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Recent data stream frequent item set mining method based on CPU+MIC (Central Processing Unit+ Many Integrated Core) cooperative computing

A technology of frequent itemset mining and frequent itemsets, applied in computing, energy-saving computing, electrical digital data processing and other directions, to achieve the effect of simple and convenient operation, high mining efficiency and reasonable design

Inactive Publication Date: 2016-07-06
LANGCHAO ELECTRONIC INFORMATION IND CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] However, there is currently no technology that can quickly and efficiently obtain frequent itemsets of streaming data to improve user experience

Method used

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  • Recent data stream frequent item set mining method based on CPU+MIC (Central Processing Unit+ Many Integrated Core) cooperative computing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] The recent data flow frequent itemset mining method based on CPU+MIC collaborative computing is realized by CPU and MIC many-core coprocessor;

[0031] The CPU side is responsible for scanning the recent current data stream by using the sliding window technology, and then divides the data stream in the current window into blocks, and transmits the sub-window data stream to the MIC card, and is responsible for the framework construction of the CPU+MIC collaborative computing mode, task scheduling and parameter initialization. , and in the calculation task CPU of the whole data mining, it will also use the openmp multi-threading mode to mine data sequentially through the genetic algorithm;

[0032] The MIC many-core coprocessor is responsible for multi-threaded parallel use of parallel genetic algorithms to find frequent itemsets in each nested data sub-window; the MIC card also uses openmp multi-threaded computing;

[0033] This method is based on CPU+MIC collaborative c...

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Abstract

The invention discloses a recent data stream frequent item set mining method based on CPU+MIC (Central Processing Unit+ Many Integrated Core) cooperative computing. The method is implemented through a CPU and an MIC coprocessor. The method is based on the CPU+MIC cooperative computing. The method comprises the following steps: setting an initial population, wherein individuals in the population are a series of frequent item sets to be selected; implementing a searching process based on crossover, variation and selection operations of a genetic algorithm; and traversing multiple generations of processing to obtain final frequent item sets. Compared with the prior art, the recent data stream frequent item set mining method based on the CPU+MIC cooperative computing has the advantages of reasonable design, easiness and convenience in operation, high mining efficiency and the like. According to the method, data streams in a nested window are processed through the genetic algorithm based on the CPU+MIC cooperative computing, so that a frequent mode in the data streams in the nested window can be rapidly and accurately mined. The method can be applied to decision making in industries of water conservation runoff analysis, power load, financial information and the like.

Description

technical field [0001] The invention relates to the technical field of realizing frequent itemsets mining of recent data streams, in particular to a method for mining frequent itemsets of recent data streams based on CPU+MIC collaborative calculation. Background technique [0002] A data stream is actually a continuously moving procession of elements consisting of collections of related data. Let t represent any time stamp, and at represent the data arriving at that time stamp. Stream data can be expressed as {..., at?1, at, at+1,...}. Different from the traditional application model, the stream data model has the following 4 points in common: (1) The data arrives in real time; (2) The order of data arrival is independent and not controlled by the application system; (3) The data scale is huge and its maximum value cannot be predicted; (4) Once the data is processed, unless it is specially saved, Otherwise, it cannot be retrieved and processed again, or it is expensive to r...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/24568G06F16/2465Y02D10/00
Inventor 龚湛张清
Owner LANGCHAO ELECTRONIC INFORMATION IND CO LTD
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