Delimited Text Compression Schema for Selective Access
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Solution Overview
Problem
Existing data compression techniques for delimited text files, particularly in genomics and bioinformatics, are suboptimal due to lack of customization and support for functionalities like fast query and random access, encryption, and authentication, leading to inefficient compression and decompression processes.
Innovation Solution
A method and system for compressing delimited text files by parsing them into data blocks and units based on a customized compression schema, using different compression algorithms for each unit, and including metadata for decompression and extended functionalities like data security and search queries, allowing selective access and efficient storage and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing compression techniques (e.g., gzip) are used on delimited text files, then compression is applied uniformly to the entire file, but compression performance is suboptimal because the same algorithm cannot efficiently handle diverse data patterns throughout the file
Solution Approach 1:
The delimited text file is divided into multiple data blocks, where each block can be compressed using a different compression algorithm selected based on the specific characteristics of that block. This segmentation allows the system to apply the most appropriate compression technique to each portion of the file, improving overall compression performance while maintaining manageable complexity through modular block processing.
Solution Approach 2:
Different compression algorithms are applied to different data blocks based on local data characteristics. The system analyzes each block's properties (such as data type, repetition patterns, and structure) and selects the most suitable compression algorithm for that specific region, rather than applying a single algorithm uniformly across the entire file.
2Productivity
If delimited text files are compressed using a single algorithm for the entire file, then the compression process is simple to implement, but the compression ratio and efficiency are suboptimal
Solution Approach 1:
The compression schema is made dynamic and adaptable to different file formats and data characteristics. The system can automatically detect the file format (such as VCF, BED, GFF3, GTF) and select or generate an appropriate compression schema, allowing the compression approach to adapt to the specific properties of each delimited text file rather than using a fixed, one-size-fits-all method.
Solution Approach 2:
The compression parameters and algorithms are changed based on the specific characteristics of different data blocks and file formats. The system adjusts compression parameters dynamically according to the detected data patterns, file type, and block properties, enabling optimal compression ratios for diverse delimited text files without requiring manual configuration for each case.
3Adaptability or versatility
If existing compression approaches are used on delimited files, then basic compression is achieved, but support for advanced functionalities (fast query, random access, encryption, authentication) is lacking
Solution Approach 1:
The compression system is designed to provide multiple functionalities beyond basic compression, including fast query capabilities, random access to specific data regions, encryption, and authentication. The schema-based architecture allows these advanced features to be integrated into the compression framework, enabling a single system to handle diverse operational requirements for delimited text files.
Solution Approach 2:
The compression schema acts as an intermediary layer between the raw delimited text data and the compression algorithms. This schema layer enables advanced functionalities by providing a structured interface that supports fast query operations, random access through block-level addressing, and security features like encryption and authentication, without requiring direct modification of the underlying compression algorithms.
4Loss of time
If the entire delimited text file is compressed as a single unit, then the compression process is straightforward, but selective access and processing of specific regions require decompression of the entire file
Solution Approach 1:
The file is segmented into multiple compressible blocks that can be independently accessed and processed. This segmentation enables selective decompression of only the specific blocks containing the desired data, rather than requiring decompression of the entire file, significantly reducing access time for regional queries while maintaining a manageable block structure for efficient processing.
Data Source
AI summary
A method for compressing data includes obtaining a compression schema customized to a format of a delimited text file, and using the compression schema to parse the delimited text file into a plurality of data blocks, split each of the data blocks into a plurality of data units for efficient selective access, and compress the plurality of data units in the plurality of data blocks using different compression algorithms for improved compression ratio. The delimited file is split into a plurality of data blocks based on the region definitions in the schema. Each of the plurality of data blocks is split into the plurality of data units based on its respective data unit size specified in the schema. The plurality of data units in each of the plurality of data blocks are compressed using the different compression algorithms indicated by the compression instructions in the schema. The compressed file consists of the compressed data blocks, the compression schema and various metadata for data decompression, file reconstruction and functionalities such as data security and search query. The delimited text file may include genomic information or another type of information.