Process Mining via Event Log Filtering and History Field Analysis
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Solution Overview
Problem
Generic process mining techniques fail to effectively leverage the structure of data in multi-tenant database systems, leading to inefficient or ineffective process mining, particularly in cloud platforms used for customer relationship management and other data-intensive applications.
Innovation Solution
The implementation of a process mining system that utilizes a layered taxonomy to classify and filter event log entries, allowing for the efficient detection of frequent patterns and identification of alternative paths in process flows by distinguishing between main and detailed actions, and leveraging both event logs and history fields to determine optimized process flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic process mining techniques are used, then implementation simplicity is maintained, but process mining effectiveness deteriorates due to failure to leverage multi-tenant database structure
Solution Approach 1:
The patent segments the process mining approach into two distinct components: (1) a generic process mining module that handles overall workflow analysis, and (2) a specialized multi-tenant database structure with separated schemas for tenants and common data. This segmentation allows the system to maintain simplicity in the generic module while achieving effectiveness through the specialized database structure, resolving the contradiction between implementation simplicity and process mining effectiveness.
Solution Approach 2:
The patent introduces an intermediary layer in the database schema that separates tenant-specific data from common platform data. This intermediary structure enables the process mining system to efficiently query and analyze data without being burdened by the full complexity of multi-tenant relationships, thereby maintaining implementation simplicity while improving process mining effectiveness through optimized data access patterns.
2Measurement precision
If all log entries are processed without filtering, then comprehensive pattern detection is achieved, but processing resources and time increase significantly
Solution Approach 1:
The patent extracts and filters out noise log entries from the complete event log before performing pattern detection. By removing irrelevant or redundant log entries, the system maintains comprehensive pattern detection accuracy on meaningful data while significantly reducing processing resources and time requirements, thus resolving the contradiction between measurement precision and productivity.
Solution Approach 2:
The patent applies partial action by selectively processing only the most relevant log entries for pattern detection rather than analyzing every single log entry. This selective approach maintains sufficient pattern detection accuracy for identifying significant process patterns while dramatically improving processing efficiency by avoiding unnecessary analysis of minor or redundant events.
3Loss of information
If detailed analysis of all actions is performed, then process flow understanding is improved, but processing time and resources increase
Solution Approach 1:
The patent applies local quality by performing detailed analysis only on specific critical actions and process flows that are most relevant to business objectives, rather than uniformly analyzing all actions in detail. This approach maintains completeness of important process flow information while reducing overall processing time by applying less intensive analysis to less critical actions.
Solution Approach 2:
The patent performs partial detailed analysis by focusing computational resources on analyzing only the most significant process actions and pathways. This selective detailed analysis preserves understanding of critical process flows while minimizing processing time and resources by avoiding exhaustive analysis of all actions, thus resolving the contradiction between information completeness and time loss.
Data Source
AI summary
Methods, systems, apparatuses, devices, and computer program products are described. A system may identify, from an event log including log entries for a tenant of a multi-tenant database system, a pattern of log entries corresponding to main actions and satisfying a frequency threshold. The system may identify log entries associated with the pattern and corresponding to the main actions, detailed actions, or both. The system may retrieve data corresponding to a history field of a data object associated with the pattern and may determine at least a portion of a process flow for the data object according to the pattern and based on the log entries and the historical data. The process flow may include operations to perform using the data object. In some cases, the system may transmit, to a user device, an indication of the portion of the process flow for user review and implementation.