Analytical Data Protocol for Dynamic Query Conversion
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Solution Overview
Problem
Existing data access systems face challenges in enabling practical use of data due to differences in data formats and user knowledge levels, leading to limitations in data accessibility and the need for complex, technically oriented view definitions.
Innovation Solution
The implementation of a data access layer that facilitates the use of analytical data protocols, allowing software applications to process query requests through a user interface model, and enabling the conversion of queries to be executable using analytical query model objects, thereby accessing and manipulating data in a more user-friendly and flexible manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If views are statically defined with hard-coded technical formats, then data access structure is stable, but end users are limited in data access and it is time-consuming to define additional views
Solution Approach 1:
The patent transforms static, hard-coded view definitions into dynamic, runtime-modifiable definitions. Users can now define and modify views during execution without requiring system restarts or technical expertise. The system allows views to be created, updated, and deleted on-the-fly through user interactions, making the previously rigid view definition process flexible and adaptable to changing user needs.
Solution Approach 2:
The patent introduces an intermediary layer between the user and the data access system. This intermediary translates user-friendly view definitions into the underlying technical query formats, allowing users to work with simplified concepts while the system handles the complex technical transformations. This mediator enables non-technical users to access data effectively without needing to understand the underlying technical protocols.
2Ease of operation
If transactional data protocols are used for data access, then data retrieval is straightforward, but analytical actions are limited and data format manipulation is restricted
Solution Approach 1:
The patent creates a universal data access framework that can handle both transactional and analytical operations through a single interface. The system translates user requests into appropriate analytical queries that can perform diverse operations including data retrieval, filtering, aggregation, and formatting transformations. This multi-functional approach eliminates the need for separate protocols for different data access needs.
Solution Approach 2:
The patent dynamically changes query parameters and data format specifications based on user requirements. The system allows users to specify desired output formats, filtering criteria, and analytical operations, then translates these into appropriate analytical queries. This parameter flexibility enables the same base system to adapt to various analytical needs without requiring separate specialized protocols.
3Reliability
If technical users configure database systems, then data storage and processing capabilities are optimized, but understanding of data usage and manipulation is limited
Solution Approach 1:
The patent implements feedback mechanisms that provide users with information about the data being accessed, the queries being executed, and the results being returned. The system exposes definitional elements of analytical queries to users, allowing them to understand what data is being retrieved and how it is being processed. This transparency bridges the gap between technical configuration and user understanding.
Solution Approach 2:
The patent introduces an intermediary layer that translates technical database operations into user-understandable concepts. This mediator presents data in terms that are meaningful to end users while maintaining the optimized technical operations underneath. Users can interact with data through familiar concepts without needing to understand the complex database configurations that enable those interactions.
Data Source
AI summary
Techniques and solutions are provided for processing query requests from a software application, such as one having a user interface model, using an analytical data protocol that accesses an analytic query. Often, user interface models access data using transactional data protocols, which can limit analytical actions that can be performed through a user interface, particularly actions altering data presented or a data format as compared with pre-defined analytical objects. A query request associated with a user interface query model is received and converted to be executable using at least one analytical query model object. The request, in an analytical protocol, is submitted to a virtual data model. The query request in the analytical protocol is converted to be used with an analytic query defined in the virtual data model. The converted query request is executed against a data store and query results are returned to a user interface layer.