Tree-Structured Prime Data Indexing for Random Access Compression
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Solution Overview
Problem
Existing data compression techniques are limited in their ability to efficiently discover and exploit redundancy across large and extremely large datasets, leading to unacceptably slow ingest and retrieval rates, especially when the redundant data is spatially or temporally separated by multiple terabytes, petabytes, or exabytes, and they are not suited for random access of data.
Innovation Solution
The method organizes data using a tree data structure, breaking it into Prime Data Elements and Derivative Elements, utilizing a Prime Data Sieve to store and retrieve these elements efficiently, allowing for lossless data reduction by referencing Prime Data Elements and applying a Reconstitution Program to derive Derivative Elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional data compression techniques are used to store large datasets, then storage cost is reduced, but data ingestion and retrieval rates become unacceptably slow
Solution Approach 1:
The patent segments data into discrete data elements and organizes them in a tree data structure with hierarchical levels. Each node in the tree represents a segment of the data space, allowing the system to divide and conquer the large dataset. This segmentation enables efficient navigation and access to specific data portions without processing the entire dataset, thereby maintaining high ingestion and retrieval rates while achieving compression.
Solution Approach 2:
The patent introduces a hierarchical dimensional structure (tree levels) to organize data elements. By adding this organizational dimension, the system can navigate through data space efficiently using path-based access rather than linear scanning. This dimensional organization allows parallel processing and rapid location of data elements, resolving the contradiction between compression and access speed.
2Quantity of substance
If data is compressed by exploiting redundancy across large datasets, then data footprint is reduced, but the ability to access random data is lost
Solution Approach 1:
The patent performs preliminary organization of data elements into a tree structure during the data ingestion phase. Data elements are pre-positioned in the hierarchical structure based on their values and relationships. This preliminary action creates an indexed organization that enables rapid random access later, as the tree structure provides direct paths to any data element without requiring sequential processing or full decompression.
Solution Approach 2:
The tree data structure acts as an intermediary between the compressed data storage and the access operations. Instead of directly accessing compressed data blocks, the system navigates through the tree structure which provides a mapping layer. This intermediary structure enables random access by translating access requests into efficient tree traversals, maintaining both compression and random access capabilities.
3Loss of information
If existing compression techniques are used on datasets separated by multiple terabytes or petabytes, then some redundancy is exploited, but the process becomes extremely slow
Solution Approach 1:
The patent segments the large dataset into data elements organized across hierarchical tree levels. Each segment (tree node) can be processed independently and stored separately. This segmentation allows the system to exploit redundancy within each segment efficiently without needing to process the entire multi-terabyte or multi-petabyte dataset as a single unit, dramatically reducing processing time while maintaining compression effectiveness.
Solution Approach 2:
The patent applies compression and redundancy exploitation at appropriate levels of the hierarchical structure rather than attempting to process all data globally. By applying compression actions partially at each tree level based on local redundancy patterns, the system achieves significant compression without the excessive processing time required for global analysis of entire multi-petabyte datasets.
Data Source
AI summary
A first node in a tree data structure may be reached by traversing a sequence of links based on a name, where the sequence of links may correspond to a first portion of the name, and where the tree data structure may organize prime data elements. A regular expression may be retrieved which is associated with a first link and which emanates from the first node. A second portion of the name may be matched with the regular expression, where the second portion is subsequent to the first portion in the name. In response to the second portion of the name matching the regular expression, the first link may be traversed to reach a second node.