Cladistics Data Analyzer for Business Trace Aggregation
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Solution Overview
Problem
Existing data analysis systems face difficulties in efficiently inspecting and aggregating large sets of process-traces to identify meaningful subsets, as they often miss similarity patterns dependent on task sequences rather than specified data-values, and require prior knowledge about similarity criteria.
Innovation Solution
A system that collects business traces, assigns unique vector values, creates a hierarchical tree using cladistics techniques, and detects sub-trees to identify similarities among traces, allowing for the automatic aggregation of large numbers of traces without requiring a priori knowledge of similarity criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection and aggregation of process traces is performed, then meaningful subsets can be identified, but the task becomes very hard when the number of traces and data amount are large
Solution Approach 1:
The patent replaces manual mechanical inspection with automated computational analysis. The system uses computer-implemented algorithms to automatically inspect and aggregate process traces, substituting human analysts with automated systems that can handle large volumes of data efficiently.
Solution Approach 2:
The system enables self-service by automatically performing the inspection and aggregation tasks without requiring manual intervention. The automated system serves itself by collecting traces, analyzing them, and generating insights without human effort, making the process scalable to large datasets.
2Loss of information
If traditional data analysis methods are used, then analysis can be performed, but similarity patterns dependent on task sequences are missed
Solution Approach 1:
The patent changes the analysis parameters from traditional data-value-based criteria to task sequence-based criteria. By transforming the similarity detection parameters to focus on sequences of tasks rather than specified data values, the system uncovers previously invisible patterns in process traces.
Solution Approach 2:
The system inverts the traditional approach by not requiring prior knowledge of similarity criteria. Instead of specifying what to look for, the system automatically discovers similarity patterns by analyzing task sequences, turning the problem upside down and finding patterns without predefined hypotheses.
3Extent of automation
If prior knowledge about similarity criteria is required, then analysis can be directed, but the system cannot automatically aggregate traces without such knowledge
Solution Approach 1:
The system achieves self-service by automatically discovering similarity criteria without requiring prior knowledge from users. The automated analysis engine independently identifies patterns in task sequences, making the system truly autonomous and capable of automatic aggregation without human expertise in similarity criteria.
Solution Approach 2:
The system performs preliminary action by automatically analyzing task sequences and establishing similarity patterns before any user-defined criteria are applied. This preliminary automated analysis creates a foundation for subsequent aggregation, eliminating the need for users to pre-specify similarity criteria.
Data Source
AI summary
An analyzer system may include a computer-apparatus to collect traces from a pool of business traces, and to assign an unique vector value to each trace. The system may also include an assembler to create a tree based upon the unique vector value of each trace. The system may further include an analyzer to detect sub-trees within the tree to identify similarities among the traces based upon the traces inclusion within a given sub-tree.