Hierarchical Histogram Trees for Flexible Binary Data Binning
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Solution Overview
Problem
Existing histogram generation methods are inflexible and inefficient, requiring multiple passes through the dataset for different binning scenarios and lacking the ability to easily change binning granularity.
Innovation Solution
A method involving data elements in a uniform binary format, organized into a tree structure with nodes counting data elements hierarchically, allowing multiple histograms to be generated efficiently by traversing only the necessary nodes for the desired granularity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional histogram generation methods are used, then histograms can be generated for specific binning scenarios, but the process requires multiple passes through the dataset and cannot efficiently change binning granularity
Solution Approach 1:
The patent segments the histogram generation process into a hierarchical tree structure where data elements are divided into multiple levels of bins. Each node in the tree represents a bin or group of bins, allowing the system to efficiently traverse and aggregate counts at different levels without reprocessing the entire dataset. This segmentation enables flexible binning scenarios while maintaining computational efficiency.
Solution Approach 2:
The patent performs preliminary action by pre-organizing data elements into a hierarchical tree structure during an initial pass. This preliminary organization allows subsequent histogram generation for different binning scenarios to be achieved by traversing the pre-built tree rather than reprocessing the raw data, significantly improving productivity while maintaining adaptability.
2Measurement precision
If additional passes through the dataset are made for different binning granularities, then histograms can be generated at various levels of detail, but the computational workload is multiplied
Solution Approach 1:
The patent implements a nested hierarchical tree structure where each node contains child nodes representing finer-grained bins. This nesting allows the system to access histograms at different granularities by traversing to appropriate levels in the tree, eliminating the need for multiple dataset passes while maintaining various levels of measurement precision.
Solution Approach 2:
The hierarchical tree structure serves multiple functions: it stores data elements, organizes them by binning scenarios, and enables efficient retrieval of histogram data at different granularities. This multi-functionality eliminates the need for separate processing passes for each granularity level, reducing time loss while maintaining measurement precision.
3Reliability
If the binning process is rerun from scratch for different binning scenarios, then accurate histograms can be generated for each scenario, but the computational workload is multiplied
Solution Approach 1:
The patent creates a hierarchical tree structure that copies and organizes data elements into multiple levels of bins. This tree structure can be traversed to generate histograms for different binning scenarios without reprocessing the original data, maintaining accuracy while significantly reducing computational workload compared to rerunning the binning process from scratch.
Solution Approach 2:
The system performs preliminary organization of data into the hierarchical tree structure during an initial pass. This preliminary action establishes the foundation for generating accurate histograms for multiple binning scenarios without requiring subsequent reprocessing, thereby maintaining reliability while improving productivity.
Data Source
AI summary
A technique of generating histograms includes providing data elements in a uniform binary format as multiple consecutive chunks, where each chunk includes a sequence of consecutive binary digits. The technique includes placing the data elements in nodes of a tree based on the chunks. The nodes are arranged in successive levels that correspond to successive chunks. Each node counts the data elements placed in that node and in any child node of that node at lower levels of the tree. The technique further includes traversing one or more nodes of the tree to generate a histogram of the data elements counted by that node or nodes.