REFLECTIONS ON THE BASIC FUNCTIONS OF JUDICIAL BIG DATA IN CHINA —— TAKING THE ANALYSIS PROCESS OF AN INSURANCE CASE AS AN EXAMPLE

2019 ◽  
Vol 35 (3) ◽  
pp. 321-339
Author(s):  
Hongyan Pan
Keyword(s):  
Big Data ◽  
Analysis Process ◽  
Insurance Case ◽  
Basic Functions

scholarly journals Strategies to Implement Big Data Analytics in Telecommunication Organizations

2021 ◽  
Vol 10 (04) ◽  
pp. 064-076
Author(s):  
Dr. Delton Aneato ◽  
Dr. Cesar Castellanos
Keyword(s):  
Big Data ◽  
Competitive Advantage ◽  
Organizational Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The United States ◽  
Communication Strategy ◽  
Analysis Process ◽  
Successful Communication ◽  
Multiple Case

Information technology (IT) leaders who do not invest in big data projects may struggle to gain a competitive advantage and business insights to improve performance. Grounded in Kotter’s change and Six Sigma models, the purpose of this qualitative multiple case study was to explore strategies IT leaders use to implement big data analytics successfully. The participants comprised 4 IT leaders from 2 telecommunication organizations in the United States of America, who expertly used big data analytics strategies to promote and maximize competitive advantage. Data were collected from semistructured interviews, company documents, and project-related documents. The collected information was examined by utilizing a thematic analysis approach. Four themes emerged from the data analysis process communication, training, employee involvement in decisions, and teamwork strategy. A key recommendation from these findings is for IT leaders to use successful communication strategies to convey the vision and objectives to all organizational levels. The successful communication-strategy can help evaluate business trends, forecasts, improve overall organizational performance and competitive advantage. The implications for positive social change include the potential for job creation, thus catalysing economic growth within communities.

scholarly journals Big Data Analysis and Research on Fracturing Construction Parameters of Shale Gas Horizontal Wells—A Case Study of Horizontal Wells in Fuling Demonstration Area, China

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8357
Author(s):  
Minxuan Li ◽  
Liang Cheng ◽  
Dehua Liu ◽  
Jiani Hu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Shale Gas ◽  
Optimal Parameter ◽  
Rapid Development ◽  
Horizontal Wells ◽  
Big Data Analysis ◽  
Arithmetic Means ◽  
Correlation Clustering ◽  
Analysis Process

With the rapid development of computer science and technology, the Chinese petroleum industry has ushered in the era of big data. In this study, by collecting fracturing data from 303 horizontal wells in the Fuling Shale Gas Demonstration Area in China, a series of big data analysis studies was conducted using Pearson’s correlation coefficient, the unweighted pair group with arithmetic means method, and the graphical plate method to determine which is best. The fracturing parameters were determined through a series of big data analysis studies. The big data analysis process is divided into three main steps. The first is data preprocessing to screen out eligible, high-yielding wells. The second is a fracturing parameter correlation clustering analysis to determine the reasonableness of the parameters. The third is a big data panel method analysis of specific fracturing construction parameters to determine the optimal parameter range. The analyses revealed that the current amount of 100 mesh sand in the Fuling area is unreasonable; further, there are different preferred areas for different fracturing construction parameters. We have combined different fracturing parameter schemes by preferring areas. This analysis process is expected to provide new ideas regarding fracturing scheme design for engineers working on the frontline.

BIG Data

2019 ◽  
pp. 171-200
Author(s):  
K. Hariharanath
Keyword(s):  
Big Data ◽  
Business Models ◽  
Business Processes ◽  
Market Competition ◽  
Global Market ◽  
Strategy Management ◽  
Complex Information ◽  
New Concepts ◽  
Information And Communication ◽  
Basic Functions

The basic functions such as production, marketing, and finance continue to be the same from an agricultural economy to an industrial economy. Business processes, procedures, methods, strategy, management thinking, and approach related to basic functions have been changed due to global market competition. Consequent to global competition, business activities have become more complex. Due to this complexity, the type and quantum of information required by the business enterprises are increasing. It is interesting to note that information and communication technology is providing many new concepts to handle and manage the complex information to remain competitive in the global market. The concepts such as big data and cloud computing along with other collaborative technology facilitate creating conceptual business models for facing realities in the global market. This chapter mainly explains with two case illustrations of the importance of the above concepts for developing business models for textile and retail sectors.

scholarly journals Analisa Performa Klastering Data Besar pada Hadoop

2021 ◽  
Vol 4 (2) ◽  
pp. 174-183
Author(s):  
Hadian Mandala Putra ◽  
◽  
Taufik Akbar ◽  
Ahwan Ahmadi ◽  
Muhammad Iman Darmawan ◽  
...  
Keyword(s):  
Big Data ◽  
Relational Model ◽  
Data Types ◽  
Analysis Process ◽  
A Value ◽  
Silhouette Coefficient ◽  
Data Partitions ◽  
Hadoop Cluster ◽  
Map Function ◽  
Cluster Quality

Big Data is a collection of data with a large and complex size, consisting of various data types and obtained from various sources, overgrowing quickly. Some of the problems that will arise when processing big data, among others, are related to the storage and access of big data, which consists of various types of data with high complexity that are not able to be handled by the relational model. One technology that can solve the problem of storing and accessing big data is Hadoop. Hadoop is a technology that can store and process big data by distributing big data into several data partitions (data blocks). Problems arise when an analysis process requires all data spread out into one data entity, for example, in the data clustering process. One alternative solution is to do a parallel and scattered analysis, then perform a centralized analysis of the results of the scattered analysis. This study examines and analyzes two methods, namely K-Medoids Mapreduce and K-Modes without Mapreduce. The dataset used is a dataset about cars consisting of 3.5 million rows of data with 400MB distributed in a Hadoop Cluster (consisting of more than one engine). Hadoop has a MapReduce feature, consisting of 2 functions, namely map and reduce. The map function performs a selection to retrieve a key, value pairs, and returns a value in the form of a collection of key value pairs, and then the reduce function combines all key value pairs from several map functions. The results of the cluster quality evaluation are tested using the Silhouette Coefficient testing metric. The K-Medoids MapReduce algorithm for the car dataset gives a silhouette value of 0.99 with a total of 2 clusters.

scholarly journals Economic Development Trend Prediction Model Based on Unsupervised Learning in the Internet of Things Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Min Kuang
Keyword(s):  
Economic Development ◽  
Big Data ◽  
Internet Of Things ◽  
Development Trend ◽  
The Internet ◽  
Actual Situation ◽  
Trend Prediction ◽  
Analysis Process ◽  
Analysis System ◽  
The Internet Of Things

In order to explore the economic development trend under the environment of the Internet of Things, this paper improves the chaotic algorithm of the Internet of Things and constructs an economic development trend analysis system based on big data technology. Moreover, this paper analyzes the actual situation of big data processing data and conducts research on economic data analysis process. In addition, this paper conducts effective research on the various modules of functional analysis, obtains the system functional architecture, constructs the system functional structure based on the actual situation, and analyzes the operating process of the system. Finally, this paper designs a simulation test based on actual data. The experimental research results show that the system model proposed in this paper has a good performance in the forecast of economic development trends, and the system can be used for forecasting in subsequent economic development forecasts.

Fast simulation method for airframe analysis based on big data

2016 ◽  
Vol 05 (04) ◽  
pp. 1650022
Author(s):  
DongLiang Liu ◽  
LiXin Zhang
Keyword(s):  
Big Data ◽  
Structural Analysis ◽  
Error Control ◽  
Simulation Method ◽  
Fast Simulation ◽  
Analysis Process ◽  
Fast Simulation Method

In this paper, we employ the big data method to structural analysis by considering the correlations between loads and loads, loads and results and results and results. By means of fundamental mathematics and physical rules, the principle, feasibility and error control of the method are discussed. We then establish the analysis process and procedures. The method is validated by two examples. The results show that the fast simulation method based on big data is fast and precise when it is applied to structural analysis.

scholarly journals Big Data Log-Based Correlation Analysis Profiling Auto Generation Model

2018 ◽  
Author(s):  
Dongsik Sohn ◽  
Seungpyo Huh ◽  
Taejin Lee ◽  
Jin Kwak
Keyword(s):  
Big Data ◽  
Response Speed ◽  
Attack Detection ◽  
Unstructured Data ◽  
Generation Model ◽  
Security Monitoring ◽  
Detection Analysis ◽  
Analysis Process ◽  
Short Period ◽  
Data Log

The number of SIEM introduction is increasing in order to detect threat patterns in a short period of time with a large amount of structured/unstructured data, to precisely diagnose crisis to threats, and to provide an accurate alarm to an administrator by correlating collected information. However, it is difficult to quickly recognize and handle with various attack situations using a solution equipped with complicated functions during security monitoring. In order to overcome this situation, new detection analysis process has been required, and there is an effort to increase response speed during security monitoring and to expand accurate linkage analysis technology. In this paper, reflecting these requirements, we design and propose profiling auto-generation model that can improve the efficiency and speed of attack detection for potential threats requirements. we design and propose profiling auto-generation model that can improve the efficiency and speed of attack detection for potential threats.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Sign in / Sign up

Export Citation Format

Share Document