Parameterized Encoding for Data Model Instantiation
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Solution Overview
Problem
Conventional CRM system data model instantiation is tedious and time-consuming, requiring discrete manipulation of CSV files and hardcoding, making it difficult to modify scale or ratios, which hinders efficient testing of relational data models for customer load requirements.
Innovation Solution
A domain-specific language is implemented to encode operations for data model instantiation, using a parameterized encoding mechanism that procures, transforms, and creates records, allowing for flexible and concise data model instantiation through a high-level programming language like Python, enabling user interaction and modification of model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If discrete manipulation of CSV files and hardcoding is used for data model instantiation, then the model can be instantiated, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent introduces an intermediary tool that acts as a mediator between the data model definition and the instantiation process. This tool automatically generates the necessary CSV files and executes the instantiation based on high-level parameters, eliminating the need for manual CSV manipulation and hardcoding, thereby dramatically improving productivity and reducing time consumption.
Solution Approach 2:
The system enables self-service instantiation by allowing users to define data models with simple parameters (scale, ratios, entity types) and automatically generating and executing the instantiation process without manual intervention. The system serves itself by automatically creating the necessary data structures and populating them according to the defined model parameters.
2Adaptability or versatility
If hardcoding the instantiation of one model is used, then the model can be instantiated, but it becomes extremely difficult to modify the scale or ratios
Solution Approach 1:
The patent implements dynamic parameterization where the data model instantiation is controlled by configurable parameters (scale, ratios, entity types) that can be modified without changing the underlying code structure. This allows users to dynamically adjust model characteristics by simply changing parameter values, making the system highly adaptable and easy to modify.
Solution Approach 2:
The system uses parameter changes as the core mechanism for adaptability. By defining data models in terms of adjustable parameters (scale factors, entity ratios, population distributions), users can easily modify model characteristics by changing these parameters rather than rewriting code, thereby achieving high versatility with minimal complexity.
3Ease of operation
If manual CSV manipulation is used for generating entities and records, then the data model can be instantiated, but the process is highly tedious
Solution Approach 1:
The patent replaces the mechanical process of manual CSV manipulation with an automated computational system. Instead of manually creating and editing CSV files, the system uses programmatic generation based on defined parameters to create and populate data models, dramatically improving ease of operation and productivity.
Data Source
AI summary
Techniques and structures to facilitate instantiating a data model, including receiving a parameterized encoding of a data model including one or more scenarios, receiving data from a source, executing one or more operations associated with each of the one or more scenarios to transform the received data and creating a plurality of records for the data model based on the transformed data.