Multi-Dimensional Temporal Data Mining Framework for Distributed Healthcare
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Solution Overview
Problem
Current data mining technologies face challenges in analyzing multi-dimensional, time-dependent data streams from distributed sources, particularly in healthcare, where temporal abstractions and alignments are difficult to manage across multiple sites, leading to inefficiencies in real-time monitoring and secondary data analysis.
Innovation Solution
A system and method for multi-dimensional temporal data mining that involves collecting and cleaning data streams, temporally abstracting them, and aligning them based on specific time points, while managing temporal and relative rules across multiple sites to support real-time or near real-time data mining operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed data mining is used to maintain data security and privacy, then data distribution and security are improved, but temporal alignment and analysis efficiency deteriorate
Solution Approach 1:
The system segments the data mining process into local preprocessing at distributed sites and centralized pattern discovery. Local sites perform temporal abstraction and cleaning of their own data streams, while the central server aggregates results for global pattern detection. This segmentation maintains data distribution for security while improving overall analysis efficiency through parallel processing.
Solution Approach 2:
The patent introduces an intermediary temporal abstraction layer that standardizes time-dependent data from multiple distributed sources before aggregation. This intermediary processing layer reconciles temporal differences between sites, enabling efficient centralized analysis without requiring raw data centralization, thus maintaining security while improving productivity.
2Measurement precision
If temporal abstraction is applied to multiple data streams, then pattern detection capability is improved, but computational complexity increases
Solution Approach 1:
The computational workload is segmented between local sites and central server. Local sites perform temporal abstraction on their own data streams using standardized algorithms, reducing the complexity at any single point. The central server then works with pre-processed results rather than raw multi-dimensional data, significantly reducing its computational burden while maintaining pattern detection capability.
Solution Approach 2:
Temporal abstraction is performed as a preliminary action at local sites before data aggregation. By pre-processing data streams to extract temporal patterns and characteristics locally, the system reduces the dimensionality and complexity of data that needs to be processed centrally, while still enabling sophisticated pattern detection through the aggregated results.
3Speed
If real-time monitoring is implemented across multiple sites, then responsiveness is improved, but data synchronization difficulty increases
Solution Approach 1:
Each distributed site performs self-service temporal abstraction and data preprocessing on its own data streams using standardized local algorithms. This self-service approach at each site eliminates the need for complex centralized synchronization of raw data, as each site independently prepares its data according to the same temporal rules, naturally achieving synchronization without complex coordination mechanisms.
Data Source
AI summary
The present invention relates to a system, method and computer program product that is a multi-dimensional data mining environment and that operable to apply a series of temporal and relative rules (i.e., STDMn0) and is further operable in at least one of the following ways: to incorporate a framework to support temporal abstractions and relative alignments to data (i.e., STDMn0); and to derive characteristics within the data (STDMn0). The present invention may incorporate data from multiple sources, and potentially multiple centers. The analysis and alignment of the data may involve both temporal dimensions and other dimensions (or relative aspects) of the data. The present invention may further be a data mining environment that is flexible enough to permit relatively open ended queries thereby enabling, for example, the detection of trends, including trends with new dimensions, or trends based on relatively small data sets.