Faceted Visualization for SPARQL Query Result Exploration
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Solution Overview
Problem
Querying and visualizing large RDF datasets is challenging due to unpredictable result sizes and overwhelming data, limiting user ability to explore and refine query results effectively.
Innovation Solution
A successive refinement approach allows users to input arbitrary RDF queries, replace variables with bound values (facets), and visually explore refined result sets through a graphical user interface, enabling incremental query refinement and visualization of query history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If SPARQL queries are executed over large RDF datasets, then comprehensive data retrieval is achieved, but result set size becomes unmanageably large
Solution Approach 1:
The patent segments the result set by introducing facets that divide the data into manageable categories. Users can select specific facets to filter and organize the vast result set into smaller, more manageable subsets while maintaining comprehensive access to the underlying data.
Solution Approach 2:
The patent adds a dimensional layer by introducing facets as an additional classification dimension. This allows users to navigate the result set not just by traditional query results but by organizing data across multiple facet dimensions, making large datasets more navigable and manageable.
2Loss of information
If query result sets are displayed in full, then complete information is provided, but user ability to parse and explore data is limited
Solution Approach 1:
The patent segments the information presentation by introducing facets that allow users to browse and filter results in organized categories. This segmentation enables users to explore data systematically without being overwhelmed by the complete result set, while still maintaining access to all information through the facet structure.
Solution Approach 2:
The patent introduces facets as an intermediary layer between the complete result set and the user interface. This intermediary structure allows users to navigate and explore data through organized categories, making the data more accessible and easier to parse while preserving information completeness.
3Loss of time
If pre-computed result sets are provided, then response time is reduced, but user flexibility in querying is limited
Solution Approach 1:
The patent performs preliminary computation by pre-computing facet information and result set statistics before user interaction. This allows the system to provide quick responses and facilitate intuitive exploration, while the faceted structure enables users to dynamically refine queries without recomputing the entire result set from scratch.
Data Source
AI summary
Embodiments use successive refinement to allow a user to systematically explore the result set of an arbitrary query over RDF, such as a SPARQL query. A user inputs an arbitrary base query and modifies this query by replacing selected variables with values to which each selected variable is bound within the result set of the base query. Embodiments present, via a GUI, variable facets that may be substituted for query variables. Embodiments also present, through a GUI, a query history graph that represents query versions that a user has created. A user may navigate this query history graph to return to previously-created query versions. The GUI also provides information about the facets, including a number of results that would be included in the result set of the query version resulting from substitution of the facet for the associated variable.