How Big Data Utilization Affect Firms Marketing-related Strategies and Performance: An Abstract

2020 ◽  
pp. 39-40
Author(s):  
Itzhak Gnizy
Keyword(s):  
Big Data ◽  
Data Utilization ◽  
And Performance

Big for Everyone? Big Data, Firm Size and Performance

2020 ◽  
Author(s):  
Raffaele Conti ◽  
Miguel Godinho de Matos ◽  
giovanni valentini
Keyword(s):  
Big Data ◽  
Firm Size ◽  
And Performance

scholarly journals Study on Economic Activity Analysis based on the Interaction of “Operation, Value and Performance”

2020 ◽  
Vol 165 ◽  
pp. 06009
Author(s):  
Jie Gao
Keyword(s):  
Big Data ◽  
Economic Activity ◽  
Corporate Strategy ◽  
Activity Analysis ◽  
Operating Efficiency ◽  
Analysis Model ◽  
Business Decision ◽  
Traceability Analysis ◽  
And Performance ◽  
Analysis System

In order to meet external regulation and challenges, and improve the quality of internal economic activity analysis, this study establishes a linkage analysis system from corporate strategy to strategic objectives to financial indicators to business indicators by building 3 independent and interrelated analysis models. One of them is the model of influencing factors of change of operating efficiency index, one of them is the traceability analysis model of the sales of electricity and electricity price, and the last one is an investment performance traceability analysis model. In this study, the actual data of a unit is used as an example. With the help of big data analysis, we fully tap the value of the company’s big data, accurately locate the weak links and risk points of management. By doing this we finely promote economic activity analysis system more comprehensive, more real-time, more dynamic and more intelligent, and thus improve the efficiency of business decision-making. The practicality of economic activity analysis based on “operation, value and performance” is confirmed.

Our Knowledge Management Hubble May Need Glasses

2018 ◽  
Vol 14 (1) ◽  
pp. 30-50 ◽  
Author(s):  
William H. Money ◽  
Stephen J. Cohen
Keyword(s):  
Big Data ◽  
Knowledge Management ◽  
Real Time ◽  
Complex Data ◽  
Data Systems ◽  
Fault Handling ◽  
Architectural Framework ◽  
Big Data Systems ◽  
And Performance ◽  
Streaming Big Data

This article analyzes the properties of unknown faults in knowledge management and Big Data systems processing Big Data in real-time. These faults introduce risks and threaten the knowledge pyramid and decisions based on knowledge gleaned from volumes of complex data. The authors hypothesize that not yet encountered faults may require fault handling, an analytic model, and an architectural framework to assess and manage the faults and mitigate the risks of correlating or integrating otherwise uncorrelated Big Data, and to ensure the source pedigree, quality, set integrity, freshness, and validity of the data. New architectures, methods, and tools for handling and analyzing Big Data systems functioning in real-time will contribute to organizational knowledge and performance. System designs must mitigate faults resulting from real-time streaming processes while ensuring that variables such as synchronization, redundancy, and latency are addressed. This article concludes that with improved designs, real-time Big Data systems may continuously deliver the value of streaming Big Data.

scholarly journals Big Data Analytics in E-commerce for the U.S. and China Through Literature Reviewing

2021 ◽  
Vol 9 (1) ◽  
pp. 16-44
Author(s):  
Weiqing Zhuang ◽  
Morgan C. Wang ◽  
Ichiro Nakamoto ◽  
Ming Jiang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Web Of Science ◽  
Big Data Analytics ◽  
Theoretical Research ◽  
Analysis Method ◽  
Theoretical Comparison ◽  
Statistical Analysis Method ◽  
And Performance ◽  
The U.S

Abstract Big data analytics (BDA) in e-commerce, which is an emerging field that started in 2006, deeply affects the development of global e-commerce, especially its layout and performance in the U.S. and China. This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S. and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases, Web of Science and CNKI, aimed at the U.S. and China. The results of this study help clarify doubts regarding the development of China’s e-commerce, which exceeds that of the U.S. today, in view of the theoretical comparison of BDA in e-commerce between them.

scholarly journals Research and Analysis of an Enterprise E-Commerce Marketing System Under the Big Data Environment

2021 ◽  
Vol 33 (6) ◽  
pp. 1-19
Author(s):  
Linze Li ◽  
Jun Zhang
Keyword(s):  
Big Data ◽  
Online Shopping ◽  
Practical Significance ◽  
Marketing System ◽  
Database Query ◽  
Online Sales ◽  
Modern Business ◽  
And Performance ◽  
Performance Conditions ◽  
Big Data Technology

As an emerging online shopping method, e-commerce has been widely popular since the popularization of the Internet. Online sales and online shopping have become the trend of modern business development. However, the functionality and performance conditions of the existing platform cannot be closely integrated with the merchant's own business. The purpose of this paper is to study the enterprise e-commerce marketing system based on big data. The system design of this paper adopts SSH framework as the main technology, the database selects HBase database, and the front end combines with Web2.0 technology for the interaction of interface display and operation. The experimental results show that applying big data technology to enterprise e-commerce marketing system has extremely important practical significance. Perform a performance analysis on this system,when the amount of data reaches 4000, the speed of HBase is 10.486s, and the query time of Mysql is 50.184s. It can be seen that the Hbase database query speed is much faster than the Mysql database query speed.

scholarly journals Task-based programming in COMPSs to converge from HPC to big data

2017 ◽  
Vol 32 (1) ◽  
pp. 45-60 ◽  
Author(s):  
Javier Conejero ◽  
Sandra Corella ◽  
Rosa M Badia ◽  
Jesus Labarta
Keyword(s):  
Big Data ◽  
High Performance ◽  
Programming Model ◽  
Good Alternative ◽  
Programming Models ◽  
Suitable Model ◽  
Advantages And Disadvantages ◽  
Big Data Applications ◽  
And Performance ◽  
The Right

Task-based programming has proven to be a suitable model for high-performance computing (HPC) applications. Different implementations have been good demonstrators of this fact and have promoted the acceptance of task-based programming in the OpenMP standard. Furthermore, in recent years, Apache Spark has gained wide popularity in business and research environments as a programming model for addressing emerging big data problems. COMP Superscalar (COMPSs) is a task-based environment that tackles distributed computing (including Clouds) and is a good alternative for a task-based programming model for big data applications. This article describes why we consider that task-based programming models are a good approach for big data applications. The article includes a comparison of Spark and COMPSs in terms of architecture, programming model, and performance. It focuses on the differences that both frameworks have in structural terms, on their programmability interface, and in terms of their efficiency by means of three widely known benchmarking kernels: Wordcount, Kmeans, and Terasort. These kernels enable the evaluation of the more important functionalities of both programming models and analyze different work flows and conditions. The main results achieved from this comparison are (1) COMPSs is able to extract the inherent parallelism from the user code with minimal coding effort as opposed to Spark, which requires the existing algorithms to be adapted and rewritten by explicitly using their predefined functions, (2) it is an improvement in terms of performance when compared with Spark, and (3) COMPSs has shown to scale better than Spark in most cases. Finally, we discuss the advantages and disadvantages of both frameworks, highlighting the differences that make them unique, thereby helping to choose the right framework for each particular objective.

Web Intelligence

Big Data ◽  
2016 ◽  
pp. 711-733 ◽  
Author(s):  
Jafreezal Jaafar ◽  
Kamaluddeen Usman Danyaro ◽  
M. S. Liew
Keyword(s):  
Big Data ◽  
Knowledge Base ◽  
Data Model ◽  
Performance Comparison ◽  
Web Ontology Language ◽  
Web Intelligence ◽  
Knowledge Based ◽  
Ontology Language ◽  
And Performance ◽  
Fuzzy Knowledge

This chapter discusses about the veracity of data. The veracity issue is the challenge of imprecision in big data due to influx of data from diverse sources. To overcome this problem, this chapter proposes a fuzzy knowledge-based framework that will enhance the accessibility of Web data and solve the inconsistency in data model. D2RQ, protégé, and fuzzy Web Ontology Language applications were used for configuration and performance. The chapter also provides the completeness fuzzy knowledge-based algorithm, which was used to determine the robustness and adaptability of the knowledge base. The result shows that the D2RQ is more scalable with respect to performance comparison. Finally, the conclusion and future lines of the research were provided.

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