Provenance Graph for Enterprise Process Traceability

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Solution Overview

Problem

Current enterprise processes face challenges in managing compliance and performance due to reliance on unstructured data and manual processes, leading to high costs and inefficiencies in tracking and correlating operations, with existing solutions failing to provide effective traceability and root cause analysis.

Innovation Solution

The development of techniques for capturing, storing, and analyzing provenance data to generate a visual representation of enterprise processes, enabling selective information capture and correlation, and providing a flexible framework for compliance and performance monitoring through a generic data model and middleware infrastructure.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If full-fledged data and process reengineering is implemented to track and correlate enterprise operations, then visibility and compliance management improve, but cost and implementation overhead increase significantly

Engineering Contradiction:

Improvevisibility of end-to-end operationsVSAvoidcost and overhead of reengineering

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent extracts only the essential provenance information needed for compliance and performance tracking, rather than implementing comprehensive data collection. The system selectively captures data about data lineage, resource involvement, and task execution, filtering out unnecessary information to reduce complexity while maintaining visibility.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The provenance data model serves multiple functions simultaneously: it tracks data lineage for compliance, monitors enterprise performance, enables root cause analysis, and supports audit activities. This multi-functional approach eliminates the need for separate tracking systems, reducing overall system complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If comprehensive provenance data collection is implemented to enable complete traceability, then compliance tracking and root cause analysis improve, but data processing complexity and storage requirements increase

Engineering Contradiction:

Improvetraceability of enterprise operationsVSAvoiddata processing and storage complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies different levels of provenance data collection to different parts of the enterprise based on their specific compliance and performance monitoring needs. Not all enterprise operations require the same level of detail, allowing the system to optimize data collection locally rather than uniformly across the entire enterprise.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The provenance data is segmented into distinct categories: data lineage information, resource involvement data, and task execution records. This segmentation allows for more efficient storage, retrieval, and processing by organizing information according to its purpose and type.

Inventive Principle:

Principle #1Segmentation

3Ease of manufacture

If manual processes and unstructured data are used for enterprise operations, then implementation cost is reduced, but compliance tracking efficiency and performance monitoring capability deteriorate

Engineering Contradiction:

Improveimplementation costVSAvoidcompliance tracking efficiency

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The system automatically collects and processes provenance data from enterprise operations without requiring manual intervention. The automated data collection mechanisms capture information about data creation, modification, and usage, as well as resource and task information, eliminating the need for manual tracking while maintaining low implementation costs.

Inventive Principle:

Principle #25Self-service

4Ease of manufacture

If selective information capture is implemented to reduce data collection overhead, then cost-effectiveness improves, but completeness of compliance and performance information may be compromised

Engineering Contradiction:

Improvecost-effectivenessVSAvoidcompleteness of provenance data

Core Design Contradiction:

Ease of manufactureVSLoss of information

Solution Approach 1:

The patent implements selective capture of provenance information that is sufficient for compliance and performance monitoring purposes. The system captures the essential elements needed to trace data lineage, identify resource involvement, and record task execution, without collecting every possible detail, achieving the right balance between completeness and cost-effectiveness.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS8595042B2Processing of provenance data for automatic discovery of enterprise process information

Publication Date: 2013.11.26 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Techniques are disclosed for capturing, storing, querying and analyzing provenance data for automatic discovery of enterprise process information. For example, a computer-implemented method for managing a process associated with an enterprise comprises the following steps. Data associated with an actual end-to-end execution of an enterprise process is collected. Provenance data is generated based on at least a portion of the collected data, wherein the provenance data is indicative of a lineage of one or more data items. A provenance graph that provides a visual representation of the generated provenance data is generated, wherein nodes of the graph represent records associated with the collected data and edges of the graph represent relations between the records. The generated provenance graph is stored in a repository for use in analyzing the enterprise process.