Context-Based Keyword Grouping for Legacy Business Rule Mining

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Solution Overview

Problem

Legacy applications, such as those with mainframe COBOL artifacts, pose challenges in business rule mining due to their complexity and the difficulty in understanding and organizing keywords for business analysts, requiring manual effort to group discovered keywords which is time-consuming.

Innovation Solution

Implementing context-based keyword grouping using a co-occurrence matrix and Latent Dirichlet Allocation (LDA) analysis to automatically group keywords based on their context, reducing manual effort and presenting the grouped keywords through a user interface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual keyword grouping is performed by business analysts, then keyword organization can be achieved, but time consumption and manual effort increase significantly

Engineering Contradiction:

Improvekeyword grouping easeVSAvoidtime for manual keyword grouping

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system enables automatic keyword grouping through contextual analysis algorithms that self-organize keywords based on their co-occurrence patterns and semantic relationships, eliminating the need for manual analyst intervention in the grouping process

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual mechanical grouping operations by business analysts are replaced with automated computational algorithms that analyze contextual relationships between keywords using co-occurrence matrices and clustering techniques

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If legacy application artifacts are analyzed without contextual grouping, then keyword discovery can begin, but the massive number of unorganized keywords becomes difficult to understand and process

Engineering Contradiction:

Improvebusiness rule mining efficiencyVSAvoidkeyword list complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The large set of discovered keywords is segmented into meaningful groups based on contextual relationships, transforming an overwhelming flat list into organized clusters that are easier to analyze and process for business rule extraction

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Keywords are organized by adding a contextual grouping dimension, transforming a one-dimensional flat list into a multi-dimensional structure where keywords are nested within thematic groups, enabling better comprehension and processing

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Measurement precision

If programming language syntax and conventions are directly presented to business analysts, then translation accuracy can be maintained, but analysts face difficulty understanding the artifacts due to specialized syntax

Engineering Contradiction:

Improvebusiness rule translation accuracyVSAvoidartifact understandability

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

Contextual keyword groups serve as an intermediary layer between raw programming artifacts and business analysts, providing a simplified yet accurate representation that bridges the gap between technical syntax and business understanding

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11397754B2Context-based keyword grouping

Publication Date: 2022.07.26 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Techniques for context-based keyword grouping for business rule mining are described herein. An aspect includes determining, based on a first corpus, a first list of keywords. Another aspect includes constructing a co-occurrence matrix based on the first list of keywords. Another aspect includes applying a clustering algorithm to the co-occurrence matrix to determine a first plurality of keyword groups. Another aspect includes presenting the first plurality of keyword groups to a user via a user interface.