The article walks through five iterative experiments using MySQL HeatWave AutoML on a golf league dataset, showing how data quality, feature selection, and over‑fitting affect predictive accuracy and emphasizing that high‑quality data is essential for reliable AI models.
The MagicScaler framework, introduced by Alibaba Cloud’s big‑data engineering team and collaborators, combines a multi‑scale attention Gaussian process predictor with an uncertainty‑aware elastic scaling decision engine, delivering significantly higher quality‑of‑service and lower operational costs than traditional autoscaling methods, as demonstrated on real MaxCompute workloads.
A VLDB 2023 Industrial Track paper by Alibaba Cloud introduces MagicScaler, a predictive autoscaling framework that combines multi‑scale attention Gaussian processes with uncertainty‑aware optimization to deliver high QoS at reduced cost for cloud resources.
By leveraging the Yugong Platform’s predictive control—combining Prophet‑based threshold forecasts, custom real‑time bandwidth models, and a token‑bucket mechanism—to smooth peaks and fill valleys, enterprises can dramatically improve CDN bandwidth utilization, automate adjustments, and substantially lower peak‑based billing costs.
This article explains the essential characteristics, primary functions, and various classification schemes of mathematical models, highlighting their abstraction, accuracy, predictive power, and how they simplify, simulate, and apply to real‑world phenomena across deterministic, stochastic, fuzzy, and abrupt domains.
This article details a practical AI-driven workflow for a turn‑based card game, covering problem background, data modeling with a DNN victory‑prediction network, reinforcement‑learning‑based data generation, and a genetic‑algorithm search to identify the strongest and weakest team compositions.
This article presents a cloud-native, end‑to‑end autoscaling solution that integrates traffic forecasting, CPU utilization meta‑prediction, and a reinforcement‑learning‑based scaling decision module into a fully differentiable system, achieving higher resource utilization and cost efficiency as demonstrated by ACM SIGKDD 2022 research.
The GoodFuture International Algorithm Team, together with Jinan University Guangdong Smart Education Research Institute, distinguished themselves among 95 global teams in the 5th Educational Data Mining in Computer Science Education Workshop, securing a champion title in one task and a runner‑up in another, showcasing advanced AI‑driven predictive and recommendation techniques for intelligent student assessment.
The article analyzes why user profiling initiatives frequently collapse, highlighting surface mistakes such as confusing past behavior with future predictions, mixing behavior with motivation, and mistaking correlation for causation, while also exposing deeper issues like unrealistic business expectations, over‑reliance on static tags, and insufficient predictive modeling and causal analysis.
This article explains the concept of User Lifetime Value (LTV), its importance in modern business, the stages of a user’s lifecycle, key characteristics of LTV, the problems it can solve, and various analytical methods—from simple arithmetic formulas to machine‑learning and neural‑network models—for estimating LTV.
This article outlines eight practical big‑data analytics use cases—from product recommendation and pricing to churn prediction—and introduces fundamental machine‑learning algorithms such as linear regression, decision trees, SVM, and random forests that power these applications.
This article explains how to design, construct, and evaluate an Action Model that quantifies the impact of business decisions on long‑term profit, covering variable selection, model assumptions, algorithm choices, data challenges, and practical evaluation methods such as offline metrics and A/B testing.
In a detailed Q&A session, Minsheng Bank’s database specialist shares 16 practical insights on intelligent database operations, covering data storage choices, monitoring tools like Zabbix and Prometheus, anomaly detection, prediction modeling, SQL metric collection, log analysis, and the evolving role of DBAs in the era of AI‑driven ops.
The 2019 JD Cup Tianjin Innovation and Entrepreneurship Challenge attracted 1,629 teams and 2,927 participants worldwide, featuring a high‑profile big‑data forecasting task on user purchases that received 3,917 submissions and showcased JD's data‑driven marketing solutions and AI‑powered predictive models.
The talk introduces Alibaba’s Fish Brain platform, an AI‑powered decision‑support system for entertainment that combines a three‑layer data‑model, AI‑processed basic data, and application models, leveraging NLP, computer‑vision, custom embeddings, loss functions and predictive hybrid networks to analyze content, user behavior, and forecast performance.
An in‑depth case study of a brand’s Uni‑Marketing strategy reveals how AI‑driven audience selection, multi‑direction diffusion, and custom predictive models increased the O→IPL conversion rate by 47%, detailing the data pipelines, feature engineering, model training, evaluation metrics, and practical challenges.
This article summarizes Wu Xiaoguang's talk at Gdevops 2017, detailing how Vipshop integrates data processing, analysis, and mining technologies—such as Flume, Kafka, Spark, and custom scheduling—to improve operational decision‑making, performance monitoring, root‑cause analysis, and predictive modeling across its e‑commerce platform.
The Meituan‑Dianping tech salon detailed how food‑delivery big data drives full‑chain marketing, using RFM‑based user segmentation, rich demographic and behavior profiles, churn‑prediction and survival models, and scenario‑driven expansion tactics to acquire, retain, and grow customers across the order lifecycle.
This article presents nine fundamental laws of data mining—covering goals, knowledge, preparation, experimentation, patterns, insight, prediction, value, and change—explaining how business objectives and domain expertise drive each stage of the CRISP‑DM process and why technical metrics alone cannot guarantee success.
This article explains how game developers and marketers use internal, platform, and external data to forecast product performance, optimize user acquisition, predict market trends, model retention curves, and make informed budgeting decisions throughout a game's lifecycle.
