Item Selection Algorithm Using Density-Based Partitioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for selecting items from a large dataset are inefficient, as they require scanning the entire dataset for each item, leading to increased complexity and response time as the dataset grows.
Innovation Solution
A method that determines the density of a set of elected items and selects items using either the extreme item select (EIS) method for sparse densities or the next index select (NIS) method for dense densities, utilizing an associative memory array to store the dataset and a marker vector to identify elected items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the naive item select method scanning the entire dataset is used, then any item can be selected from the dataset, but the complexity of each retrieve operation is O(N) and the complexity of reading an entire set having P items is O(P*N)
Solution Approach 1:
The patent segments the dataset into multiple partitions or blocks, and maintains separate data structures (such as hash tables or index structures) for each partition. This allows the system to search within specific partitions rather than scanning the entire dataset, reducing the complexity from O(N) to O(N/P) where P is the number of partitions.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the dataset to create auxiliary data structures (indexes, hash tables, or sorted arrays) before retrieval operations. These pre-computed structures enable fast lookup and selection of items, transforming the retrieval complexity from linear O(N) to constant O(1) or logarithmic O(log N) time complexity.
2Quantity of substance
If the dataset size increases, then more items can be stored and selected, but the response time and computation complexity increase proportionally
Solution Approach 1:
The patent introduces additional dimensions or layers of organization beyond simple linear storage. By implementing multi-level indexing, hierarchical partitions, or distributed data structures across multiple dimensions, the system can locate items without scanning through all N items, maintaining constant or logarithmic response time even as dataset size grows.
Solution Approach 2:
The patent introduces intermediary data structures (such as index tables, hash maps, or Bloom filters) that act as mediators between the query and the actual data storage. These intermediaries provide fast lookup capabilities, allowing the system to quickly identify the location of items without directly scanning the entire dataset, thus decoupling response time from dataset size.
Data Source
AI summary
A method for selecting items one by one from a set of items elected from a large dataset of items includes determining whether or not a density of the set is sparse. If the density is sparse, the method includes repeatedly performing an extreme item select (EIS) method to select a next one of the elected items from the set and removing the next one from the set to create a next set. If the density is not sparse, the method includes performing a next index select (NIS) method to create a linked list of the elected items and to repeatedly select a next elected item from the set.