Visualizing Anomalous Feature Vectors in Healthcare Data
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Solution Overview
Problem
Current data intake and query systems face challenges in efficiently searching and analyzing large sets of raw machine data due to the lack of user-friendly tools for visually identifying data subsets of interest, particularly in diverse and rapidly growing data environments.
Innovation Solution
A data intake and query system architecture that includes a flexible schema for event-based data processing, allowing for late-binding schema application during search time, and utilizing a metadata catalog for dynamic query execution and data extraction, enabling efficient indexing and querying of machine data across disparate sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data systems store and pre-process only specified data items to facilitate efficient retrieval, then retrieval efficiency is improved, but data flexibility and completeness are reduced
Solution Approach 1:
The system performs preliminary actions by ingesting and storing all raw machine data without pre-processing or filtering. This allows the data to be readily available for retrieval while maintaining flexibility for various analysis needs. The preliminary action of complete data ingestion eliminates the need for later pre-processing steps that would compromise data completeness.
Solution Approach 2:
The system implements dynamic data processing where the level of processing applied to data is not fixed but adapts based on the specific analysis needs. Data can be processed to different degrees depending on the query requirements, allowing the system to optimize between retrieval speed and analysis flexibility dynamically rather than being constrained by a static pre-processing approach.
2Loss of information
If tools are designed to search and analyze large sets of raw machine data comprehensively, then data analysis completeness is improved, but user accessibility and ease of use are reduced
Solution Approach 1:
The system introduces an intermediary layer between the raw machine data and the user interface. This intermediary includes components that automatically process, index, and organize the raw data, making it accessible through user-friendly search and visualization tools. The intermediary handles the complexity of comprehensive data analysis while presenting simplified interfaces to users, thus maintaining both completeness and accessibility.
3Adaptability or versatility
If massive quantities of raw data are stored for later analysis, then data completeness and flexibility are improved, but storage costs and data management complexity increase
Solution Approach 1:
The system segments the data management process into distinct stages: data ingestion, raw data storage, indexed data storage, and analysis execution. This segmentation allows raw data to be stored in its original form for completeness while separate indexing structures organize the data for efficient retrieval. The segmentation reduces management complexity by handling different data forms in separate systems rather than requiring a single complex system to manage both raw and processed data.
Data Source
AI summary
Medication security and healthcare privacy analytics systems are described that enable users to search for and process stored healthcare environment data. The medication security and healthcare privacy analytics systems receive and correlate data from a plurality of data sources, including medication dispensing systems, healthcare employee records, and patient records. The medication security and healthcare privacy analytics systems generate a plurality of feature vectors from processed healthcare environment data. The visualizations are created using datasets generated by clustering algorithms and can indicate those feature vectors from the plurality of feature vectors whose data indicate anomalous interactions with various systems (e.g., indicative of unexpected or non-customary events).