scholarly journals Parallel Classification Algorithm Design of Human Resource Big Data Based on Spark Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wang Zhouhuo
Keyword(s):  
Big Data ◽  
Human Resources ◽  
Human Resource ◽  
Data Clustering ◽  
Ant Colony Algorithm ◽  
Algorithm Design ◽  
Large Data ◽  
Classification Algorithm ◽  
Classification Rule ◽  
Fuzzy Genetic

In order to solve the problem of large data classification of human resources, a new parallel classification algorithm of large data of human resources based on the Spark platform is proposed in this study. According to the spark platform, it can complete the update and distance calculation of the human resource big data clustering center and design the big data clustering process. Based on this, the K-means clustering method is introduced to mine frequent itemsets of large data and optimize the aggregation degree of similar large data. A fuzzy genetic algorithm is used to identify the balance of big data. This study adopts the selective integration method to study the unbalanced human resource database classifier in the process of transmission, introduces the decision contour matrix to construct the anomaly support model of the set of unbalanced human resource data classifier, identifies the features of the big data of human resource in parallel, repairs the relevance of the big data of human resource, introduces the improved ant colony algorithm, and finally realizes the design of the parallel classification algorithm of the big data of human resource. The experimental results show that the proposed algorithm has a low time cost, good classification effect, and ideal parallel classification rule complexity.

Uncertainty-Based Clustering Algorithms for Large Data Sets

2018 ◽  
pp. 1-33 ◽  
Author(s):  
B. K. Tripathy ◽  
Hari Seetha ◽  
M. N. Murty
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Mining Machine ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
New Algorithms

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

scholarly journals Big Data: Concept, Potentialities and Vulnerabilities

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fernando Almeida
Keyword(s):  
Social Networks ◽  
Organizational Culture ◽  
Big Data ◽  
Information Systems ◽  
Human Resources ◽  
Large Data ◽  
Data Driven ◽  
The Internet ◽  
Strategic Decisions ◽  
Use Of The Internet

The evolution of information systems and the growth in the use of the Internet and social networks has caused an explosion in the amount of available data relevant to the activities of the companies. Therefore, the treatment of these available data is vital to support operational, tactical and strategic decisions. This paper aims to present the concept of big data and the main technologies that support the analysis of large data volumes. The potential of big data is explored considering nine sectors of activity, such as financial, retail, healthcare, transports, agriculture, energy, manufacturing, public, and media and entertainment. In addition, the main current opportunities, vulnerabilities and privacy challenges of big data are discussed. It was possible to conclude that despite the potential for using the big data to grow in the previously identified areas, there are still some challenges that need to be considered and mitigated, namely the privacy of information, the existence of qualified human resources to work with Big Data and the promotion of a data-driven organizational culture.

BIG DATA IN HUMAN RESOURCE MANAGEMENT

2020 ◽  
Vol 9 (3) ◽  
pp. 76-80
Author(s):  
D. Bragina ◽  
N. Molodchik
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Resource Management ◽  
Human Resources ◽  
Human Resource Management ◽  
Human Resource ◽  
Human Resources Management ◽  
Big Data Analysis ◽  
Resources Management ◽  
Employee Data

The article discusses the possibilities of using big data in the field of human resources management, shows the difficulties that can be encountered when introducing these technologies into the work of the company. The main problems of the use of employee data by companies for the analysis, forecasting and improvement production indicators are given. Examples of companies that successfully use big data in their work are shown. Recommendations how to introduce the technology of big data analysis in the field of human resource management are given.

A Survey on Service Level Components in Big-Cloud-IoT Systems with Hybrid Meta-heuristic Techniques

2020 ◽  
pp. 95-101
Author(s):  
Xueqiang Yin ◽  
Athreya Tao Chen
Keyword(s):  
Big Data ◽  
Ant Colony Algorithm ◽  
Data Transfer ◽  
Processing System ◽  
Large Data ◽  
Computing System ◽  
Service Level ◽  
Edge Computing ◽  
Service Components ◽  
Iot Devices

Big data is one such technology. When we receive huge volume of data, there will be high demand in processing the huge data. It can also be said challenging task in big data processing. The increases in IoT devices in the network system collect more data to be processed in centralized devices called cloud storage. Every big data is processed and stored in the cloud. To overcome the performance and latency issues in large data computation, big cloud processing system uses edge computing in it. One of the key components of IoT is edge computing. We combine big data with cloud and edge computing in this paper as hybrid edge computing system. In the edge computing system, huge number of IoT devices computes services in its nearby network edge. Data sharing and transmission between the various service components may affect performance of the system. The main aim of this research article is to reduce the delay in data transfer between the components. This optimization goal is achieved by new Hybrid Meta-heuristic optimization (HMeO) algorithm. New HMeO algorithm designed for IoT devices to deploy the service components. MHO model is design to optimize the process by selecting the edge computing with minimum latency. Our proposed HMeO algorithm is compared with existing genetic algorithm and ant colony algorithm. The result shows HMeO algorithm provides more performance and efficient in in-depth data analysing and locating the component in big databased cloud environment.

scholarly journals Analysis of Artistic Modeling of Opera Stage Clothing Based on Big Data Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Weiwei Luo
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Feature Recognition ◽  
Programming Model ◽  
Large Data ◽  
Binary Function ◽  
Good Convergence ◽  
Second Order Cone ◽  
Space State

In order to deal with the problem that the traditional stage costume artistry analysis method cannot correct the results of big data clustering, which leads to deviations in the extraction of costume artistry features, this paper proposes a clothing artistic modeling method based on big data clustering algorithm. The proposed method provides a database for big data clustering by constructing the attribute set of the big data feature sequence training set and, at the same time, constructing a second-order cone programming model to correct the big data. Aiming at the problem that traditional stage costume art analysis methods cannot correct the clustering results of big data. On this basis, the costume elements of the opera stage are segmented, initialized, and transformed into a binary function. Finally, using the convolutional neural network, combining the element segmentation results and the large data clustering space state vector, a feature extraction model of stage costume art is constructed. Experimental results show that the model has good convergence, short time-consuming, high accuracy, and ideal feature recognition capabilities.

scholarly journals Scientific Impact of Sports on Human Health and Physique Based on Optimization Big Data Ant Colony Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lin Wu
Keyword(s):  
Big Data ◽  
Physical Fitness ◽  
Human Health ◽  
Ant Colony Algorithm ◽  
Impact Analysis ◽  
Ant Colony ◽  
Classification Rule ◽  
Scientific Impact ◽  
Fitness Level ◽  
The Impact

With the continuous improvement of living standards, people began to pay more and more attention to sports, and the impact of sports on human health and physique has been paid more and more attention. This study mainly analyzes the scientific impact of sports on human health and physique under the background of big data. Firstly, the big data analytic hierarchy process is used to construct the comprehensive evaluation structure system of sports on human health and physique. Then, an improved big data adaptive ant colony classification rule algorithm is proposed. Finally, the performance evaluation and physical impact analysis of the improved big data algorithm are carried out. The results show that compared with other algorithms, ACA ∗ (ant colony algorithm) based on big data has more obvious advantages in stability, optimization ability, running time, and convergence speed and is more suitable for practical application. In general, the improvement of the physical fitness level of the association members in 2019 mainly depends on the results of the improvement of the physical fitness level. In the future, we need to strengthen physical exercise, change living habits and traffic habits, and other methods to optimize the overall physical fitness.

Big Data and People Management

2019 ◽  
pp. 127-153
Author(s):  
Daria Sarti ◽  
Teresina Torre
Keyword(s):  
Big Data ◽  
Human Resources ◽  
Human Resource Management ◽  
Human Resource ◽  
Future Research ◽  
Key Points ◽  
Hr Managers ◽  
The Impact ◽  
Offer Suggestion

This chapter investigates the role of big data (BD) in human resource management (HRM). The interest is related to the strategic relevance of human resources (HR) and to the increasing importance of BD in every dimension of a company's life. The analysis focuses on the perception of the HR managers on the impact that BD and BD analytics may have on the HRM and the possible problems the HR departments may encounter when implementing human resources analytics (HRA). The authors' opinion is that attention to the perceptions shown by the HR managers is the more important element conditioning their attitude towards BD and it is the first feature influencing the possibility that BD can become a positive challenge. After the presentation of the topic and of the state of the art, the study is introduced. The main findings are discussed and commented to offer suggestion for HR managers and to underline some key points for future research in this field.

ICHC Framework

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Human Resources ◽  
Cultural Heritage ◽  
Human Resource ◽  
Data Model ◽  
Different Types ◽  
Distributed Operations ◽  
Relational Aspect ◽  
Data Context ◽  
Huge Variety

The Goal in this paper is to propose a cultural heritage data model and evolve towards the creation of a framework based on MongoDB that will allow to manage a JSON model representing the cultural heritage of a city ICHC (Intelligent Cultural Heritage of a City). This manuscript per the authors noticed that during the census of cultural heritage, the presence of human resources linked to heritage is not something that is represented in a smart engine of a framework. Which is why the goal is to integrate the human resource and therefore add a relational aspect to the NoSql documents so that the resulting framework can have a smart engine to link data.This model is a set of ICHD (Intelligent Cultural Heritage Document) which are JSON documents that represent of the different types of cultural heritage entities. Those documents will be managed in a MongoDB repository architecture that will allow to them, so that the microservices-based ICHC framework can offer a big data context that can handle a huge variety, volume and velocity of data and be based on distributed operations.

scholarly journals Big Data Clustering And Its Applications Examination

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3687-3693
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Data Sets ◽  
Clustering Methods ◽  
Time Saving ◽  
Data Set ◽  
The Many

Clustering is a type of mining process where the data set is categorized into various sub classes. Clustering process is very much essential in classification, grouping, and exploratory pattern of analysis, image segmentation and decision making. And we can explain about the big data as very large data sets which are examined computationally to show techniques and associations and also which is associated to the human behavior and their interactions. Big data is very essential for several organisations but in few cases very complex to store and it is also time saving. Hence one of the ways of overcoming these issues is to develop the many clustering methods, moreover it suffers from the large complexity. Data mining is a type of technique where the useful information is extracted, but the data mining models cannot utilized for the big data because of inherent complexity. The main scope here is to introducing a overview of data clustering divisions for the big data And also explains here few of the related work for it. This survey concentrates on the research of several clustering algorithms which are working basically on the elements of big data. And also the short overview of clustering algorithms which are grouped under partitioning, hierarchical, grid based and model based are seenClustering is major data mining and it is used for analyzing the big data.the problems for applying clustering patterns to big data and also we phase new issues come up with big data

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