Data Buffer Management Using Functional Paradigm and Two-Dimensional Matrix
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Solution Overview
Problem
Current data processing systems face challenges in prioritizing data entries with complex constraints, as simple dispatch policies like FIFO and LIFO do not account for intricate relationships among data entries, leading to cumbersome and expensive implementations for managing data buffers.
Innovation Solution
A functional paradigm is introduced that uses a two-dimensional matrix to represent data entries and apply linear constraints, allowing for flexible prioritization and processing based on predefined constraints, enabling efficient management of data buffers through convex optimization with linear constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple dispatch policies (FIFO, LIFO) are used to manage data buffers, then the system is easy to implement and operate, but it cannot handle complex constraints and relationships among data entries
Solution Approach 1:
The patent creates a universal constraint-based dispatch framework that can handle multiple types of constraints (time deadlines, priority levels, resource requirements, dependencies) within a single unified system. This framework replaces the need for multiple specialized dispatch policies by providing a multi-functional platform that adapts to various complex scenarios through configurable constraints.
Solution Approach 2:
The system transforms the dispatch problem by changing parameters from simple sequence-based ordering to constraint-based prioritization. By introducing parameters such as time deadlines, priority weights, resource availability, and dependency relationships, the system can dynamically adjust dispatch decisions based on multiple factors rather than relying on fixed FIFO or LIFO rules.
2Reliability
If individual programs are constructed to implement each complex application, then specific constraints can be handled, but the system becomes cumbersome and expensive to maintain
Solution Approach 1:
Instead of creating separate programs for each application scenario, the patent implements a single universal constraint-based dispatch framework that can serve multiple applications. This framework accepts constraints as input parameters and automatically generates appropriate dispatch policies, eliminating the need for multiple specialized programs and reducing system complexity.
Solution Approach 2:
The system enables applications to self-configure their own dispatch behavior by specifying their constraints directly. The framework automatically processes these constraints and generates the appropriate dispatch logic without requiring custom program development for each application, thereby reducing maintenance burden and cost.
3Measurement precision
If complex rules are implemented to prioritize data entries with constraints, then processing accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the complex constraint satisfaction problem into manageable components by representing constraints as a constraint graph where nodes represent data entries and edges represent relationships. This segmentation allows the system to process constraints systematically through graph traversal algorithms rather than evaluating all possible permutations, reducing computational complexity while maintaining accuracy.
Data Source
AI summary
A computer-implemented method, data processing system and computer readable medium manages processing of data entries in a data buffer. Data entries are stored in a data buffer. A representation of a two-directional matrix is annotated to mark the data arrivals as a plurality of entries. In addition, the two-directional matrix is annotated to define a valid processing period for the data entries. The data entries in the data buffer are processed by removing the data entries from the data buffer, marked for processing in the two-dimensional matrix during the valid processing period, by annotating the matrix, labeled as ACS, to define the number of available entries as a cumulative sum along the columns, A-X, such that ACS=cumsum(A-X), where ‘cumsum’ is the cumulative column-sum. Finally, the data entries from the data buffer are processed to determine the entries removed from the data buffer as defined by the row-sum of X. The technique has application for implementing a buffer management system for tax loss carry forward calculations.