Application Asset Discovery for Automated Data Lineage Mapping

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Solution Overview

Problem

Conventional data governance tools lack the capability to provide a precise, automated understanding of data lineage and asset relationships, leading to inaccurate inventory and manual tracking that is time-consuming and error-prone, especially in API-driven environments.

Innovation Solution

Implementing a language-agnostic data discovery module that utilizes machine learning algorithms to scan application source code, identify technical assets, harvest metadata, and create a knowledge map for fine-grained data understanding, including data stores, APIs, and services, with automated data quality checks and policy enforcement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual tracking methods are used for data inventory and lineage, then implementation simplicity is maintained, but accuracy and reliability of data tracking deteriorate

Engineering Contradiction:

Improveaccuracy of data trackingVSAvoidcomplexity of tracking system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces manual tracking mechanisms with automated code scanning and machine learning algorithms. The system automatically scans application source code, identifies data assets and relationships, and builds lineage graphs without human intervention, thereby improving accuracy while managing complexity through automation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by having applications automatically generate and update their own data lineage information through code scanning. The metadata extraction and relationship mapping occur autonomously, allowing the system to self-maintain accurate data inventory without requiring manual tracking efforts.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual tracking of data assets is performed, then resource consumption is low, but time required for data governance increases

Engineering Contradiction:

Improvespeed of data inventory creationVSAvoidtime for manual tracking

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary action by scanning and analyzing application source code during the development or deployment phase, extracting metadata and establishing data relationships before production. This proactive approach creates the data lineage information in advance, eliminating the need for time-consuming manual tracking later.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The automated scanning and metadata extraction processes run continuously or periodically, maintaining up-to-date data lineage information as applications evolve. This continuous automation eliminates idle time and ensures productivity gains are sustained as the system adapts to changing data assets.

Inventive Principle:

Principle #20Continuity of useful action

3Measurement precision

If fine-grained analysis of technical assets is implemented, then measurement precision improves, but complexity of detection and measurement increases

Engineering Contradiction:

Improvegranularity of data asset understandingVSAvoiddifficulty of code scanning and metadata extraction

Core Design Contradiction:

Measurement precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The patent segments the complex task of data lineage tracking into distinct components: code scanning, metadata extraction, relationship identification, and lineage graph construction. Each component handles a specific aspect of the analysis, making the overall fine-grained measurement manageable through modular processing of individual data assets and their relationships.

Inventive Principle:

Principle #1Segmentation

4Productivity

If automated code scanning and metadata harvesting are implemented, then productivity increases, but device complexity increases

Engineering Contradiction:

Improveautomation of data discoveryVSAvoidcomplexity of data discovery module

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The data discovery module is designed as a universal, language-agnostic system that can scan and analyze code across multiple programming languages and frameworks. This multi-functionality consolidates what would otherwise require separate tools for each language, managing device complexity while maintaining high productivity through a single automated platform.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12626148B2Methods and systems for discovering and classifying application assets and their relationships

Publication Date: 2026.05.12 JPMORGAN CHASE BANK NA

AI summary

Various methods, apparatuses/systems, and media for implementing a data discovery module are disclosed. A repository includes one or more memories that stores application code for each application among a plurality of applications. A processor is operatively connected to the repository via a communication network. The processor scans the application source code for each application among the plurality of applications; identifies, in response to scanning, all technical assets and their relationships within each application; harvests technical metadata from the technical assets and their relationships to identify what information is used, stored, created, and moved by the application; implements machine learning algorithms to automatically assign descriptive and administrative metadata at a field level; loads the assigned descriptive and administrative metadata into an enterprise data catalog; and creates, in response to loading, a knowledge map, thereby providing a fine-grain level understanding of data within the technical assets.