Feature-Bucketed Data Structure for Computer-Based Reasoning Search
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Solution Overview
Problem
Computer-based reasoning systems face significant computational challenges in searching and querying, particularly with complex searches like similarity searches, Lebesgue space searches, and probability searches, as the computation required increases dramatically with the amount of underlying data, leading to inefficiencies in processing time.
Innovation Solution
The implementation of a feature-bucketed data structure, such as a unique separable box filter tree, using ordered hashes and strictly-oriented linear hashes to index and query multidimensional data, allowing for constant or logarithmic time complexity in search operations by creating ordered hashes of feature values and organizing them into feature buckets, enabling efficient retrieval of nearest neighbors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional searching methods are used in computer-based reasoning systems, then the system can handle basic search operations, but the computation required increases dramatically with the amount of underlying data
Solution Approach 1:
The patent segments the multidimensional search space into discrete feature buckets based on hash values. Each feature dimension is divided into multiple buckets, and data points are distributed across these buckets based on their feature values. This segmentation allows the system to handle large amounts of data by organizing it into manageable segments that can be searched independently, thereby maintaining search efficiency even as the quantity of underlying data increases.
Solution Approach 2:
The patent transforms the traditional multidimensional search problem into a one-dimensional hash space. By applying hash functions to feature values and organizing data points based on their hash bucket assignments across multiple dimensions, the system converts complex multidimensional range queries into simpler bucket-based lookups. This dimensionality transformation enables efficient searching by mapping high-dimensional data into a structured bucket hierarchy that can be traversed more effectively.
2Adaptability or versatility
If complex searches like similarity searches and Lebesgue space searches are implemented, then the system provides more sophisticated querying capabilities, but the computation required increases dramatically
Solution Approach 1:
The patent performs preliminary actions by pre-computing hash values for all feature dimensions and organizing data points into feature buckets before actual search operations. This pre-organization creates an indexed structure where similarity searches and Lebesgue space searches can be executed more efficiently. By preparing the data in advance with pre-computed hashes and bucket assignments, the system reduces the computational burden during actual search operations, enabling sophisticated querying capabilities without proportional increases in computation required.
Solution Approach 2:
The patent introduces feature buckets as an intermediary structure between the raw multidimensional data and the search queries. These buckets serve as a mediating layer that simplifies complex search operations by pre-grouping data points based on their feature hash values. When performing similarity searches or Lebesgue space searches, the system can efficiently navigate through these intermediary buckets rather than directly processing all raw data, thereby providing sophisticated querying capabilities while managing computation required.
3Quantity of substance
If the amount of underlying data increases, then the system has more data to analyze for better predictions, but the computation required for searching increases dramatically
Solution Approach 1:
The patent segments training data into feature buckets based on hash values of feature dimensions. This segmentation allows the system to store and retrieve large amounts of training data efficiently by organizing it into distributed buckets across the data structure. When performing searches, the system only needs to examine relevant buckets rather than scanning all training data, thereby maintaining fast processing time even as the amount of training data increases to improve prediction accuracy.
Data Source
AI summary
Techniques for improved searching and querying in computer-based reasoning systems are discussed and include receiving multiple new multidimensional data element to store in a computer-based reasoning data model; determining a feature bucket for each feature of each data element and storing a reference identifier in the feature bucket(s). A query on the computer-based reasoning system includes input data element (e.g., an actual data element, or a set of restrictions on features). For each feature in the input data element, feature buckets are determined, candidate results are determined based on whether cases have related feature buckets, and the results are determined based at least in part on the candidate results. In some embodiments, control of controllable systems may be caused based on the results.