Linear Graph Visualization for Multi-Source Web Data Organization
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Solution Overview
Problem
Current web-based information systems lack effective methods to visually organize and compare data from multiple sources in a comprehensible format, restricting users to limited graphing options and preventing the simultaneous display of related information from different web sites.
Innovation Solution
A method and system that extract and organize facts from web pages into a linear graph format, allowing users to visualize data in a linear order by identifying attributes and values, and presenting them in a customizable and comprehensible manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If web sites provide graphing abilities for visualizing data, then data visualization capability is improved, but the user is restricted to a limited set of graphing options
Solution Approach 1:
The system segments the graphing functionality by automatically detecting and separating different data types (temporal, spatial, categorical) from the raw web data. Each data type is then processed through specialized visualization modules, allowing flexible combination of graph types without requiring users to manually configure complex parameters.
Solution Approach 2:
The system changes the parameters of data representation by automatically inferring temporal, spatial, and categorical dimensions from unstructured web data. This transformation enables the same raw data to be visualized in multiple graph types (line charts, bar charts, scatter plots) without manual intervention, expanding graphing options while maintaining interface simplicity.
2Adaptability or versatility
If users want to graph and compare data from across different web sites, then data comparison capability is improved, but existing systems do not permit this functionality
Solution Approach 1:
The system implements a universal data extraction and normalization layer that can ingest data from multiple web sites with different structures and formats. This universal interface abstracts away the complexity of accessing diverse data sources, enabling users to compare data across sites through a single unified graphing interface without needing to understand the underlying data aggregation complexity.
Solution Approach 2:
The system introduces an intermediary data normalization layer that acts as a mediator between raw web data from different sources and the visualization interface. This intermediary layer standardizes data formats, resolves inconsistencies, and creates a unified representation that enables cross-site comparison while hiding the complexity of data aggregation from the user.
3Ease of operation
If information on the web is presented in text format, then information completeness is improved, but ease of comprehension is reduced
Solution Approach 1:
The system adds temporal, spatial, and categorical dimensions to the flat text data structure by automatically detecting patterns and relationships in the data. This dimensional transformation converts one-dimensional text information into multi-dimensional visual representations, making the data more comprehensible while preserving complete information through interactive exploration capabilities.
Data Source
AI summary
A set of objects having facts is established. Facts of objects having positions in an order are identified. Some facts explicitly describe the positions in the linear order, while are facts do not explicitly describe the positions. The facts are presented in the order on a linear graph, such as a timeline. Facts of the objects describing geographic positions are presented on a map.