scholarly journals PARALLEL MINING OF LARGE MAXIMAL BICLIQUES USING ORDER PRESERVING GENERATORS

2014 ◽  
pp. 105-113 ◽  
Author(s):  
R. V. Nataraj ◽  
S. Selvan
Keyword(s):  
Parallel Algorithm ◽  
Optimization Techniques ◽  
Main Memory ◽  
Memory Consumption ◽  
Running Time ◽  
Mining Algorithm ◽  
Parallel Mining ◽  
Mining Algorithms ◽  
Symmetric Property ◽  
Memory Efficient

In this paper, we propose a parallel algorithm for mining large maximal bicliques from graph datasets. We propose POP-MBC (Parallel Order Preserving Maximal BiClique mining algorithm), a fast and memory efficient parallel algorithm, which enumerates all the maximal bicliques independently and concurrently across several processors without any synchronization between the processors. The POP-MBC algorithm is highly memory efficient since it does not store the previously computed patterns in the main memory and requires only the dataset to be stored in the memory. To enhance the load sharing among different nodes, POP-MBC uses a round robin strategy which enables to achieve load balancing as high as 90%. We have also incorporated bit-vectors and numerous optimization techniques exploiting the symmetric property of the graph dataset to reduce the memory consumption and overall running time of the algorithm. Our comp rehensive experimental analyses involving publicly available datasets show that our algorithm distributes the load among the different processors equally and takes less memory, less running time than other maximal biclique mining algorithms.

scholarly journals Effective Application of Improved Profit-Mining Algorithm for the Interday Trading Model

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yu-Lung Hsieh ◽  
Don-Lin Yang ◽  
Jungpin Wu
Keyword(s):  
Financial Markets ◽  
Real World ◽  
Traditional Approach ◽  
Rule Mining ◽  
Mining Algorithm ◽  
Large Databases ◽  
Real World Applications ◽  
Trading Model ◽  
Mining Algorithms ◽  
Infant Stage

Many real world applications of association rule mining from large databases help users make better decisions. However, they do not work well in financial markets at this time. In addition to a high profit, an investor also looks for a low risk trading with a better rate of winning. The traditional approach of using minimum confidence and support thresholds needs to be changed. Based on an interday model of trading, we proposed effective profit-mining algorithms which provide investors with profit rules including information about profit, risk, and winning rate. Since profit-mining in the financial market is still in its infant stage, it is important to detail the inner working of mining algorithms and illustrate the best way to apply them. In this paper we go into details of our improved profit-mining algorithm and showcase effective applications with experiments using real world trading data. The results show that our approach is practical and effective with good performance for various datasets.

scholarly journals Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD

2021 ◽  
Vol 11 (18) ◽  
pp. 8476
Author(s):  
June Choi ◽  
Jaehyun Lee ◽  
Jik-Soo Kim ◽  
Jaehwan Lee
Keyword(s):  
Memory Management ◽  
Computing System ◽  
Optimization Techniques ◽  
Main Memory ◽  
Apache Spark ◽  
Computing Platform ◽  
Intermediate Data ◽  
Management Capability ◽  
Overall Performance ◽  
Optimization Methodology

In this paper, we present several optimization strategies that can improve the overall performance of the distributed in-memory computing system, “Apache Spark”. Despite its distributed memory management capability for iterative jobs and intermediate data, Spark has a significant performance degradation problem when the available amount of main memory (DRAM, typically used for data caching) is limited. To address this problem, we leverage an SSD (solid-state drive) to supplement the lack of main memory bandwidth. Specifically, we present an effective optimization methodology for Apache Spark by collectively investigating the effects of changing the capacity fraction ratios of the shuffle and storage spaces in the “Spark JVM Heap Configuration” and applying different “RDD Caching Policies” (e.g., SSD-backed memory caching). Our extensive experimental results show that by utilizing the proposed optimization techniques, we can improve the overall performance by up to 42%.

An Efficient Compression Technique for Vertical Mining Methods

2007 ◽  
pp. 143-173
Author(s):  
Mafruz Ashrafi ◽  
David Taniar ◽  
Kate Smith
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Main Memory ◽  
Rule Mining ◽  
Compression Technique ◽  
Efficient Manner ◽  
Handling Technique ◽  
Mining Methods ◽  
Mining Algorithms

Association rule mining is one of the most widely used data mining techniques. To achieve a better performance, many efficient algorithms have been proposed. Despite these efforts, many of these algorithms require a large amount of main memory to enumerate all frequent itemsets, especially when the dataset is large or the user-specified support is low. Thus, it becomes apparent that we need to have an efficient main memory handling technique, which allows association rule mining algorithms to handle larger datasets in the main memory. To achieve this goal, in this chapter we propose an algorithm for vertical association rule mining that compresses a vertical dataset in an efficient manner, using bit vectors. Our performance evaluations show that the compression ratio attained by our proposed technique is better than those of the other well-known techniques.

scholarly journals Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Dingjun Chen ◽  
Sihan Li ◽  
Junjie Li ◽  
Shaoquan Ni ◽  
Xiaolong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Travel Time ◽  
High Speed ◽  
Optimization Techniques ◽  
High Speed Rail ◽  
High Speed Railway ◽  
Running Time ◽  
Total Travel Time ◽  
Timetable Optimization

Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes.

scholarly journals An Efficient Parallel Mining Algorithm Representative Pattern Set of Large-Scale Itemsets in IoT

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 79162-79173 ◽  
Author(s):  
Zhang Tianrui ◽  
Wei Mingqi ◽  
Liu Bin
Keyword(s):  
Large Scale ◽  
Mining Algorithm ◽  
Parallel Mining

Research on the Web Mining Algorithm Application in Tourism E-Commerce

2013 ◽  
Vol 380-384 ◽  
pp. 1133-1136
Author(s):  
Xue Song Zhao ◽  
Kai Fan Ji
Keyword(s):  
Association Rules ◽  
Clustering Analysis ◽  
Web Mining ◽  
Web Design ◽  
Developed Countries ◽  
Online Sales ◽  
Mining Algorithm ◽  
Personalized Services ◽  
Mining Algorithms ◽  
The Web

Web mining algorithms are widely used in e-commerce. Tourism e-commerce develops fast in recent years in China but the application of web mining algorithms stays in low level compared with some developed countries. This paper first discusses two major web mining algorithms: the Association Rules algorithm and Clustering Analysis, and then analyzes the application of web mining algorithm in tourism e-commerce. It concludes that web mining algorithms can help tourism e-commerce to improve web design, increase online sales and provide better personalized services for web users.

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