Self-Describing Data Model for Recursive Query Execution
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Solution Overview
Problem
Current search technologies face challenges in implementing recursive searches in relational databases and integrating data across dynamic data models, particularly due to difficulties in formulating and executing recursive queries and compatibility issues with certain markup languages.
Innovation Solution
A query engine is developed for recursive searches in a self-describing data system, enabling communication between client and third-party systems by generating logical model items and mapping types, allowing for the execution of recursive queries across dynamic data models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recursive search queries are executed in relational databases, then search functionality is improved, but query formulation and execution difficulty increases
Solution Approach 1:
The patent introduces a query engine as an intermediary layer between the user and the relational database. This query engine automatically formulates and executes recursive search queries, translating high-level search requests into complex SQL queries without requiring users to manually formulate them. The query engine acts as a mediator that handles the complexity of recursive query formulation and execution.
Solution Approach 2:
The system enables self-service by allowing users to perform recursive searches without needing to understand or manually formulate complex SQL queries. The query engine automatically handles the entire process of query generation, execution, and result retrieval, making the system accessible to users without technical expertise in database querying.
2Adaptability or versatility
If dynamic data models are used to integrate external systems, then system adaptability is improved, but schema compatibility errors increase
Solution Approach 1:
The patent employs parameter changes by dynamically adjusting the data model schema based on external systems. Instead of using a fixed schema, the system allows the schema to evolve and adapt to different external object models. The query engine modifies query parameters and data model definitions dynamically to match the structure of external systems being integrated.
Solution Approach 2:
The system introduces dynamics into the data model by allowing it to change and evolve over time as external systems are integrated. The data model transitions from a static, rigid structure to a dynamic, flexible structure that can automatically adapt to new external systems without requiring manual schema updates or causing compatibility errors.
3Adaptability or versatility
If multiple computer-implemented systems are integrated, then data communication capability is improved, but integration complexity increases
Solution Approach 1:
The patent implements universality by creating a unified query engine that can communicate with multiple different computer-implemented systems through a common interface. The query engine provides multi-functional capabilities, handling queries across various external systems (e.g., Salesforce, ServiceNow) using the same fundamental mechanisms, thereby reducing integration complexity despite the diversity of target systems.
Data Source
AI summary
In one embodiment, a computing device includes a memory device storing instructions, and a processing device communicatively coupled to the memory device. The processing device executes the instructions to execute a computer-implemented system configured to manage items in a self-describing data model, and generate a representation of a federated system in the self-describing data model. The representation includes logical model items having logical model item types associated with data in an external object model used by a third-party computer-implemented system. The processing device may enable communication of the items and the logical model items between a client computer-implemented system and the third-party computer-implemented system.