Focused on big data technology research, exploring the Big Data era | [email protected]
This article demonstrates a step‑by‑step workflow for constructing a churn prediction model using R in Discover, covering data loading, preprocessing, feature extraction, labeling, random‑forest training, prediction, and evaluation to help businesses proactively retain high‑value customers.
This guide demonstrates how to train a KMeans model with Transwarp Sophon and deploy it in Slipstream for real‑time streaming predictions on Kafka data, covering model export, stream creation, SQL‑based inference, and result persistence.
This guide explains the fundamentals of association‑rule mining, introduces support, confidence and lift metrics, and demonstrates step‑by‑step how to use the discoverR R package with Apriori and FP‑Growth algorithms to extract and visualize recommendation rules from the classic groceries dataset.
This tutorial demonstrates how to create a table with a Chinese analyzer, insert multilingual records, and leverage Search SQL's CONTAINS, NEAR, and FUZZY operators to perform precise, high‑performance full‑text searches beyond simple LIKE patterns.
This article explains how Search SQL enables easy full‑text search on Transwarp Search by using standard SQL syntax, covering tokenization, analyzer configuration, CONTAINS queries, and advanced NEAR and FUZZY operators to improve performance and query semantics.
Guardian 5.0 transforms big‑data security by introducing a standalone service, an enhanced ARBAC model, fine‑grained permission and quota management, visual configuration, unified LDAP/Kerberos authentication, and simplified operations, delivering higher flexibility, availability, and efficiency for enterprise data protection.
Guardian 5.0 introduces a comprehensive security solution for the TDH big‑data platform, featuring LDAP/Kerberos authentication, unified ARBAC authorization, quota management, and multi‑layer architecture, while enhancing data protection and simplifying operations across cloud‑based deployments.
This article walks through Rubik's OLAP cube reduction techniques—including aggregation groups, required, combined, derived, hierarchical, and partial cubes—by designing and implementing buyers and suppliers cubes with six tables, demonstrating performance gains through pre‑computed queries and SQL examples.
This article explains why raw OLAP Cubes consume exponential storage, then details six practical dimensionality‑reduction methods—Aggregation Group, Mandatory Dimension, Joint Dimension, Derived Dimension, Hierarchy Dimension, and Partial Cube—showing how each can dramatically shrink materialized tables while preserving query performance.
Waterdrop is a cross‑platform SQL IDE and database management suite that offers a navigator, editor, executor, and data viewer, plus import/export and stored‑procedure testing tools, enabling developers and DBAs to efficiently browse metadata, write and format SQL, execute queries, edit data, and manage licenses.
