ONP-Tree Data Management for Multi-Dimensional Storage
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Solution Overview
Problem
Relational database management systems (RDBMSs) are not optimally suited for storing and retrieving multi-level, multi-dimensional (ML/MD) data used by online application processing (OLAP) applications and graphical user interfaces, leading to inefficiencies in data storage and access due to the mismatch between ML/MD data access patterns and the table-based paradigm of RDBMSs.
Innovation Solution
An efficient database management system (DBMS) employing memory-mapped files (MMFs) and a tree-like partitioning structure using hash objects, where ML/MD data is stored in a hash-object representation and organized into an ordered-nested-partition-tree (ONP) data structure, allowing for efficient storage and retrieval by leveraging memory-mapped files to avoid the overheads associated with traditional DBMSs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If ML/MD data is stored in relational database tables, then data storage capability is provided, but access delays increase and retrieval efficiency decreases
Solution Approach 1:
The patent segments ML/MD data into hierarchical levels and dimensions, organizing it into a multi-level multi-dimensional data structure that separates data by hierarchy level and dimensional attributes. This segmentation allows efficient retrieval by enabling direct access to specific hierarchical levels and dimensional slices without scanning entire relational tables, thereby reducing access delays while maintaining storage capability.
Solution Approach 2:
The patent introduces a new dimensional organization paradigm by storing data in a multi-level multi-dimensional structure with explicit hierarchy levels and dimensional attributes. This dimensional transformation from traditional relational tables enables efficient querying along different dimensional axes and hierarchical levels, significantly improving retrieval efficiency for OLAP operations while preserving data storage capacity.
2Adaptability or versatility
If RDBMS table-based paradigm is used for ML/MD data, then standardized SQL interface is available, but computational efficiency decreases
Solution Approach 1:
The patent introduces an intermediary layer between the standardized SQL interface and the underlying data storage. This intermediary translates SQL queries into efficient operations on the multi-level multi-dimensional data structure, maintaining compatibility with standardized SQL interfaces while optimizing computational efficiency by leveraging the hierarchical and dimensional organization of the data for faster processing.
Solution Approach 2:
The patent changes the fundamental parameters of data organization from relational tables to a hierarchical multi-dimensional structure with explicit levels and dimensional attributes. This parameter transformation enables more efficient computational operations for ML/MD data by aligning the data structure with the natural access patterns of OLAP applications, thereby improving productivity while maintaining interface adaptability.
3Quantity of substance
If massive amounts of ML/MD data are stored in relational database tables, then data storage capacity is achieved, but physical data-storage subsystem efficiency decreases
Solution Approach 1:
The patent segments massive ML/MD data into hierarchical levels and dimensional components, organizing it into a structured multi-level multi-dimensional format. This segmentation enables efficient physical storage by allowing selective loading and indexing of specific hierarchical levels and dimensional slices, improving physical data-storage subsystem efficiency while maintaining the capacity to store large volumes of data.
Solution Approach 2:
The patent transforms the storage paradigm by introducing explicit hierarchy levels and dimensional attributes into the data structure. This dimensional organization enables more efficient physical storage and retrieval operations by allowing the system to directly access specific dimensional slices and hierarchical levels without scanning entire datasets, thereby enhancing physical data-storage subsystem efficiency while preserving storage capacity.
Data Source
AI summary
The present application is directed to a data management system. The data management system is implemented within a computer system that includes one or more processors, one or more electronic memories, and a memory-mapped file facility that electronically stores data. The data management system, implemented by data management system control routines that are executed by the one or more processors, retrieves a particular projection of a multi-level, multi-dimension data set stored within the ONP-tree data structure that contains the multi-level, multi-dimension data set.