Ordered Traversal Rules for PLM Data Hierarchy
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Solution Overview
Problem
Current product lifecycle management (PLM) and product data management (PDM) systems face inefficiencies in traversing and processing large datasets due to slow performance and scalability issues when dealing with complex hierarchies and numerous database queries, particularly in retrieving child objects related to input objects.
Innovation Solution
A system and method that processes traversal rules in a determined precedence order to recursively acquire child objects from a data store, optimizing performance by sorting rules based on the number of parent types and reducing the number of database queries through set-based queries and hierarchical organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional traversal methods are used to retrieve child objects in PLM systems, then completeness of data retrieval is improved, but processing speed and system scalability deteriorate due to numerous database queries and complex hierarchy handling
Solution Approach 1:
The system performs preliminary actions by pre-computing and caching traversal results, building hierarchical structures in advance, and preparing query execution plans before actual data retrieval operations. This allows the system to quickly serve subsequent traversal requests without repeating expensive database operations, thus improving processing speed while maintaining data completeness.
Solution Approach 2:
The traversal process is segmented into multiple independent phases: rule processing, hierarchy construction, query generation, and data retrieval. Each phase handles specific tasks independently, allowing for optimized execution of individual steps and better resource utilization, which improves overall processing efficiency without compromising the completeness of child object retrieval.
2Reliability
If traditional traversal methods are used to process large datasets, then thoroughness of processing is improved, but scalability and resource utilization deteriorate due to excessive database queries
Solution Approach 1:
Multiple database queries are merged into single set-based queries where possible. The system combines traversal rules and consolidates retrieval operations to execute fewer, more efficient queries that retrieve larger datasets in bulk. This merging approach maintains thorough processing coverage while significantly reducing the total number of database round-trips, thereby improving system scalability.
Solution Approach 2:
The system transitions from iterative single-record traversal to batch-oriented set-based operations. By changing the dimension of processing from individual object level to collection level, the system achieves better resource utilization and scalability while maintaining comprehensive processing coverage through structured query execution.
3Measurement precision
If complex traversal rules are applied to ensure accurate child object retrieval, then retrieval accuracy is improved, but query complexity and execution time deteriorate
Solution Approach 1:
Traversal rules are pre-processed and validated before execution. The system prepares query execution plans in advance, pre-computes traversal paths based on predefined rules, and caches intermediate results. This preliminary preparation ensures accurate retrieval by establishing correct query structures beforehand, while simultaneously reducing execution time by avoiding repeated rule interpretation during actual data retrieval.
4Reliability
If recursive traversal is used to handle deep class hierarchies, then completeness of hierarchy coverage is improved, but performance and throughput deteriorate due to excessive database round-trips
Solution Approach 1:
The recursive traversal process is segmented into hierarchical levels, with each level processed in discrete batches. The system constructs hierarchical structures level-by-level, retrieving and processing child objects at each hierarchy depth independently. This segmentation maintains complete hierarchy coverage by systematically visiting all levels, while improving throughput by enabling parallel processing of different hierarchy levels and reducing the overhead of deep recursion.
Solution Approach 2:
The system transforms deep recursive traversal into iterative batch processing with explicit hierarchy level management. By introducing a level dimension and processing objects in structured batches rather than deep recursive calls, the system achieves complete hierarchy coverage while significantly improving performance through better stack utilization and reduced function call overhead.
Data Source
AI summary
A system is provided that carries out object traversal in a product lifecycle management system. The system may process a received set of traversal rules in a determined precedence order for a received list of input objects to recursively acquire from a data store a list of child objects related to the input objects based on the traversal rules. The traversal rules may be processed in the precedence order determined based at least in part on a number of parent types in a hierarchical arrangement that specifies relationships between object types for a source type of object specified by each respective traversal rule. For each respective traversal rule, a set based query may be carried out on the data store to determine child objects for the input objects having one of a type or a parent type corresponding to the source type associated with the respective traversal rule.