Time Series Data Processing via BOTS Representation
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Solution Overview
Problem
Existing systems face performance and data modeling challenges in handling time series data, particularly due to the complexity of implementing time series at the application layer, which affects system performance and requires significant resources, and existing solutions either focus on performance or simplicity but not both simultaneously.
Innovation Solution
The Basic Object of Time Series (BOTS) representation and Reverse Order (RO) methods allow time series to be treated as a basic object in the data store layer, enabling efficient query processing and simplifying data organization, thereby addressing both performance and data representation challenges by reducing the complexity of time series implementation and minimizing additional effort needed to handle the time domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time series are implemented at the application layer, then generality of products is maintained, but system performance deteriorates and resource requirements increase
Solution Approach 1:
The patent introduces a specialized time series processing module as an intermediary layer between the application layer and data store layer. This module handles time series-specific operations (such as temporal queries, trend analysis, and time-based aggregations) efficiently, preventing these specialized operations from degrading the performance of the general-purpose application layer while maintaining product generality.
Solution Approach 2:
The system architecture is segmented into distinct layers: a general application layer for product generality, a specialized time series processing module for performance-critical temporal operations, and a data store layer. This segmentation allows each layer to be optimized independently, with the time series module handling only temporal workloads to avoid resource contention with other application functions.
2Adaptability or versatility
If time series are implemented at the application layer, then product versatility is maintained, but complexity of implementation increases
Solution Approach 1:
The time series processing module serves as an intermediary that encapsulates complex time series logic, presenting a simplified interface to the application layer. This allows the system to maintain versatility by supporting complex temporal operations while hiding the implementation complexity within the specialized module, reducing the burden on application developers.
Solution Approach 2:
The system incorporates feedback mechanisms where the time series processing module monitors performance metrics and data patterns, automatically optimizing query execution plans and resource allocation. This feedback loop reduces manual configuration complexity while maintaining high versatility in handling diverse time series workloads.
3Productivity
If existing time series solutions are used, then performance requirements are addressed, but data representation complexity increases
Solution Approach 1:
The patent applies local quality by optimizing data representation specifically for time series workloads within the specialized processing module, without imposing this complexity on the entire system. The time series module uses efficient temporal data structures (such as time-series-specific indexing and compression) only where needed, while the rest of the system maintains simple, generic data representations.
Solution Approach 2:
The system dynamically changes data representation parameters based on the specific time series workload characteristics. The time series processing module automatically adjusts compression ratios, indexing strategies, and storage formats according to data patterns and query requirements, achieving high performance without requiring complex manual configuration of data representation.
Data Source
AI summary
A method of effectively representing and processing data sets with time series is disclosed. The method may comprise representing time series as a virtual part of data in a data store layer of a user system, thereby allowing processing of time-series related queries in said data store layer of said user system.