Network Data Layering for Efficient Search
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Solution Overview
Problem
As networks grow in size and complexity, managing large amounts of data generated during monitoring becomes increasingly difficult for administrators, leading to inefficiencies and potential missed important information, as searching through this data is time and resource-consuming.
Innovation Solution
A method and system for managing network data by receiving and correlating machine-sourced and human-sourced information, storing it in data layers defined by user-provided parameters, and allowing users to search and access specific data efficiently, reducing the burden on resources and improving data consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If administrators manually search through all network monitoring data, then they can find specific operational information, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent segments the large network data into multiple data layers based on different parameters (time periods, data types, sources, priorities). Each layer contains specific subsets of data, allowing administrators to search only relevant layers rather than scanning all data, thus reducing search time while maintaining retrieval accuracy.
Solution Approach 2:
The patent introduces an intermediary data layering mechanism that automatically organizes and indexes network data before presentation to administrators. This intermediary structure acts as a mediator between raw data and user queries, enabling efficient retrieval without administrators manually searching through all原始数据.
2Quantity of substance
If all network data is stored in a single structure, then data completeness is maintained, but data management and retrieval become increasingly difficult as network size grows
Solution Approach 1:
The patent divides the single data structure into multiple segmented data layers, each organized by specific parameters such as time periods, data types, sources, and priorities. This segmentation maintains data completeness across all layers while making management and retrieval significantly easier through targeted access to specific layers based on query requirements.
Solution Approach 2:
The patent adds dimensional organization to the data structure by creating layers along multiple parameters (time, type, source, priority). This multi-dimensional organization transforms the single flat structure into a hierarchical, multi-layered structure that preserves data completeness while enabling efficient management through parameter-based navigation.
3Reliability
If network monitoring collects all possible data, then comprehensive monitoring coverage is achieved, but the complexity of managing and analyzing the data increases
Solution Approach 1:
The patent segments comprehensive network monitoring data into multiple organized layers based on parameters like time periods, data types, sources, and priorities. This segmentation maintains comprehensive monitoring coverage across all network aspects while reducing management complexity by allowing targeted access and analysis of specific data layers rather than handling all data uniformly.
Solution Approach 2:
The patent applies local quality by organizing different data layers with specific characteristics suited to their content. Each layer has optimized properties for its particular data type (e.g., time-series optimization for temporal data, filtering for specific data types), which maintains comprehensive monitoring coverage while reducing overall system complexity through specialized local structures.
Data Source
AI summary
Aspects of the present disclosure involve systems and methods for summarizing large amounts of data over time into one or more data layers. The systems and methods provide for storing data from a large data feed, which may include machine-sourced and human-sourced information, into one or more layers that are defined by layer parameters. A user of an interface may provide various parameters that define the portion or portions of the raw data feed to be included in the layer. With the received parameters, the system may analyze the raw data feed as it is received at the monitoring or collecting system to identify instances of data that match the received parameters. Through the system, data from a large raw data feed is searched and made available to a network administrator for easier management of the network without consuming vast network resources and administrator time.