A Cosmological Imperative: Emergence of Information, Complexity, and Life

2001 ◽  
pp. 15-17 ◽  
Author(s):  
Eric J. Chaisson
Keyword(s):  
Information Complexity

Optimization Method of Massive Data Query

2014 ◽  
Vol 602-605 ◽  
pp. 3247-3250
Author(s):  
Yu Ming Chen
Keyword(s):  
Optimization Method ◽  
Experimental Results ◽  
Massive Data ◽  
Information Complexity ◽  
Apriori Algorithm ◽  
Amount Of Information ◽  
Data Query ◽  
Target Data

Optimization method ofmassive dataquery is researched in this paper.In the modernInternet environment,the datahas the characteristics oflarge amount of information, complexity, disorder, andchaosassociation. Using traditionalqueried methodsoftenrequirea lot oflimitedconditions, witha lot of drawbacks such as time-consuming data query, moreineffective queryand low efficiency.To this end, anoptimizationmethod of massive data query based onparallel Apriori algorithm is proposed in this paper.The massive dataare made simplification processing andredundant data are deleted to providedata foundation for fast and accuratedataquery.Effectiveassociation rulesof the massive data are calculated, in order to obtain the relevantof the target data. Based onAprioriparallel algorithm,massivedata are processedto achieveaccurate query. Experimental results show thatthe use ofoptimization algorithm for massive dataquerycan improvethe query speedof target data and it has a strong superiority.

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