HDFS framework for efficient frequent itemset mining using MapReduce

Kulkarni, Prajakta; Khonde, Shraddha R.

doi:10.1109/icisim.2017.8122169

Cited by 2 publications

(1 citation statement)

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“…it stores the files to different nodes and then accessibility of the data becomes fast as the data are accessed from the nodes and there are various components that look after different tasks assigned to them. [20] The components of HDFS were name-node, data-node, task-tracker and job-tracker but with the entry of YARN the things are slightly changed and now there is a new scenario. Previously the hadoop consists of two major components namely HDFS and mapreduce.…”

Section: Fig 2b Content Based Filtering Hadoop Distributed File Sysmentioning

confidence: 99%

Optimized Recommendation System for E Commerce on Product Features and User Behavior

G.¹,

Singh²

2019

IJRTE

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Big data is a late of huge information stored in it and all we need is to dig into get the important information out of it and create a useful system which can be very helpful in improving the current scenario. There are various applications where big data is being used and even there are few fields that are learning techniques to go with big data and evaluate their work and get an improve decision. This paper particularly concentrates on the e commerce system which is highly trending on the market field. [20] E commerce also known as electronic commerce is a market place which gives you a platform to enjoy various services from both buyers as well as sellers. It is a place with various varieties are provided that can help the consumer to choose from and the buyer can get a platform where he can show case his product and get millions of the customer at the same time and he does not have to look for site all the time, it’s the system that take care of it. Now big data is playing a vital role in e commerce as it reads about user behavior and provides him a suitable product that he may need according to his behavior and query. There are various machine learning algorithms that are working on this and improving the services. [11] Basically in this paper we will read the user information and combine it with the product attributes and get a suitable suggestion for the user that will be most likely to be purchased by him. In the existing system we just look at one part of the case and give suggestion but in this paper we looked at both the sides, that is we looked after the product entities (the attributes and features that it poses) and the user behavior (the information given by the user and its previous history) that will better prediction and improve the system. Moreover for the optimized working of the system we included an enhanced version of HPCA scheduling algorithm for the Hadoop distributed file system also known as HDFS, which is very suitable for the heterogeneous system, the existing algorithm looks after the overall capacity of the node and then the tasks were assigned but here we will consider the health and the left over capacity of the nodes and arrange the queue for the same which will be refreshed all the time after the task is completed by any node.[18] The aim of the paper is to provide fast and most suitable suggestions to the users which can play a vital role in improving the sales of the company and getting the target done soon and faster

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Section: Fig 2b Content Based Filtering Hadoop Distributed File Sysmentioning

confidence: 99%