Compressed Analytics Data Stream for Network Sites
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Solution Overview
Problem
Transmitting and processing large amounts of site analytics data strain data storage and communication resources, and maintaining real-time data availability is challenging in dynamic network-based sites.
Innovation Solution
Compressing analytics data into a compressed analytics data stream by identifying recurring time periods and adding data values as either baseline or difference objects, interleaved according to a time-based ordering, to reduce storage and transmission burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If large amounts of site analytics data are transmitted and processed, then comprehensive user interaction insights are obtained, but data storage and communication resources are strained
Solution Approach 1:
The patent segments analytics data by recurring time periods (e.g., days of the week, hours of the day) and processes each segment separately. This allows the system to identify patterns within each time period segment and store only the essential characteristics, reducing overall data volume while preserving user interaction insights.
Solution Approach 2:
The patent transforms raw analytics data into a compressed representation by changing parameters - storing baseline values for each time period and only recording deviations from those baselines. This parameter transformation significantly reduces the quantity of data that needs to be stored and transmitted while maintaining the ability to reconstruct complete insights.
2Speed
If real-time data availability is maintained in a dynamic environment, then current data access is enabled, but processing and storage complexity increases
Solution Approach 1:
The patent performs preliminary processing of analytics data by pre-calculating baseline values for each recurring time period and pre-organizing data in a compressed format. This preliminary action enables faster real-time access because the heavy processing work is done in advance, reducing the complexity of real-time operations.
Solution Approach 2:
The patent leverages the periodic nature of recurring time periods to apply consistent processing rules and patterns. By recognizing that certain time periods repeat (e.g., Mondays repeat weekly), the system can use periodic action to maintain data availability efficiently, applying the same compression and storage logic across all periods.
Data Source
AI summary
Analytics data for a network-based site may be compressed according to recurring time periods. An analytics service may obtain analytics data for network-based sites to compress into a compressed analytics data stream. To compress the analytics data, the analytic service may identify a particular time period corresponding to each analytic data value and may add the analytic data value to the compressed analytics data stream as either a baseline object for the particular time period or a difference object relative to an existing baseline object for the particular time period. These objects may be interleaved according to a time-based ordering of multiple different recurring time periods. An analytic service may send the compressed analytics data stream to an analytics client. The analytics client may decompress a portion of the compressed analytics trend without decompressing the remaining portions of the compressed analytics data stream.