Pivoting Multidimensional Dataset Views for Web Analytics
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Solution Overview
Problem
Visualizing and analyzing large multi-dimensional datasets for web analytics is challenging due to the complexity of handling significant volumes of traffic data with numerous dimensions and metric attributes, making it difficult for information analysts to discover valuable insights for optimizing website usage.
Innovation Solution
A computer-implemented method and system for visualizing multi-dimensional datasets by allowing users to pivot data, where a client device connected to a server system displays a subset of the dataset, receives user requests to partition metric data by a pivot dimension, and updates the display accordingly, enabling users to interactively explore the data by changing axes and filtering options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the volume of traffic data is significant or the metadata includes a large number of dimensions and metric attributes, then the dataset contains more comprehensive information, but the exercise of searching information within the multidimensional dataset becomes non-trivial and more difficult
Solution Approach 1:
The patent applies segmentation by dividing the multidimensional dataset into manageable subsets through slicing operations. The system allows users to define specific slices based on dimension values, breaking down the large complex dataset into smaller, more searchable segments that can be analyzed independently while maintaining the ability to reconstruct the full picture when needed.
Solution Approach 2:
The patent implements dimensionality change by introducing the concept of pivoting between different dimensional perspectives. The system enables dynamic reorientation of the data view by changing which dimensions are used for slicing and which are used for display, effectively transforming the data presentation from one dimensional arrangement to another, making information more accessible without reducing the total data volume.
2Adaptability or versatility
If the metadata includes a large number of dimensions and metric attributes, then the dataset provides more analytical capabilities, but visualizing and analyzing the data becomes more complex and challenging
Solution Approach 1:
The patent applies dynamics by enabling interactive exploration of the multidimensional dataset. The system allows users to dynamically adjust slice definitions, pivot between different dimensional views, and iteratively refine their analysis focus. This dynamic interaction reduces complexity by allowing users to progressively simplify the data view to match their specific analytical needs rather than presenting all dimensions simultaneously.
Solution Approach 2:
The patent uses dimensionality change to manage analytical complexity by allowing pivoting between different dimensional perspectives. The system can transform the presentation of data by changing which dimensions are displayed versus which are used for slicing, effectively rotating the data view to present information in the most analytically useful orientation without adding visual complexity.
Data Source
AI summary
A computer-implemented method for visualizing a multi-dimensional dataset at a client device is disclosed. The client device displays a first view of a subset of the multi-dimensional dataset, including displaying dimension data of a first reference dimension attribute and metric data of a first metric attribute that corresponds to the respective first reference dimension data along a first axis. After receiving a user request to partition the metric data of the first metric attribute by a first pivot dimension attribute, the client device requests and receives dimension data of the first pivot dimension attribute and the corresponding partitioned metric data of the first metric attribute from a server system and displays a second view of the subset of the multi-dimensional dataset, including displaying the first pivot dimension data and the corresponding partitioned metric data of the first metric attribute along the second axis.