Multi-Dimensional Data Filtering via Client-Side Summaries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face inefficiencies when specifying filter criteria for data sets with multiple dimensions, as it is difficult to visualize relationships between items, leading to time-consuming trial and error processes, especially on mobile devices with low-bandwidth connections.
Innovation Solution
A search system generates a summary of the data set that provides interactive graphical user interfaces on client devices, allowing users to explore filter criteria without constant communication with the server, by summarizing multi-dimensional distributions and displaying them as histograms or other statistical representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users interact with search systems through trial and error to specify filter criteria, then they can eventually find desired information, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system pre-computes and provides summaries of data distributions across multiple dimensions before the user needs to specify filter criteria. This preliminary action allows users to understand the data structure and relationships in advance, enabling them to specify accurate filter criteria without time-consuming trial and error interactions.
Solution Approach 2:
The system provides feedback to users by displaying summaries that show the distribution of data across different dimensions and the relationships between dimensions. This feedback mechanism helps users understand what filter criteria will yield desired results, significantly reducing the time needed for trial and error while improving productivity.
2Difficulty of detecting and measuring
If the search system provides detailed data distributions across multiple dimensions, then users can better understand relationships between filter criteria, but the amount of information processing and communication increases
Solution Approach 1:
The system extracts key information from the multi-dimensional data by generating summaries that highlight important distribution patterns and relationships between dimensions. This extraction process presents only the most relevant information to users, making it easier to visualize relationships while reducing the overall communication bandwidth required compared to transmitting complete data sets.
Solution Approach 2:
The system provides detailed data distributions locally on user devices rather than requiring continuous communication with the server. By caching and storing summaries of data distributions on client devices, the system maintains high-quality information availability while reducing communication overhead, as users can access detailed distribution information without constant server interaction.
3Ease of operation
If the search system processes and displays detailed multi-dimensional data distributions, then users can make informed filter criteria selections, but computational resources and processing time increase
Solution Approach 1:
The system performs computationally intensive tasks such as data distribution analysis and relationship mapping in advance, before the user needs to specify filter criteria. By pre-computing these summaries and storing them on client devices, the system reduces the computational burden during actual user interaction, making filter specification easier while lowering real-time processing requirements.
Solution Approach 2:
The system enables users to perform filter specification operations independently on their local devices using pre-loaded data summaries. This self-service capability allows users to interact with the search system without requiring continuous server processing power, thereby reducing overall computational resource consumption while maintaining ease of operation.
4Reliability
If the search system requires constant communication with the server for filter exploration, then data is always fresh and accurate, but communication time and bandwidth consumption increase significantly
Solution Approach 1:
The system performs data processing, distribution analysis, and summary generation in advance and caches these results on user devices. This preliminary action ensures that users can explore filter criteria locally with fresh and accurate data without requiring constant communication with the server, significantly reducing communication time while maintaining data reliability.
Solution Approach 2:
The system creates local copies of data summaries and distribution information on user devices rather than requiring continuous server communication. These local copies maintain the accuracy and freshness of the data by being updated periodically or on-demand from the server, allowing users to explore filter criteria independently without significant communication overhead.
Data Source
AI summary
Interactive and dynamic exploring of filter criteria for large amounts of numerical data having multiple dimensions is described. Exploration occurs without excessive amounts of computation or querying of a database based on a data set summary that may be obtained from a server in a search system and applied by a client device. A data set is summarized so as to capture relationships between distributions of items in the data set along multiple dimensions. To explore filter criteria, a user may interactively adjust a range of values that act as a filter criteria along a dimension and determine an effect on a distribution of items along another dimension. The client device may apply the data summary in reverse to determine, based on user input specifying aspects of a distribution along one dimension, filter criteria for another dimension that would result in the specified distribution.