Event Heatmap Visualization for Distributed Data Analysis
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Solution Overview
Problem
Enterprise organizations face challenges in analyzing and visualizing large quantities of distributed data, which hinders the identification of valuable insights into operational performance and security, due to the size and isolation of data sources.
Innovation Solution
A method and system for visualizing values over time by receiving user input to specify a field and time range, identifying unique values, and providing a visualization of event counts using a heat map, where each intersection of rows and columns represents the number of events with a specific value and timestamp, allowing for customization of color or shade scales and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected and stored from multiple distributed data sources, then the quantity and comprehensiveness of data increases, but the difficulty of analyzing and identifying patterns increases
Solution Approach 1:
The patent segments the large volume of distributed data into discrete events with standardized structures. Each event represents a unit of data from any data source, allowing the system to process and analyze data in manageable segments rather than as an overwhelming bulk, thereby reducing analysis difficulty while maintaining data quantity
Solution Approach 2:
The patent introduces an intermediary layer (the event processing system) that sits between raw distributed data sources and analysis tools. This intermediary standardizes and contextualizes data from multiple sources into uniform events, making the data more accessible and easier to analyze without losing the comprehensiveness of the original data volume
2Ease of operation
If data is analyzed in isolation from individual data sources, then analysis simplicity is maintained, but valuable enterprise-wide patterns are missed
Solution Approach 1:
The patent merges data from multiple distributed sources into a unified event stream with consistent structure. By combining data while maintaining event-level granularity, the system enables enterprise-wide pattern recognition while preserving the analytical simplicity of standardized data formats, avoiding the need to analyze each source in isolation
3Loss of information
If detailed event data is visualized for multiple unique values, then comprehensive insight is provided, but visualization complexity increases
Solution Approach 1:
The patent transforms complex multi-dimensional event data into a two-dimensional heatmap visualization where one axis represents time slots and the other represents unique field values. This dimensional transformation allows comprehensive visualization of event counts across multiple values and time periods while maintaining visual clarity, avoiding the complexity of traditional multi-axis charts
Data Source
AI summary
Systems and methods are provided for an enhanced graphical user interface (GUI) for presenting events. In particular, a graphical user interface includes, a visualization of unique values. Such a visualization includes a first set of rows for each unique value, wherein each row is divided into regions each representing a timeslot within the time range. Each region in a particular row of the first set of rows graphically illustrates a count of events from the plurality of events that both have an associated timestamp that is within a respective timeslot represented by the region and include a particular unique value associated with the particular row. The graphical user interface also includes a table including a second set of rows that each correspond to the first set of rows. Each row includes a set of statistics determined from events that each include the particular unique value associated with the row.