Multitemporal Data Analysis via Directed Computation Graph
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Solution Overview
Problem
Current data analysis systems are inefficient in handling large amounts of data and real-time streaming data, as they often require manual curation and lack flexibility, making it difficult to create, share, and distribute data analysis models across different applications and platforms.
Innovation Solution
A system and method for multitemporal data analysis that allows for programmatically analyzing both large amounts of stored data and real-time streaming data, using a distributed architecture with directed computational graph analysis and transformer services to map, split, and transform data, enabling flexible and scalable data analysis across various applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a system is designed to analyze large amounts of stored data, then data analysis capability is improved, but real-time streaming data handling capability deteriorates
Solution Approach 1:
The system divides data processing into separate specialized components: one for batch processing of stored data and another for real-time streaming data handling. This segmentation allows each component to be optimized for its specific function without compromising the other capability.
Solution Approach 2:
The system creates a unified data analysis platform that can handle both large volumes of stored data and real-time streaming data through a common architecture. The system uses a standardized data model and processing framework that works across different data types and temporal characteristics.
2Measurement precision
If data analysis models are manually curated, then analysis accuracy is improved, but time consumption and complexity deteriorate
Solution Approach 1:
The system pre-defines standardized data models, analysis templates, and processing pipelines that can be directly applied to common data analysis scenarios. This preliminary preparation eliminates the need for manual model curation while maintaining analysis accuracy through proven, pre-tested approaches.
Solution Approach 2:
The system enables replication and distribution of data analysis models across multiple applications and platforms. Once a model is created or selected, it can be copied and reused indefinitely without additional manual curation effort, significantly reducing time consumption while preserving analysis quality.
3Productivity
If a system is designed for batch data processing, then processing depth is improved, but real-time responsiveness deteriorates
Solution Approach 1:
The system dynamically adjusts its processing mode based on the characteristics of the input data and requirements of the application. It can switch between deep batch processing for historical data and rapid real-time processing for streaming data, optimizing the balance between processing depth and responsiveness according to current needs.
Data Source
AI summary
A system for multitemporal data analysis is provided, comprising a directed computation graph service module configured to receive input data from a plurality of sources, analyze the input data to determine a best course of action for analyzing the input data, and split the input data for queueing to a general transformer service module or a decomposable service module based at least in part by analysis of the input data; a general transformer service module configured to receive data from the directed computation graph service module, and perform analysis on the received data; and a general transformer service module configured to receive data from directed computational graph module, and perform analysis on the received data.