diverse stakeholders.
This curated list groups recent explanatory and simulation model articles—covering clustering analysis, linear regression, queueing theory, Markov chains, and Monte Carlo methods—into easy-to-navigate sections for quick reference, helping students and practitioners locate relevant resources efficiently.
This curated list groups recent articles on change and predictive models, covering topics such as war dynamics, population, epidemic spread, differential equations, regression, time‑series analysis, machine learning classifiers, and grey‑prediction techniques, providing students with ready references for diverse modeling approaches.
This article compiles and categorizes recent model‑related publications, offering a comprehensive list of evaluation‑model resources—including concepts, preprocessing techniques, weighting methods, and various algorithms—and optimization‑model references covering linear and integer programming, graph theory, network flows, and meta‑heuristics.
This article models a 60 kg human free‑falling from 10 km, analyzes forces such as gravity, buoyancy and air resistance, computes minimum and maximum terminal velocities for different body orientations, and estimates that roughly 6 m of loosely packed cotton or feather‑down would be required to prevent injury, with larger safety margins suggesting 10 m or more.
This article explains how software architects use modeling techniques and UML diagrams to create clear, stakeholder‑focused design documents that capture domain problems, system structure, and implementation details across requirement analysis, conceptual design, and detailed design phases.
This article introduces Grey System Theory, explains its origins, core concepts of partial information, advantages over black‑box models, data accumulation/reduction techniques, and demonstrates a Python case study that improves forecasting accuracy for short‑term exponential trends.
This article explains how a self‑built performance testing platform leverages data‑statistical models, such as scoring and interval models based on normal distribution and percentile analysis, to quantify web‑page metrics, guide automated and semi‑automated optimizations, and integrate with CI/CD workflows for frontend development.
This article explains the three-step process for constructing differential-equation models, introduces the micro-element analysis method, and demonstrates its application to a hemispherical water-outflow problem, including derivation of the governing equation, solution via separation of variables, and a Python implementation using SymPy.
This article explains how to formulate and solve the 2020 B biodiversity funding problem as an optimization model, covering decision variables, objective functions, constraints, and common solution strategies such as multi‑objective conversion and algorithm selection.
This article outlines the HiMCM 2020 Problem B, which challenges teams to develop models for efficiently allocating limited funds to protect endangered Florida plant species over long time horizons, detailing objectives, data fields, and suggested analytical approaches.
This guide outlines how to build and explain a HiMCM 2020 A problem model by creating diverse fictional personas, generating appropriate data, interpreting results, and leveraging charts such as radar, bar, and tables to clearly demonstrate model usefulness and adaptability.
