Iterative Spatial Search via XML Parsing
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Solution Overview
Problem
Current spatial search applications are difficult for users to navigate, as they lack flexibility in defining and refining the spatial extent of searches, leading to inefficient data retrieval and limited ability to discover new information.
Innovation Solution
A flexible database and iterative spatial search process that uses XML strings to parse user input, allowing users to refine search criteria by adding or deleting attributes from previous search results, enabling a more dynamic and efficient search methodology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users perform spatial search with fixed search criteria, then search results are obtained, but users cannot adequately specify spatial extent or refine search to discover new information
Solution Approach 1:
The search system transitions from static to dynamic operation by allowing users to iteratively refine search criteria. The interface enables users to add, remove, or modify search attributes based on previous results, creating a living search process that adapts to user needs and discovers new information progressively.
Solution Approach 2:
The system implements feedback mechanisms where search results inform subsequent search criteria. Users receive feedback about returned datasets and can use this information to refine their search specifications, creating an iterative loop that improves search effectiveness and enables discovery of unexpected data.
2Productivity
If search criteria are rigid and fixed, then search process is simple, but scope of results is limited and new data cannot be discovered
Solution Approach 1:
The search criteria are segmented into multiple independent attributes that can be individually manipulated. Users can selectively add, remove, or modify specific attributes without affecting the entire search query, enabling flexible refinement while maintaining processing efficiency through structured attribute handling.
Solution Approach 2:
The search system evolves from rigid to dynamic by allowing real-time modification of search criteria based on returned results. This dynamic approach enables users to expand or narrow search scope iteratively, improving both productivity through efficient querying and adaptability through flexible refinement.
3Adaptability or versatility
If users manually edit search criteria after receiving results, then search can be refined, but time is lost and efficiency is reduced
Solution Approach 1:
The system prepares for refinement by pre-organizing search results and attributes in a way that facilitates quick iteration. Previous search results are structured to enable rapid conversion into new search criteria, reducing the time needed for refinement through preliminary organization of data.
Solution Approach 2:
Feedback mechanisms provide users with structured information about returned results that can be directly leveraged for refinement. This feedback reduces refinement time by eliminating the need to re-analyze raw data, as the system already organizes results in a refinement-friendly format.
Data Source
AI summary
A flexible database and iterative spatial search process is described. In an embodiment, a flexible database server is described which takes input queries in the form of XML strings describing a search specification and parses them using a stored procedure. Searching is performed by generating a temporary table for each term in the search specification and then comparing the temporary tables to pre-existing tables within the database to identify datasets that meet the search specification. An application is also described which generates the XML string in response to user input and which displays the results to a user. The application provides a user interface which enables users to select attributes of results, such as the spatial data associated with a dataset, to include in a second search specification and to trigger this new search.