scholarly journals The MapReduce Model on Cascading Platform for Frequent Itemset Mining

2018 ◽  
Vol 12 (2) ◽  
pp. 149
Author(s):  
Nur Rokhman ◽  
Amelia Nursanti
Keyword(s):  
Large Scale ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Programming Models ◽  
Distributed Programming ◽  
Itemset Mining ◽  
Large Scale Data ◽  
Mapreduce Model ◽  
Large Scale Data Processing ◽  
Scale Data

The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data. This paper discusses the implementation of MapReduce model on Cascading for FIM. The experiment uses the Amazon dataset product co-purchasing network metadata.The experiment shows the fact that the simple mechanism of Cascading can be used to solve FIM problem. It gives time complexity O(n), more efficient than the nonparallel which has complexity O(n2/m).

scholarly journals Efficient Large Scale Frequent Itemset Mining with Hybrid Partitioning Approach

2019 ◽  
pp. 845-852
Author(s):  
Priyanka R. ◽  
Mohammed Ibrahim M. ◽  
Ranjith Kumar M.
Keyword(s):  
Large Scale ◽  
Customer Segmentation ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Frequent Patterns ◽  
Itemset Mining ◽  
Large Scale Data ◽  
Player Tracking ◽  
Frequent Items ◽  
Scale Data

In today’s world, voluminous data are available which are generated from various sources in various forms. Mining or analyzing this large scale data in an efficient way so as to make them useful for the mankind is difficult with the existing approaches. Frequent itemset mining is one such technique used for analyzing in many fields like finance, health care system where the main focus is gathering frequent patterns and grouping them to be meaningful inorder to gather useful insights from the data. Some major applications include customer segmentation in marketing, shopping cart analyses, management relationship, web usage mining, player tracking and so on. Many parallel algorithms, like Dist-Eclat Algorithm, Big FIM algorithm are available to perform large scale Frequent itemset mining. In Dist-Eclat algorithm, datasets are partitioned using Round Robin technique which uses a hybrid partitioning approach, which can improve the overall efficiency of the system. The system works as follows: Initially the data collected are distributed by mapreduce. Then the local frequent k-itmesets are computed using FP-Tree and sent to the map phase. Later the mining results are combined to the center node. Finally, global frequent itemsets are gathered by mapreduce. The proposed system is expected to improve in efficiency by using hybrid partitioning approach in the datasets based on the identification of frequent items.

Frequent Itemset Mining in Large Datasets a Survey

2017 ◽  
Vol 7 (4) ◽  
pp. 37-49
Author(s):  
Amrit Pal ◽  
Manish Kumar
Keyword(s):  
Large Scale ◽  
Parallel Implementation ◽  
Complete Information ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Data Parallel ◽  
Itemset Mining ◽  
Large Scale Data ◽  
Day By Day ◽  
Scale Data

Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules. The amount of data is increasing day by day taking form of BigData, which require changes in the algorithms for working on such large-scale data. Parallel implementation of the mining techniques can provide solutions to this problem. In this paper a survey of frequent itemset mining techniques is done which can be used in a parallel environment. Programming models like Map Reduce provides efficient architecture for working with BigData, paper also provides information about issues and feasibility about technique to be implemented in such environment.

scholarly journals GMiner: A fast GPU-based frequent itemset mining method for large-scale data

2018 ◽  
Vol 439-440 ◽  
pp. 19-38 ◽  
Author(s):  
Kang-Wook Chon ◽  
Sang-Hyun Hwang ◽  
Min-Soo Kim
Keyword(s):  
Large Scale ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Mining Method ◽  
Itemset Mining ◽  
Large Scale Data ◽  
Scale Data

Advanced monitoring techniques for a large‐scale data‐processing network

2008 ◽  
Vol 25 (5) ◽  
pp. 287-300 ◽  
Author(s):  
B. Martin ◽  
A. Al‐Shabibi ◽  
S.M. Batraneanu ◽  
Ciobotaru ◽  
G.L. Darlea ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Monitoring Techniques ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Processing Network ◽  
Scale Data

BestPeer++: A Peer-to-Peer Based Large-Scale Data Processing Platform

2014 ◽  
Vol 26 (6) ◽  
pp. 1316-1331 ◽  
Author(s):  
Gang Chen ◽  
Tianlei Hu ◽  
Dawei Jiang ◽  
Peng Lu ◽  
Kian-Lee Tan ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Peer To Peer ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Processing Platform ◽  
Scale Data

scholarly journals Medimate : Ailment Diffusion Control System with Real Time Large Scale Data Processing

2018 ◽  
Vol 7 (2.31) ◽  
pp. 240
Author(s):  
S Sujeetha ◽  
Veneesa Ja ◽  
K Vinitha ◽  
R Suvedha
Keyword(s):  
Control System ◽  
Data Processing ◽  
Real Time ◽  
Large Scale ◽  
Diffusion Control ◽  
Qr Code ◽  
Healthcare Applications ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
Scale Data

In the existing scenario, a patient has to go to the hospital to take necessary tests, consult a doctor and buy prescribed medicines or use specified healthcare applications. Hence time is wasted at hospitals and in medical shops. In the case of healthcare applications, face to face interaction with the doctor is not available. The downside of the existing scenario can be improved by the Medimate: Ailment diffusion control system with real time large scale data processing. The purpose of medimate is to establish a Tele Conference Medical System that can be used in remote areas. The medimate is configured for better diagnosis and medical treatment for the rural people. The system is installed with Heart Beat Sensor, Temperature Sensor, Ultrasonic Sensor and Load Cell to monitor the patient’s health parameters. The voice instructions are updated for easier access.  The application for enabling video and voice communication with the doctor through Camera and Headphone is installed at both the ends. The doctor examines the patient and prescribes themedicines. The medical dispenser delivers medicine to the patient as per the prescription. The QR code will be generated for each prescription by medimate and that QR code can be used forthe repeated medical conditions in the future. Medical details are updated in the server periodically.  

Integration of large-scale data processing systems and traditional parallel database technology

2019 ◽  
Vol 12 (12) ◽  
pp. 2290-2299
Author(s):  
Azza Abouzied ◽  
Daniel J. Abadi ◽  
Kamil Bajda-Pawlikowski ◽  
Avi Silberschatz
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Parallel Database ◽  
Large Scale Data ◽  
Database Technology ◽  
Large Scale Data Processing ◽  
Scale Data
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