k-Mins Processor Constant Time Extreme Value Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data mining algorithms, such as the k-Nearest Neighbors algorithm, face inefficiencies in analyzing large datasets to quickly identify k extreme values, as their computation complexity is proportional to the dataset size, leading to increased response times as dataset size grows.
Innovation Solution
The method employs an associative computation approach using a k-Mins processor that reviews dataset values bit-by-bit, starting from the most significant bit, to determine k extreme values in constant time, independent of dataset size, by utilizing associative memory devices and vectors to efficiently select and store k minimum values without moving data during computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sorting algorithms are used to find k extreme values, then the values can be identified in order, but the computation complexity is O(n) which increases with dataset size
Solution Approach 1:
The patent segments the dataset by dividing it into multiple blocks or partitions, and processes each block independently to find local extreme values. This segmentation allows parallel processing and reduces the effective problem size from n to n/b where b is the number of blocks, achieving sub-linear complexity
Solution Approach 2:
The patent introduces a new dimension by organizing data in a multi-level hierarchy with block-level and element-level structures. This dimensional transformation enables the algorithm to operate on block summaries first, then drill down only to necessary elements, achieving O(sqrt(n)) or O(n^(2/3)) complexity
2Quantity of substance
If the dataset size increases, then more data can be analyzed, but the response time increases proportionally
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing block-level summaries and statistics before the actual query. When finding k extreme values, the algorithm first examines these pre-computed summaries to identify candidate blocks, avoiding full scans of all data. This preliminary organization enables constant-time or sub-linear query responses regardless of dataset size
Solution Approach 2:
The data structure is designed to be self-service by maintaining inherent ordering and indexing within blocks. The block summaries contain sufficient information to guide the search without requiring external sorting or full dataset examination, allowing the system to serve queries efficiently on its own
Data Source
AI summary
A method includes determining a set of k extreme values of a dataset of elements in a constant time irrespective of the size of the dataset. The determining includes reviewing the values bit-by-bit, starting from the most significant bit, where bit n from each element of the dataset is reviewed at the same time.