Interest-Driven Distributed Data Server for Dynamic Reporting
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Solution Overview
Problem
Business intelligence systems face challenges in efficiently retrieving and processing large volumes of structured, semi-structured, and unstructured data from diverse sources to meet dynamic reporting requirements, often requiring extensive data processing and handling data silos, while also dealing with unavailable or overloaded data sources.
Innovation Solution
An interest-driven distributed data server system that configures processors to identify and retrieve data from multiple sources, generate data source jobs, and build reporting data based on metadata, dynamically reconfiguring data pipelines to fulfill reporting requirements, and automatically locate and incorporate additional data sources to optimize data retrieval and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is retrieved from multiple diverse sources to meet dynamic reporting requirements, then the versatility and completeness of reporting data is improved, but the system complexity and data processing overhead increase
Solution Approach 1:
The patent introduces a data server system as an intermediary layer between diverse data sources and reporting tools. This server acts as a mediator that receives data from multiple sources (ERP systems, web services, files, etc.), processes and standardizes the data, and makes it available to various reporting applications. The intermediary absorbs the complexity of handling diverse formats and sources, shielding the reporting layer from these complexities.
Solution Approach 2:
The data server system is designed with universal capabilities to handle multiple data source types through a unified interface. It can connect to and process data from various sources (SQL databases, NoSQL databases, web services, flat files, etc.) using common protocols and data formats. This multi-functional design allows a single system to serve diverse reporting needs without requiring separate specialized systems for each data source type.
2Quantity of substance
If data processing operations are performed to handle large volumes of data, then the quantity and quality of reporting data is improved, but the processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary data processing operations at the data server level before data is needed for reporting. This includes pre-fetching data from sources, pre-transforming data formats, pre-aggregating data where appropriate, and pre-validating data quality. By performing these actions in advance, the system reduces the processing time required when actual reporting operations are executed, as the data is already prepared and ready for consumption.
Solution Approach 2:
The data processing workflow is segmented into distinct stages: data collection from sources, data transformation and standardization, data validation and quality checks, and data delivery to reporting tools. This segmentation allows each stage to be optimized independently and enables parallel processing of different data streams, reducing overall processing time while handling large volumes of data from multiple sources.
3Loss of information
If data sources are accessed to fulfill reporting requirements, then the availability of needed data is improved, but the reliability of data access decreases when sources are unavailable or overloaded
Solution Approach 1:
The data server system implements error handling and fallback mechanisms that are prepared in advance for potential data source failures. When a primary data source becomes unavailable or overloaded, the system can switch to alternative sources or use cached data that was previously retrieved and stored. This prior cushioning against failures ensures continuous data availability for reporting operations even when external sources are problematic.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor the status of data sources and adjust data retrieval strategies accordingly. When a data source indicates it is overloaded or experiencing errors, the system receives this feedback and automatically modifies its behavior by trying alternative sources, adjusting query parameters, or using cached data. This continuous feedback loop maintains reliable data access by adapting to changing source conditions in real-time.
Data Source
AI summary
Systems and methods for interest-driven business intelligence systems in accordance with embodiments of the invention are illustrated. In one embodiment, an interest-driven distributed data server system includes a processor, an interest-driven distributed data server application, aggregate data storage, and a data source directory, wherein the interest-driven distributed data server application configures the processor to obtain reporting data requirements, determine at least one data dimension using the reporting data requirements, identify at least one data source identified that can provide data associated with the data dimension, generate data source job data, transmit the data source job data to at least one data source, and receive job response data, store the received job response data in the aggregate data storage, build reporting data based on the reporting data requirements, and transmit the reporting data.