Timeline Analytics Framework for Stateful Metric Computation
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Solution Overview
Problem
Existing data processing systems face high development complexity and poor cost-performance tradeoffs when handling context-sensitive stateful computations over continuously-evolving system and user/machine state.
Innovation Solution
A timeline framework for time-state analytics that models attribute evolution over time, enabling efficient configuration and computation of time-state metrics through timeline representations and operators, reducing the need for complex coding and improving computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional tabular or relational models are used for stateful computations, then the system can process data, but development complexity and time increase significantly
Solution Approach 1:
The patent introduces a timeline framework as an intermediary layer between raw event data and analytical queries. This timeline framework transforms continuous event streams into discrete time-state representations, simplifying complex stateful computations while maintaining computational efficiency. The timeline acts as a mediator that converts unstructured event data into a structured format that is easier to query and analyze.
Solution Approach 2:
The patent transforms the parameter representation of time-state data by introducing timeline-based parameters instead of traditional tabular parameters. This parameter transformation enables more efficient stateful computations by changing how time and state are represented, allowing for reduced development complexity while maintaining or improving computational efficiency.
2Reliability
If existing data processing systems handle continuously-evolving state, then they can provide stateful computations, but cost-performance tradeoffs deteriorate
Solution Approach 1:
The patent segments continuously-evolving state into discrete time-state units organized along a timeline. By dividing the continuous state stream into manageable time-based segments, the system maintains accurate stateful computations while reducing overall system complexity. Each segment represents a specific state at a specific time, making the complex continuous state easier to manage and process.
Solution Approach 2:
The patent adds a time dimension to the state representation, transforming traditional flat state data into multi-dimensional time-state data. This dimensional transformation enables more efficient handling of continuously-evolving state by organizing data along the time axis, improving both reliability of stateful computations and reducing system complexity through better data organization.
3Measurement precision
If fine-grained metrics are computed in real-time, then actionable insights are obtained, but resource usage increases
Solution Approach 1:
The patent applies partial action by computing only the necessary time-state metrics required for specific analytical queries rather than computing all possible fine-grained metrics. The timeline framework allows selective computation of metrics based on query requirements, maintaining measurement precision for needed metrics while avoiding unnecessary computational resource usage for unused metrics.
Solution Approach 2:
The patent performs preliminary organization of event data into timeline structures before metric computation. By pre-organizing data along the time axis and establishing the timeline framework in advance, the system enables efficient real-time metric computation with reduced resource usage. This preliminary structuring eliminates the need for expensive post-hoc data organization during metric calculation.
Data Source
AI summary
Determining a time-state metric includes receiving a stream of raw data values of an attribute. Each received raw data value of the attribute is associated with a timestamp. It further includes converting the received stream of raw data values into a timeline representation of the attribute over time. The timeline representation comprises a sequence of spans. A span comprises a span start time, a span end time, and a span value. The span value comprises an encoding of one or more values of the attribute over a time interval determined by the span start time and the span end time. It further includes determining a time-state metric according to a timeline request configuration. The timeline request configuration comprises one or more timeline operations. The time-state metric is computed at least in part by performing a timeline operation on the timeline representation of the attribute.