scholarly journals Routing Optimization Algorithms Based on Node Compression in Big Data Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Lifeng Yang ◽  
Liangming Chen ◽  
Ningwei Wang ◽  
Zhifang Liao
Keyword(s):  
Big Data ◽  
Shortest Path ◽  
Optimization Algorithms ◽  
Real Life ◽  
Large Data ◽  
Path Selection ◽  
Shortest Path Problem ◽  
Limited Time ◽  
Starting Point ◽  
Data Environment

Shortest path problem has been a classic issue. Even more so difficulties remain involving large data environment. Current research on shortest path problem mainly focuses on seeking the shortest path from a starting point to the destination, with both vertices already given; but the researches of shortest path on a limited time and limited nodes passing through are few, yet such problem could not be more common in real life. In this paper we propose several time-dependent optimization algorithms for this problem. In regard to traditional backtracking and different node compression methods, we first propose an improved backtracking algorithm for one condition in big data environment and three types of optimization algorithms based on node compression involving large data, in order to realize the path selection from the starting point through a given set of nodes to reach the end within a limited time. Consequently, problems involving different data volume and complexity of network structure can be solved with the appropriate algorithm adopted.

scholarly journals Big Data Affluence in Statistics Application: A Comparison of Real Life and Simulated Open Data

2020 ◽  
Author(s):  
Nureni Adeboye ◽  
◽  
Oyedunsi Olayiwola ◽  
Keyword(s):  
Big Data ◽  
Real Life ◽  
Open Data ◽  
Large Data ◽  
Learning Ability ◽  
Sufficient Data ◽  
Data Repositories ◽  
Classroom Activities ◽  
Life Data ◽  
Real Life Data

Large data repositories or database management still remain a mirage and tough challenge to accomplish by most developing countries and establishments around the globe. This necessitates the need to improvise on the gathering of suitable data with a good spread to serve as a complement, in the absence of sufficient real-life data. Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. Through classroom activities that involved both sourced real-life and simulated big data in R-environment, models were built and estimates obtained from the adopted techniques revealed the robustness of simulated datasets in a unified observation with improved significant values as reflected in the results. Students appreciated the use of such big data as it enhances their machine learning ability and the availability of sufficient data without delay.

A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment

2020 ◽  
Vol 9 (2) ◽  
pp. 132-161 ◽  
Author(s):  
Ranjan Kumar ◽  
Sripati Jha ◽  
Ramayan Singh
Keyword(s):  
Linear Programming ◽  
Shortest Path ◽  
Path Length ◽  
Real Life ◽  
Shortest Path Problem ◽  
Fuzzy Linear Programming ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Functions ◽  
Fuzzy Environment

The authors present a new algorithm for solving the shortest path problem (SPP) in a mixed fuzzy environment. With this algorithm, the authors can solve the problems with different sets of fuzzy numbers e.g., normal, trapezoidal, triangular, and LR-flat fuzzy membership functions. Moreover, the authors can solve the fuzzy shortest path problem (FSPP) with two different membership functions such as normal and a fuzzy membership function under real-life situations. The transformation of the fuzzy linear programming (FLP) model into a crisp linear programming model by using a score function is also investigated. Furthermore, the shortcomings of some existing methods are discussed and compared with the algorithm. The objective of the proposed method is to find the fuzzy shortest path (FSP) for the given network; however, this is also capable of predicting the fuzzy shortest path length (FSPL) and crisp shortest path length (CSPL). Finally, some numerical experiments are given to show the effectiveness and robustness of the new model. Numerical results show that this method is superior to the existing methods.

Note on “Optimal path selection approach for fuzzy reliable shortest path problem”

2020 ◽  
Vol 39 (5) ◽  
pp. 7653-7656
Author(s):  
Ranjan Kumar ◽  
SA Edalatpanah ◽  
Hitesh Mohapatra
Keyword(s):  
Shortest Path ◽  
Fuzzy Systems ◽  
Optimal Path ◽  
Vital Role ◽  
Path Selection ◽  
Shortest Path Problem ◽  
Shortest Path Problems ◽  
Fuzzy Environment ◽  
Selection Approach ◽  
Arc Lengths

There are different conditions where SPP play a vital role. However, there are various conditions, where we have to face with uncertain parameters such as variation of cost, time and so on. So to remove this uncertainty, Yang et al. [1] “[Journal of Intelligent & Fuzzy Systems, 32(1), 197-205”] have proposed the fuzzy reliable shortest path problem under mixed fuzzy environment and claimed that it is better to use their proposed method as compared to the existing method i.e., “[Hassanzadeh et al.; A genetic algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths, Mathematical and Computer Modeling, 57(2013) 84-99” [2]]. The aim of this note is, to highlight the shortcoming that is carried out in Yang et al. [1] article. They have used some mathematical incorrect assumptions under the mixed fuzzy domain, which is not true in a fuzzy environment.

scholarly journals Research on Computing Efficiency of MapReduce in Big Data Environment

2019 ◽  
Vol 26 ◽  
pp. 03002
Author(s):  
Tilei Gao ◽  
Ming Yang ◽  
Rong Jiang ◽  
Yu Li ◽  
Yao Yao
Keyword(s):  
Big Data ◽  
Distributed Computing ◽  
Large Data ◽  
Time Consumption ◽  
Computing Model ◽  
Important Solution ◽  
Data Volume ◽  
Computing Framework ◽  
Data Environment

The emergence of big data has brought a great impact on traditional computing mode, the distributed computing framework represented by MapReduce has become an important solution to this problem. Based on the big data, this paper deeply studies the principle and framework of MapReduce programming. On the basis of mastering the principle and framework of MapReduce programming, the time consumption of distributed computing framework MapReduce and traditional computing model is compared with concrete programming experiments. The experiment shows that MapReduce has great advantages in large data volume.

scholarly journals A genetic algorithm for the fuzzy shortest path problem in a fuzzy network

2020 ◽  
Author(s):  
Lihua Lin ◽  
Chuzheng Wu ◽  
Li Ma
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Ad Hoc ◽  
Real Life ◽  
Shortest Path Problem ◽  
Arc Length ◽  
Routing Problem ◽  
Path Lengths ◽  
Fuzzy Network ◽  
Arc Lengths

Abstract The shortest path problem (SPP) is an optimization problem of determining a path between specified source vertex s and destination vertex t in a fuzzy network. Fuzzy logic can handle the uncertainties, associated with the information of any real life problem, where conventional mathematical models may fail to reveal proper result. In classical SPP, real numbers are used to represent the arc length of the network. However, the uncertainties related with the linguistic description of arc length in SPP are not properly represented by real number. We need to address two main matters in SPP with fuzzy arc lengths. The first matter is how to calculate the path length using fuzzy addition operation and the second matter is how to compare the two different path lengths denoted by fuzzy parameter. We use the graded mean integration technique of triangular fuzzy numbers to solve this two problems. A common heuristic algorithm to solve the SPP is the genetic algorithm. In this manuscript, we have introduced an algorithmic method based on genetic algorithm for determining the shortest path between a source vertex s and destination vertex t in a fuzzy graph with fuzzy arc lengths in SPP. A new crossover and mutation is introduced to solve this SPP. We also describe the QoS routing problem in a wireless ad hoc network.

SH: A Novel Method for the Dynamic and Shortest Path Problem

2010 ◽  
Vol 129-131 ◽  
pp. 1013-1017
Author(s):  
Ya Fei Guo ◽  
Zheng Qin ◽  
Rong Hua Guo ◽  
Lei Ji
Keyword(s):  
Shortest Path ◽  
Heuristic Search ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Real Life ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Current Path ◽  
Novel Method ◽  
Better Than

For the dynamic and shortest path problem, a novel algorithm SH(simulate human) is designed by simulating the process of our searching path in real life. The algorithm adopts the idea of heuristic search and integrates with the ant colony algorithm, in which the saved current path, the idea of “ask once every junction”, the bypassing barrier search and other some related definitions are proposed, as well as the ant colony algorithm is improved, so as to find the better solution and reduce the searching time. The experimental results show that the algorithm runs better than other existing methods. Moreover, it can find the shortest path or the approximate shortest one in a shorter time on road networks of any scales. Especially, SH algorithm is more effective for the large scale road network.

A Study of Neutrosophic Shortest Path Problem

2020 ◽  
pp. 148-179 ◽  
Author(s):  
Ranjan Kumar ◽  
Arindam Dey ◽  
Said Broumi ◽  
Florentin Smarandache
Keyword(s):  
Shortest Path ◽  
Real Life ◽  
Shortest Path Problem ◽  
Combinatorial Optimization Problem ◽  
Neutrosophic Set ◽  
Multi Criteria Decision Making ◽  
Numerical Examples ◽  
Decision Making Problem ◽  
Theory Uncertainty ◽  
Arc Lengths

Shortest path problem (SPP) is an important and well-known combinatorial optimization problem in graph theory. Uncertainty exists almost in every real-life application of SPP. The neutrosophic set is one of the popular tools to represent and handle uncertainty in information due to imprecise, incomplete, inconsistent, and indeterminate circumstances. This chapter introduces a mathematical model of SPP in neutrosophic environment. This problem is called as neutrosophic shortest path problem (NSPP). The utility of neutrosophic set as arc lengths and its real-life applications are described in this chapter. Further, the chapter also includes the different operators to handle multi-criteria decision-making problem. This chapter describes three different approaches for solving the neutrosophic shortest path problem. Finally, the numerical examples are illustrated to understand the above discussed algorithms.

scholarly journals Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes

2019 ◽  
pp. 1-9
Author(s):  
Jerome Jourquin ◽  
Stephanie Birkey Reffey ◽  
Cheryl Jernigan ◽  
Mia Levy ◽  
Glendon Zinser ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Big Data ◽  
Patient Outcomes ◽  
Data Science ◽  
Real Life ◽  
Large Data ◽  
Patient Advocacy ◽  
Imaging Data ◽  
Data Repositories ◽  
Improve Patient

Integrating different types of data, including electronic health records, imaging data, administrative and claims databases, large data repositories, the Internet of Things, genomics, and other omics data, is both a challenge and an opportunity that must be tackled head on. We explore some of the challenges and opportunities in optimizing data integration to accelerate breast cancer discovery and improve patient outcomes. Susan G. Komen convened three meetings (2015, 2017, and 2018) with various stakeholders to discuss challenges, opportunities, and next steps to enhance the use of big data in the field of breast cancer. Meeting participants agreed that big data approaches can enhance the identification of better therapies, improve outcomes, reduce disparities, and optimize precision medicine. One challenge is that databases must be shared, linked with each other, standardized, and interoperable. Patients want to be active participants in research and their own care, and to control how their data are used. Many patients have privacy concerns and do not understand how sharing their data can help to effectively drive discovery. Public education is essential, and breast cancer researchers who are skilled in using and analyzing big data are needed. Patient advocacy groups can play multiple roles to help maximize and leverage big data to better serve patients. Komen is committed to educating patients on big data issues, encouraging data sharing by all stakeholders, assisting in training the next generation of data science breast cancer researchers, and funding research projects that will use real-life data in real time to revolutionize the way breast cancer is understood and treated.

scholarly journals Application of Pentagonal Neutrosophic Number in Shortest Path Problem

2020 ◽  
pp. 21-28
Author(s):  
Avishek Chakraborty ◽  
Keyword(s):  
Cost Function ◽  
Shortest Path ◽  
Score Function ◽  
Path Selection ◽  
Shortest Path Problem ◽  
Time Dependent ◽  
Accuracy Function ◽  
Shortest Path Algorithm ◽  
Human Kind ◽  
Neutrosophic Number

Real-human kind issues have distinct sort of ambiguity and among them; one of the critical troubles is solving the shortest path problem. In this contribution, we applied the developed score function and accuracy function of pentagonal neutrosophic number (PNN) into a shortage path selection problem. Further, a time dependent and heuristic cost function related shortest path algorithm is considered here in PNN area and solved it utilizing an influx of dissimilar rational pioneer thinking. Lastly, estimation of total ideal time of the graph reflects the importance of this noble work.

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