Temporal Data Visualization System with Metadata Segmentation
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Solution Overview
Problem
Current data processing systems struggle to effectively capture and visualize temporal data relationships from diverse sources, often obscuring important metadata due to voluminous data volumes and lack of efficient metadata handling.
Innovation Solution
A data capture and visualization system that utilizes a platform with features like data origin tracking, temporal deduplication, and transformation using OPAL query language, integrated with cloud services like AWS and Snowflake, to manage and present temporal data relationships across various sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from diverse sources is captured and processed, then data volume increases providing more information, but metadata becomes obscured and difficult to manage
Solution Approach 1:
The patent segments data into distinct components: event data, timestamp data, and metadata (origin information). Each component is processed and stored separately, allowing metadata to be maintained independently from the voluminous event data. This segmentation prevents metadata from being obscured by large data volumes.
Solution Approach 2:
The patent introduces an intermediary processing layer that captures origin metadata before data enters the main processing pipeline. This intermediary component extracts and preserves metadata about data sources, timestamps, and contextual information separate from the primary data flow, preventing information loss despite increased data volume.
2Loss of information
If temporal data relationships are captured across multiple sources, then data relationships become more complex and valuable, but processing and visualization become more difficult
Solution Approach 1:
The patent performs preliminary processing of temporal data relationships by normalizing and standardizing data from multiple sources before full processing. Timestamps are standardized, origin metadata is captured upfront, and data is pre-filtered to identify meaningful relationships. This preliminary action reduces the complexity of subsequent processing while preserving temporal relationships.
Solution Approach 2:
The patent transforms complex temporal data by changing parameters such as timestamp formats, data representations, and relationship descriptors. By standardizing these parameters across diverse data sources, the system simplifies processing while maintaining the integrity of temporal relationships, making visualization more manageable.
3Reliability
If comprehensive metadata is retained for data origin tracking, then data provenance is improved, but data processing overhead increases
Solution Approach 1:
The patent extracts only the essential metadata elements needed for data provenance tracking (origin source, timestamp, contextual identifiers) while leaving out unnecessary or redundant metadata. This selective extraction maintains data reliability and provenance information while minimizing processing overhead associated with handling comprehensive metadata sets.
Data Source
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AI summary
A data capture and visualization system providing temporal data relationships is disclosed. An example embodiment is configured to: capture and forward data using collection agents; buffer and load the captured data into a data warehouse; transform the data as specified by a temporal algebra query language; enable querying of the transformed data using the temporal algebra query language, the querying including temporal data relationships; and present the results of the queries to a user via a user interface.