scholarly journals Refactoring Java Code to MapReduce Framework (S)

2021 ◽  
Author(s):  
Junfeng Zhao
Keyword(s):  
Mapreduce Framework ◽  
Java Code

Selenium Broken Links Automation Testing

2017 ◽  
Vol 7 (7) ◽  
pp. 53
Author(s):  
Gursimran Singh ◽  
Harpreet Kaur
Keyword(s):  
Web Page ◽  
Automate Testing ◽  
Website Content ◽  
Java Code ◽  
Automation Testing

With the growth of website content it is become difficult to manage relations between Individual webpage and keep track of their hyperlinks within a website. This causes some Hyperlink become dead or broken. A broken Link  is a  link on a web page that no longer works. It is difficult to find out the broken link manually by checking each hyperlink individually because it is time consuming and tedious work. So to eliminate this we can use the selenium web driver tool and java code to automate testing of each hyperlink individually. The objective of this thesis is to automate finding of broken links using selenium web driver tool.

Algorithm of Text Categorization Based on Cloud Computing

2013 ◽  
Vol 311 ◽  
pp. 158-163 ◽  
Author(s):  
Li Qin Huang ◽  
Li Qun Lin ◽  
Yan Huang Liu
Keyword(s):  
Cloud Computing ◽  
Text Categorization ◽  
Experimental Results ◽  
Support Vector ◽  
Computing Environment ◽  
Mapreduce Framework ◽  
Cloud Computing Environment ◽  
Environment Map ◽  
Vector Machines ◽  
Parallel Text

MapReduce framework of cloud computing has an effective way to achieve massive text categorization. In this paper a distributed parallel text training algorithm in cloud computing environment based on multi-class Support Vector Machines(SVM) is designed. In cloud computing environment Map tasks realize distributing various types of samples and Reduce tasks realize the specific SVM training. Experimental results show that the execution time of text training decreases with the number of Reduce tasks increasing. Also a parallel text classifying based on cloud computing is designed and implemented, which classify the unknown type texts. Experimental results show that the speed of text classifying increases with the number of Map tasks increasing.

Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation

2021 ◽  
pp. 016555152110137
Author(s):  
N.R. Gladiss Merlin ◽  
Vigilson Prem. M
Keyword(s):  
Big Data ◽  
Similarity Measure ◽  
Patient Information ◽  
Complex Data ◽  
Mapreduce Framework ◽  
Maximum Value ◽  
User Query ◽  
Indexing And Retrieval ◽  
Sine Cosine Algorithm ◽  
Disparate Source

Large and complex data becomes a valuable resource in biomedical discovery, which is highly facilitated to increase the scientific resources for retrieving the helpful information. However, indexing and retrieving the patient information from the disparate source of big data is challenging in biomedical research. Indexing and retrieving the patient information from big data is performed using the MapReduce framework. In this research, the indexing and retrieval of information are performed using the proposed Jaya-Sine Cosine Algorithm (Jaya–SCA)-based MapReduce framework. Initially, the input big data is forwarded to the mapper randomly. The average of each mapper data is calculated, and these data are forwarded to the reducer, where the representative data are stored. For each user query, the input query is matched with the reducer, and thereby, it switches over to the mapper for retrieving the matched best result. The bilevel matching is performed while retrieving the data from the mapper based on the distance between the query. The similarity measure is computed based on the parametric-enabled similarity measure (PESM), cosine similarity and the proposed Jaya–SCA, which is the integration of the Jaya algorithm and the SCA. Moreover, the proposed Jaya–SCA algorithm attained the maximum value of F-measure, recall and precision of 0.5323, 0.4400 and 0.6867, respectively, using the StatLog Heart Disease dataset.

A personalized IPTV channel-recommendation mechanism based on the MapReduce framework

2014 ◽  
Vol 69 (1) ◽  
pp. 225-247 ◽  
Author(s):  
Hong-Yi Chang ◽  
Shih-Chang Huang ◽  
Chih-Chun Lai
Keyword(s):  
Mapreduce Framework
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