Static and adaptive distributed data replication using genetic algorithms

2004 ◽  
Vol 64 (11) ◽  
pp. 1270-1285 ◽  
Author(s):  
Thanasis Loukopoulos ◽  
Ishfaq Ahmad
Keyword(s):  
Genetic Algorithms ◽  
Data Replication ◽  
Distributed Data

scholarly journals The BioRSTM Integration and Retrieval System: An open system for distributed data integration

2006 ◽  
Vol 3 (2) ◽  
pp. 274-281 ◽  
Author(s):  
Andreas Kaps ◽  
Konstantin Dyshlevoi ◽  
Klaus Heumann ◽  
Ralf Jost ◽  
Ioannis Kontodinas ◽  
...  
Keyword(s):  
Data Integration ◽  
Open System ◽  
Data Exchange ◽  
Retrieval System ◽  
Data Replication ◽  
Global Network ◽  
Distributed Data ◽  
Local Data ◽  
Distributed Information ◽  
Retrieval Software

Summary Modern academic and industrial research in life sciences generates huge amounts of data and information. To extract knowledge from this information space, optimized integration and retrieval software tools are essential. In the last years, a number of academic as well as commercial systems have been developed to solve this problem. However, as scientific projects are distributed at different locations (e.g., subsidiaries of companies, academic partnerships), data exchange and availability must be realized in a way that avoids data replication.In this article, we describe a global solution for integrating distributed information by applying the BioRSTM Integration and Retrieval System and its inter-BioRS communication capability that goes beyond the standard issue of local data integration. Each site integrates and maintains locally generated data using a local copy of the BioRS software. Applying the inter-BioRS communication, all available BioRS instances can communicate with each other realizing a global network of integrated databanks. All databanks integrated in this network can be accessed from any site without any data replication. This open system allows the addition of new information and sites dynamically. However, access privileges for certain databanks can be maintained on a per user and databank level ensuring data security when required.

scholarly journals EDDA: An Efficient Distributed Data Replication Algorithm in VANETs

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 547 ◽  
Author(s):  
Junyu Zhu ◽  
Chuanhe Huang ◽  
Xiying Fan ◽  
Sipei Guo ◽  
Bin Fu
Keyword(s):  
Data Replication ◽  
Distributed Data

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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