Stateful Counter Aggregation for Device Data Analytics
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Solution Overview
Problem
Conventional device monitoring approaches utilize stateless counter aggregation techniques, leading to incomplete and erroneous datasets due to gaps and errors when members are added or removed, or when counters are reset unexpectedly, which are disadvantageous for analytics and device management.
Innovation Solution
Implementing a stateful counter aggregation algorithm that leverages historical data to compute accurate and complete aggregated counter values by ignoring negative deltas and ignoring new or removed members, ensuring cumulative aggregate delta values are monotonically increasing and non-negative.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If stateless counter aggregation techniques are used, then the aggregation process is simple, but the dataset becomes incomplete and erroneous when members are added or removed
Solution Approach 1:
The system performs preliminary actions by storing historical counter values and member information before aggregation is needed. When aggregation occurs, it retrieves these pre-stored historical data to compute accurate aggregate values, ensuring dataset completeness without adding complex real-time processing logic.
Solution Approach 2:
The patent introduces an intermediary mechanism that tracks individual member counter values and uses them as mediators to compute aggregate values. This intermediary layer of tracking enables accurate aggregation even when members are added or removed, resolving the contradiction between simplicity and reliability.
2Speed
If stateless counter aggregation techniques are used, then the computation is fast, but gaps appear in the historical dataset when members are added or removed
Solution Approach 1:
The system performs preliminary actions by continuously storing individual member counter values and metadata in historical data structures. When aggregation is needed, it retrieves these pre-stored values to compute aggregate metrics, eliminating gaps without requiring complex real-time computations.
Solution Approach 2:
The patent implements feedback mechanisms where the aggregation system continuously monitors member changes and updates historical data accordingly. This feedback loop ensures that when members are added or removed, the system detects these changes and computes appropriate aggregate values to fill potential gaps, maintaining continuous historical data.
3Ease of operation
If conventional aggregation approaches are used, then the processing is straightforward, but erroneous values are inserted when counters are reset unexpectedly
Solution Approach 1:
The patent introduces an intermediary validation layer that tracks individual member counter values before aggregation. This intermediary tracking enables detection of unexpected resets by comparing current values against historical data, allowing the system to identify and exclude erroneous values while maintaining processing simplicity.
Solution Approach 2:
The system implements feedback through continuous monitoring of counter values against historical baselines. When a counter reset occurs, the feedback mechanism detects the anomaly by comparing the reset value with expected ranges derived from historical data, enabling the system to flag or correct erroneous aggregate values while maintaining straightforward processing.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automated stateful counter aggregation of device data are provided herein. An example computer-implemented method includes obtaining historical aggregate counter data and historical individual member counter data associated with a variable set of device members and a given temporal period; computing one or more stateful aggregate counter data values attributed to at least a portion of the variable set of device members for a given temporal value by applying at least one stateful counter aggregation algorithm to the obtained data; and performing one or more automated actions based at least in part on the one or more computed stateful aggregate counter data values.