Probabilistic Tree Database Querying Without Sorting
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Solution Overview
Problem
Existing space-partitioning methods for databases fail to scale with increasing dimensions, require time-consuming sorting, and cannot handle missing data or extrapolate beyond/interpolate between rows, making them inefficient for large databases and complex queries.
Innovation Solution
Implementing a learning model using hierarchical probabilistic decomposition, where new parameters are determined for child nodes based on current parameters and associated rows, allowing for efficient query answering by building a probabilistic tree that can handle missing values and scale with the number of dimensions and rows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If space-partitioning methods are used to enable faster query searching, then query speed is improved, but the system cannot scale as the number of dimensions grows because the number of partitions per node becomes 2^n where n is the number of dimensions
Solution Approach 1:
The patent changes the fundamental parameter of partitioning from exponential (2^n partitions for n dimensions) to linear or logarithmic scaling by using a different data organization approach that doesn't require creating a partition for every possible dimension combination, thus resolving the scalability issue while maintaining query speed
Solution Approach 2:
The patent introduces a new dimensional approach to organizing data by using a hierarchical or projection-based structure that transforms the high-dimensional space into a more manageable form, allowing efficient querying without exponential growth in partition complexity
2Productivity
If space-partitioning methods are used to organize database for faster searching, then query efficiency is improved, but sorting the database becomes time-consuming with large databases
Solution Approach 1:
The patent performs preliminary organization of data during the indexing phase rather than requiring sorting at query time, so that when queries are executed, the data is already in an optimal structure for fast retrieval without needing time-consuming sorting operations
Solution Approach 2:
The patent replaces the mechanical sorting process with a more efficient data organization mechanism that uses hashing, indexing, or projection techniques to arrange data in a query-friendly structure without the overhead of traditional sorting algorithms
3Speed
If space-partitioning methods are used to structure the database, then query searching is faster, but the system cannot handle missing data nor can it extrapolate beyond or interpolate between rows in the database
Solution Approach 1:
The patent creates a data structure that serves multiple functions simultaneously: it enables fast querying through efficient organization, handles missing data through appropriate representations, and supports extrapolation/interpolation through the preservation of dimensional relationships, thus resolving the contradiction between speed and versatility
Data Source
AI summary
A method and apparatus for implementing a learning model for facilitating answering a query on a database. Example embodiments include processing logic to: receive access to a database having a plurality of rows; determine for each row in the plurality of rows which of a plurality of child nodes to associate with a row based on the row and current parameters, where the current parameters correspond to parameters of a probability distribution for the plurality of child nodes; determine new parameters for the plurality of child nodes based on associated rows of the plurality of rows; determine whether or not to return a result which indicates the new parameters based on the current parameters, the new parameters, and the plurality of rows; and return a result which indicates the new parameters.