Virtual Array API for Large Dataset Memory Management
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Solution Overview
Problem
Programmers face inefficiencies when accessing data from various file formats, requiring knowledge of specific file formats and formats-specific instructions, which can be cumbersome, especially when dealing with large datasets that cannot be loaded into memory entirely.
Innovation Solution
The introduction of a virtual array data structure and a corresponding API that allows indexing into datasets in secondary storage, enabling efficient access and manipulation of data without loading the entire dataset into memory, using a familiar syntax similar to array manipulation, and employing a formatter object to facilitate data access across different formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the entire dataset is loaded into memory for manipulation, then data access and manipulation becomes simple and efficient, but memory resources are consumed and large datasets cannot be handled
Solution Approach 1:
The patent segments the dataset into smaller blocks or chunks that can be loaded into memory individually rather than loading the entire dataset at once. This allows efficient data manipulation of manageable portions while conserving memory resources for larger datasets.
Solution Approach 2:
The patent introduces a virtual array as an intermediary layer between the data storage system and the user's manipulation operations. This virtual array provides a familiar array-based interface while managing the underlying data in a memory-efficient manner, abstracting away the complexity of direct file I/O.
2Reliability
If format-specific instructions are used to access data from different file formats, then data access becomes precise and reliable, but the complexity of working with multiple formats increases
Solution Approach 1:
The patent creates a universal virtual array interface that can handle multiple data formats through a single consistent syntax. The virtual array acts as a multi-functional wrapper that translates different file format requirements into a unified array manipulation interface, eliminating the need for format-specific instructions.
Solution Approach 2:
The virtual array serves as an intermediary layer that translates between the diverse file format requirements and the user's standardized array manipulation operations. This mediator handles the format-specific details internally while presenting a consistent, simple interface to the user.
3Measurement precision
If detailed knowledge of file format and data organization is required, then data extraction becomes accurate, but the learning curve and programming complexity increase
Solution Approach 1:
The virtual array acts as an intermediary that encapsulates the complex file format and data organization details. Users interact with the data through intuitive array indexing and manipulation operations on the virtual array, while the system handles the underlying format-specific complexity automatically.
Solution Approach 2:
The virtual array creates a conceptual copy or view of the data structure that mirrors familiar array operations. This virtual representation allows users to manipulate data using standard array syntax without needing to understand the actual file format or data organization on disk.
Data Source
AI summary
A generalized input/output application programming interface (API) may provide an interface to file related operations. As a result of using this API, users of the array-based programming language may be able to manipulate stored data as though the data were a virtual array loaded into memory. Users do not need to memorize a different API in order to access data which may be stored in different file or data formats. In addition, using this API simplifies the task of working with large data sets by giving users seamless access to the sections of data they want to operate on when the entire data is too large to be loaded into memory.