Tuple-Based Propositions for Analytics Computing Systems
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Solution Overview
Problem
Programming analytics computing systems to accurately reflect business or technical processes is complex, requiring multiple expert teams and involving significant time and expense due to the need for functional analysis, architectural drafting, and quality checking, which can be challenging for enterprises using low-code or no-code solutions that lack customization and flexibility.
Innovation Solution
The use of tuple-based propositions allows for simplified programming by expressing data as tuples with predicates and arguments, enabling non-software personnel to generate propositions with minimal training and facilitating matrix-based operations for efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional programming methods are used to program analytics computing systems, then accuracy in reflecting business processes is improved, but complexity of programming and time required increase significantly
Solution Approach 1:
The patent uses tuple-based propositions as a simplified copy or representation of complex business process logic. Instead of implementing full traditional programming structures, the system copies essential relationships into tuple format (subject, predicate, object) that can be processed directly by the analytics engine, maintaining accuracy while reducing programming complexity
Solution Approach 2:
The patent changes the parameter of programming language from traditional structured code to a simplified tuple-based proposition format. This parameter change allows non-software personnel to express business logic using intuitive subject-predicate-object relationships rather than complex programming constructs, reducing the barrier to entry while maintaining analytical accuracy
2Manufacturing precision
If traditional programming methods are used, then analytical accuracy is improved, but time and expense required for multiple expert teams increase
Solution Approach 1:
The patent enables non-software personnel to directly create and maintain analytics propositions using tuple-based syntax without requiring traditional software development teams. Business users can independently translate their domain knowledge into executable analytics code, eliminating the need for multiple expert teams (functional analysts, architects, developers, quality checkers) and significantly reducing time and expense while maintaining analytical accuracy
3Loss of time
If low-code or no-code solutions are used, then programming time is reduced, but customization and flexibility are lost
Solution Approach 1:
The patent implements a dynamic proposition framework where tuple-based propositions can be easily created, modified, deleted, and combined. The system allows runtime addition of new propositions and dynamic configuration of analytics pipelines, enabling both rapid deployment (low-code benefit) and extensive customization (flexibility benefit) through a flexible proposition management system
Data Source
AI summary
Various examples are directed to systems and methods in an analytics computing system. The analytics computing system may receive query data and an indication of a subject tuple. The analytics computing system may access analytics code comprising first proposition data corresponding to the subject tuple. The first proposition data may comprise proposition head data describing the subject tuple and proposition body data describing at least one proposition condition, the at least one proposition condition comprising a relationship between a first proposition body tuple and a second proposition body tuple. The analytics computing system may access query processing data and determine a subset of the query processing data for which the at least one proposition condition is true.