Data Cleansing Processing using Pentaho Data Integration: Case Study Data Deduplication

Setyawan, Dwi Cahya; Kusumasari, Tien Fabrianti; Alam, Ekky Novriza

doi:10.1109/icst50505.2020.9732824

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…The authors have also given an idea about the tools used for Big Data storage and processing along with the algorithms used in the Big Data processing. Hadoop, HDFS [3], CDH [4], Mon-goDB [5], Apache spark [6], Apache Solr [7], [8],Alteryx [9] Designer, Data Meer [9], Google Big Query [9] etc are the tools used to store process and analyze the Big Data and Support Vector Machine [10], Neural Network, Logistic regression [11], Linear Regression [11], Nearest Neighbor, Decision tree, Naive Bayes are the Algorithms used in healthcare analysis.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Big Data Tools for Remote Healthcare Services

Ray,

Mainuddin,

Singha

et al. 2023

Preprint

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Health Data storage and analytics are the two important parts of the health or drugs business and health research. Health Data storage and management are important for storing and retrieving Health Data efficiently. The scalable and flexible infrastructure capable of efficient storing and processing the millions of Health Data generated every day must be very much needed for the industry. Big Data tools and NoSQL databases are becoming the necessary tools to store millions of data and respond to given queries efficiently. In general relational databases are used for many applications, but they are not at all efficient for storing and processing Big Data. The drawbacks of relational databases motivate the developers to use NoSQL databases and Big Data tools while creating the Health application. A comparative study must be made to find out the performance of different Big Data platforms along with the NoSQL Databases to store Health Data for a Health-related application and to respond to queries efficiently. In this paper, we have tested the performance of Hadoop, HIVE, Cassandra, MongoDB, HBASE and NGMR to respond to different queries to store Health Data for a remote health framework. Such a study helps us to come up with new ideas that can help us to store and retrieve the Health Data of a particular application using a data model that is iteratively tested and modified. It provides us with an understanding of the Big Data solution or tool as well. The key aim of the study is to make a review on the capability of data storage and the read/write performance of the tools

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