Representation Manipulation Language for API Response Optimization
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Solution Overview
Problem
In electronic commerce publication systems, clients are unable to manipulate the rigid query response representations provided by APIs to optimize performance, bandwidth, and response time, limiting their ability to address specific use cases and manage resources effectively.
Innovation Solution
The implementation of a Representation Manipulation Language (RML) that allows clients to configure and manipulate query response representations using Xpath and Map/Reduce functions, enabling the server to provide more processing power and address latency issues, particularly for mobile clients with limited resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed query response representation is provided by the API, then the system structure is simple and reliable, but the client cannot manipulate the response to optimize performance, bandwidth, and response time
Solution Approach 1:
The patent introduces a Representation Manipulation Language (RML) as an intermediary layer between the fixed API response and the client's needs. RML allows clients to manipulate query response representations without changing the underlying API structure, thus providing adaptability while maintaining system simplicity. The RML acts as a mediator that translates between the rigid API response format and the flexible client requirements.
Solution Approach 2:
The patent makes the query response representation dynamic by allowing clients to specify manipulation operations (such as projection, filtering, aggregation) on the response data. Instead of a static fixed response, the system now supports dynamic response customization through RML queries, enabling optimization of bandwidth and response time based on client-specific needs while maintaining the simple fixed API structure.
2Quantity of substance
If the entire query response representation is provided, then the client receives complete information, but bandwidth consumption increases and response time increases
Solution Approach 1:
The patent applies extraction by allowing clients to selectively retrieve only the needed portions of the query response representation through RML manipulation operations. Instead of receiving the entire response, clients can extract specific fields, apply filters, and perform projections to obtain only the necessary information, thus reducing bandwidth consumption while maintaining information completeness for the client's specific use case.
Solution Approach 2:
The patent segments the query response representation into manageable parts that can be selectively retrieved and manipulated. Through RML, clients can divide the response into relevant and irrelevant portions, requesting only the segmented data needed for their purposes. This segmentation approach reduces the overall data transfer volume while preserving the necessary information completeness.
3Quantity of substance
If the entire query response representation is provided, then the client receives complete information, but response time increases
Solution Approach 1:
The patent applies preliminary action by allowing clients to specify the manipulation operations to be performed on the query response representation before the data is fully retrieved and processed. The system can pre-determine which fields are needed and optimize the retrieval process accordingly, reducing response time while maintaining information completeness through the RML query formulation.
4Productivity
If the API structure is modified to allow manipulation, then client performance can be optimized, but the system requires creating or modifying existing APIs
Solution Approach 1:
The patent uses RML as an intermediary that enables client performance optimization without requiring modifications to the underlying API structure. Clients can manipulate query response representations using RML operations while the API remains unchanged, thus achieving improved productivity without the complexity of API modification or creation.
Data Source
AI summary
Receiving data from a client machine, the data representing selection of a subset of components of a set of components of a fixed query response representation; performing a map/reduce function on the data to retrieve the subset of components; and transmitting the subset of components to the client machine. The data may include an Xpath expression that is created at the client machine or at a server and the map/reduce function may be performed according to a design pattern that designates the pattern of components in a fixed query response representation designated by the client machine.