Metafutures Graphed Data Lookup Optimization
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Solution Overview
Problem
In computerized environments handling graphed data, numerous repeated queries are required to optimize the state and organization of hierarchical serial numbers, leading to inefficient processing and lookups, as existing optimization strategies fail to maximize batched lookups effectively.
Innovation Solution
A method is introduced that defines a metafuture with initial data references to determine dependencies, initiating a recursive graph traversal, and using aggregation functions to optimize lookups by traversing the graph laterally or vertically, maximizing batch calls while minimizing re-traversals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional query optimization strategies are used for graphed data, then some level of query performance is achieved, but the number of repeated queries remains high and batched lookups are not maximized
Solution Approach 1:
The patent performs preliminary graph traversal to identify all data references and dependencies before executing the actual query. By pre-computing the traversal path and batching all necessary lookups in advance, the system eliminates repeated queries and maximizes batched data retrieval efficiency
Solution Approach 2:
The patent merges multiple individual data lookups into a single batched operation by collecting all required data references during the preliminary traversal phase. This combining of separate lookup operations into one unified batch call significantly reduces the total number of queries and improves overall processing efficiency
2Reliability
If graph traversal is performed without optimization, then all data can be retrieved, but extraneous processing and re-traversals increase significantly
Solution Approach 1:
The patent segments the graph traversal process into distinct phases: a preliminary traversal to identify all data references and dependencies, followed by an optimized execution phase that retrieves only the necessary data. This segmentation eliminates extraneous processing by clearly separating the discovery phase from the retrieval phase
Solution Approach 2:
The patent uses feedback from the preliminary traversal to inform and optimize the subsequent query execution. By analyzing the traversal results to identify all data references and their dependencies, the system can construct an optimized batched lookup strategy that retrieves exactly the right data without unnecessary re-traversals or processing
3Measurement precision
If data lookups are performed individually rather than in batches, then each lookup can be precise, but the overall processing efficiency decreases
Solution Approach 1:
The patent performs preliminary graph traversal to precisely identify all data references and their hierarchical relationships before batching the lookups. This preliminary analysis ensures that the subsequent batched operation retrieves exactly the required data with the same precision as individual lookups, while gaining the efficiency benefits of batching
Data Source
AI summary
A computerized query optimization methodology, computer program product, and electronic device for intelligently optimizing the lookup of graphed data in a data store or distributed across a plurality of databases is disclosed. The method defines an initial metafuture including a plurality of attributes starting with initial data references to determine a starting list of data reference dependencies with respect to metafuture construction functions. In response to a query, a processor initiates a recursive graph traversal of nodes in a graph starting with the initial metafuture as a root node. At each step of the graph traversal, if the metafuture contains an answer, then the process traverses the graph laterally. If there are no further siblings, then the processor initiates a vertical traversal. If all of the children of the metafuture contain answers and the metafuture has no dependency tuples, then an answer of the metafuture is populated by running a graphing function with the answers of the children metafutures, and continuing the traversal either laterally, or vertically if there are no further siblings. If the metafuture is unvisited in the current traversal, then the process adds all not in-map data references in the work list to a needed list, and resolves all in-map data references to metafuture children while continuing the traversal down the graph. After the traversal, if the root metafuture contains an answer, the answer to the query is returned.