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Recently, the book "Multi-Dimensional Database," co-authored by Dr. Qiao Guibin and authoritative academic experts in the field, was published and released by Science Press. As an important achievement in the "Information Science and Technology Academic Works Series," this book systematically organizes the complete knowledge framework of multi-dimensional databases, from fundamental theories to engineering practices. Possessing both academic rigor and industrial practicality, it serves as an authoritative reference work in the fields of multi-dimensional databases, data warehousing, OLAP, and enterprise data analysis.
Dr. Qiao Guibin, one of the authors of this book, is an expert in the field of multi-dimensional databases and big data software, and a co-founder of Beijing Intcube Technology Co., Ltd. Dr. Qiao previously served as a Principal Software Engineer at Oracle Corporation, possessing years of experience in core database technology research and development and industry applications. He is a pioneer in the independent research and development of domestic multi-dimensional databases and graph databases, has led multiple invention patents in the database field, and holds profound expertise and unique insights into the engineering implementation and industrial application of multi-dimensional databases.
Development and Application of Multi-Dimensional Databases
In recent years, with the rapid growth in demand for enterprise data analysis, traditional ROLAP analysis systems based on relational databases have gradually revealed technical limitations when handling complex analytical queries. The demand for multi-dimensional databases based on MOLAP emerged accordingly. Multi-dimensional databases adopt a multi-dimensional data cube structure, pre-calculating and aggregating data by dimensions (such as time, region, product) and measures (such as sales amount, quantity). This forms a multi-dimensional data model that supports complex multi-dimensional operations and possesses efficient query and analytical computing performance, becoming the core technological foundation of current enterprise management systems.
Currently, multi-dimensional databases are widely applied in analytical enterprise management systems such as Enterprise Performance Management (EPM). Through functions like pre-computation and flexible drill-down, multi-dimensional databases significantly enhance the query efficiency and analytical depth of various management systems, helping enterprises achieve intelligent management in operational monitoring, resource optimization, and strategic decision-making.
Domestic Multi-Dimensional Databases: From Technological Breakthrough to Management Empowerment
Although multi-dimensional database technology originated in the West, it has been applied domestically for nearly two decades. In 2010, Cai Zhihong and Dr. Qiao Guibin, who had previously served on the core R&D team at Hyperion, returned to China and successfully developed the country's first multi-dimensional database system with completely independent intellectual property rights, Intcube OLAP. This filled a gap in the field of domestic independent multi-dimensional database technology. Based on Intcube OLAP, Intcube further launched its Enterprise Performance Management system (Intcube EPM), becoming a typical representative of China's achievement in achieving independent and controllable core technology in the field of enterprise management software.
Intcube EPM is a comprehensive intelligent management platform for enterprises, integrating functions such as comprehensive budgeting, business-finance integration, financial analysis, and operational decision-making. Its core functionalities include multiple modules for comprehensive budget management, consolidated reporting, budget execution control, financial analysis, and operational analysis. Each module can be used independently or in any combination according to the enterprise's different management needs, opening up the entire chain from strategic planning to implementation execution, fully meeting the enterprise's requirements for intelligent management. Currently, Intcube has successfully deployed EPM management solutions based on its multi-dimensional database platform for hundreds of domestic enterprises and institutions, fully verifying the exceptional reliability, security, and stability of the domestic multi-dimensional database in the fields of EPM and data governance.
After more than a decade of continuous iteration, the latest generation of the Intcube EPM product uses the domestic multi-dimensional database GmmCube as its underlying technological foundation. GmmCube features several functional breakthroughs in performance, architecture, functionality, and deployment. It relies on its independently developed OLAP engine, Intcube Booster, to provide core computing power, fully supporting the industry-standard MDX syntax and capable of processing massive and complex multi-dimensional data model calculations at extremely high speeds, providing powerful support for enterprise-level analysis.
● In-Memory Operation and Intelligent Optimization
GmmCube achieves multiple innovations in performance optimization. It supports a full in-memory operation mode, where the database runs entirely in memory. By eliminating disk I/O latency, it achieves extremely low latency and high throughput, meeting the demands of complex scenarios such as real-time data analysis and high-concurrency access. Simultaneously, the intelligent dynamic range calculation function minimizes the dynamic calculation scope, significantly improving computational performance. Automatically optimizing storage strategies for dense and sparse dimensions effectively reduces memory footprint, ensuring the system remains efficient and stable even under complex queries.
● Cloud-Native Architecture: Elastic Scalability and Massive Processing
GmmCube adopts a cloud storage and cloud computing model, supporting the full utilization of memory resources on a single computer and the ability to split a single multi-dimensional database into multiple sub-databases for distributed operation on a cloud platform. Through coordinated data exchange between a central server and supporting servers, it achieves efficient processing and calculation of massive data. This architectural design enables it to elastically schedule resources, easily handling budgeting, analysis, and forecasting tasks involving enterprise-scale big data volumes.
● Functional Integration: Full-Stack Platform and Multi-Domain Empowerment
GmmCube deeply integrates technologies such as data warehousing, data mining, and OLAP, providing a unified web-based management platform. It supports data visualization on PC, mobile, and large screens, covering multiple business areas including human resources, operations, and finance. From strategy formulation, plan decomposition to budget preparation, submission approval, and execution analysis, GmmCube supports various financial and business analysis needs with its powerful multi-dimensional data capabilities, helping enterprises achieve a high degree of unity between strategy and planning budgets, effectively optimizing resource allocation, controlling costs, and improving operations.
● Flexible Deployment: On-Premises and SaaS Dual Modes
GmmCube supports two deployment modes: on-premises private deployment and SaaS cloud deployment. Leveraging cloud computing and container technology, the system can reasonably allocate and schedule resources, ensuring data processing efficiency and computing power in scenarios such as budget management, while meeting the enterprise's diverse needs for real-time status monitoring, management accounting reform, and reduction of information construction costs.
Book Introduction
Against the backdrop of accelerating digital transformation and intelligent decision-making, multi-dimensional databases, as the core support for Online Analytical Processing (OLAP) and enterprise-level data analysis, have become critical infrastructure in industries such as manufacturing, finance, energy, and healthcare.
This book is based on four major dimensions: theory, technology, application, and innovation, completely covering the full-stack knowledge of multi-dimensional databases:
● Development Background and Core Concepts: Starting from the development history, it helps readers establish a systematic understanding.
● In-Depth Analysis of Core Technologies: Covering key technologies such as multi-dimensional modeling, query calculation, storage indexing, and security mechanisms.
● Industry Applications and Ecosystem Comparison: Panoramically showcasing enterprise management and multi-industry application practices, comparing domestic and international product ecosystems.
● Localized Implementation Case Studies: Using the domestic multi-dimensional database GmmCube as an example, detailing its architectural design and innovations.
● Future Trend Outlook: Prospectively discussing the development direction of intelligent multi-dimensional databases.
The book has a clear logical structure and solid content. It can serve as a textbook for university majors such as computer science, data science, and software engineering, and is also suitable for systematic learning by professionals in database R&D, data architecture, data analysis, and enterprise IT.
【Book Highlights】
● Complete System, Gradual Progression: From the history of database development to OLAP principles, from basic modeling to MDX syntax and execution engines, it progresses step by step, making it easy to learn and understand.
● Technically Robust, Engineering-Oriented: Covers key implementations such as dimension design, index structures, permission security, and query optimization, directly addressing R&D and implementation needs.
● Rooted in Localization, Vivid Case Studies: Uniquely details the application of multi-dimensional databases in industries such as manufacturing, finance, education, power grids, and healthcare, as well as the architecture, modeling, engine, and innovation of the domestic multi-dimensional database GmmCube, aiding in the learning of independently controllable technology.
● Forward-Looking Perspective, Intelligence-Focused: Explores the development directions of the new generation of intelligent multi-dimensional databases, including intelligent modeling, natural language querying, performance optimization, predictive analysis, and anomaly detection.
【Target Audience】
● Students and faculty in computer science, data science, big data, and artificial intelligence
● Database kernel R&D engineers, data warehouse and BI engineers
● Enterprise data architects, data analysis and decision support practitioners
● Technology managers focused on domestic databases and independently controllable technology
【Purchase Information】
● Title: "Multi-Dimensional Database"
● Publisher: Science Press
● Purchase Channel: Science Press Official Flagship Store
The publication of this book provides a learning and practice guide in the field of multi-dimensional databases that combines systematic theory, engineering implementation, and localization characteristics. It is sincerely recommended for readers to purchase and read.
It is foreseeable that with the rapid development of new technologies like AI, multi-dimensional databases, as a key technological support for enterprise management software, will continue to evolve towards being more intelligent, efficient, and precise. Technological innovation is the core driving force empowering enterprise management. Intcube is willing to work together with industry colleagues to promote the deepening of learning and expansion of application in the fields of domestic multi-dimensional databases and Enterprise Performance Management (EPM).
