Data Consistency Windows for Time-Aligned Analytics
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Solution Overview
Problem
In distributed networked computer environments, ensuring data consistency across distinct datasets from various sources is challenging due to asynchronous data arrival and different time intervals, making it difficult to provide time-aligned data for analytics, especially in federated architectures where data is constantly changing.
Innovation Solution
A data analytics system that stores and formats data with consistency windows and data resolution times, allowing for arbitrary groups of data to be time-aligned without regard to their meaning, and provides time-aligned data in response to queries by determining a reference time based on the query time, consistency time window, and data resolution of each dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected from distributed sources in real-time with different time intervals, then data freshness and granularity are improved, but data consistency and time alignment deteriorate
Solution Approach 1:
The system pre-processes incoming data streams by assigning timestamps and organizing data into time-aligned buckets before analytics processing. This preliminary organization ensures that when analytics queries are executed, the data is already structured for consistent time-based comparison across multiple sources, eliminating the need for complex runtime alignment operations.
Solution Approach 2:
The patent introduces an intermediary data normalization layer between distributed data sources and the analytics engine. This intermediary component standardizes data formats, applies consistent time window definitions, and reconciles different sampling intervals before data reaches the analytics processing stage, thereby maintaining both granularity and consistency.
2Ease of operation
If data analytics are embedded directly in the enterprise platform, then user accessibility and intuitiveness are improved, but data alignment complexity increases
Solution Approach 1:
The patent extracts the complex data alignment and time synchronization logic from the embedded analytics interface and places it in a separate backend processing layer. This allows the user-facing embedded analytics to remain simple and intuitive, while the complex alignment operations are handled transparently by the underlying infrastructure that normalizes data before presentation.
Solution Approach 2:
The system implements self-service data alignment mechanisms where the analytics platform automatically performs time synchronization and consistency checks without requiring user intervention. The platform autonomously manages time window alignment, data format standardization, and consistency validation, freeing users from dealing with alignment complexity while maintaining ease of access.
3Reliability
If traditional separation between live data and analytic data is maintained, then data integrity is preserved, but analytics usability and contextual relevance deteriorate
Solution Approach 1:
The patent merges the previously separated live data and analytic data into a unified data model that maintains the integrity of source data while enabling contextual analytics. The system creates a unified view where live data streams and analytical processing coexist, allowing users to access analytics directly in the context of the enterprise platform without compromising data integrity through traditional copying mechanisms.
Data Source
AI summary
Systems and methods for providing consistent and time aligned data from arbitrary sets of data in a data analytics platform are disclosed. Embodiments of such systems and methods may format datasets comprising data received from various data sources to facilitate the provisioning of time aligned data from these datasets based on a time specified in a query for one or more of those datasets.