Exploring relationships between medical college rankings and performance with big data

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.

Download Full-text

Our Knowledge Management Hubble May Need Glasses

International Journal of Knowledge Management ◽

10.4018/ijkm.2018010103 ◽

2018 ◽

Vol 14 (1) ◽

pp. 30-50 ◽

Cited By ~ 2

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.

Download Full-text

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

Journal of Systems Science and Information ◽

10.21078/jssi-2021-016-29 ◽

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.

Download Full-text

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

Journal of Organizational and End User Computing ◽

10.4018/joeuc.20211101.oa15 ◽

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.

Download Full-text

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

The International Journal of High Performance Computing Applications ◽

10.1177/1094342017701278 ◽

2017 ◽

Vol 32 (1) ◽

pp. 45-60 ◽

Cited By ~ 11

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.

Download Full-text

Web Intelligence

Big Data ◽

10.4018/978-1-4666-9840-6.ch033 ◽

2016 ◽

pp. 711-733 ◽

Cited By ~ 1

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.

Download Full-text

Value that matters: intellectual capital and big data to assess performance in healthcare. An empirical analysis on the European context

Journal of Intellectual Capital ◽

10.1108/jic-02-2020-0067 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ginevra Gravili ◽

Francesco Manta ◽

Concetta Lucia Cristofaro ◽

Rocco Reina ◽

Pierluigi Toma

Keyword(s):

Big Data ◽

Organizational Performance ◽

Healthcare System ◽

Healthcare Sector ◽

Healthcare Organizations ◽

Content Type ◽

Structural Capital ◽

And Performance ◽

The Relationship

PurposeThe aim of this paper is to analyze and measure the effects of intellectual capital (IC), i.e. human capital (HC), relational capital (RC) and structural capital (SC), on healthcare industry organizational performance and understanding the role of data analytics and big data (BD) in healthcare value creation (Wang et al., 2018). Through the assessment of determined variables specific for each component of IC, the paper identifies the guidelines and suggests propositions for a more efficient response in terms of services provided to citizens and, specifically, patients, as well as predicting effective strategies to improve the care management efficiency in terms of cost reduction.Design/methodology/approachThe study has a twofold approach: in the first part, the authors operated a systematic review of the academic literature aiming to enquire the relationship between IC, big data analytics (BDA) and healthcare system, which were also the descriptors employed. In the second part, the authors built an econometric model analyzed through panel data analysis, studying the relationship between IC, namely human, relational and structural capital indicators, and the performance of healthcare system in terms of performance. The study has been conducted on a sample of 28 European countries, notwithstanding the belonging to specific international or supranational bodies, between 2011 and 2016.FindingsThe paper proposes a data-driven model that presents new approach to IC assessment, extendable to other economic sectors beyond healthcare. It shows the existence of a positive impact (turning into a mathematical inverse relationship) of the human, relational and structural capital on the performance indicator, while the physical assets (i.e. the available beds in hospitals on total population) positively mediates the relationship, turning into a negative impact of non-IC related inputs on healthcare performance. The result is relevant in terms of managerial implications, enhancing the opportunity to highlight the crucial role of IC in the healthcare sector.Research limitations/implicationsThe relationship between IC indicators and performance could be employed in other sectors, disseminating new approaches in academic research. Through the establishment of a relationship between IC factors and performance, the authors implemented an approach in which healthcare organizations are active participants in their economic and social value creation. This challenges the views of knowledge sharing deeply held inside organizations by creating “new value” developed through a more collaborative and permeated approach in terms of knowledge spillovers. A limitation is given by a fragmented policymaking process which carries out different results in each country.Practical implicationsThe analysis provides interesting implications on multiple perspectives. The novelty of the study provides interesting implications for managers, practitioners and governmental bodies. A more efficient healthcare system could provide better results in terms of cost minimization and reduction of hospitalization period. Moreover, dissemination of new scientific knowledge and drivers of specialization enhances best practices sharing in the healthcare sector. On the other hand, an improvement in preventive medicine practices could help in reducing the overload of demand for curative treatments, on the perspective of sharply decreasing the avoidable deaths rate and improving societal standards.Originality/valueThe authors provide a new holistic framework on the relationship between IC, BDA and organizational performance in healthcare organizations through a systematic review approach and an empirical panel analysis at a multinational level, which is quite a novelty regarding the healthcare. There is little research focussed on healthcare industries' organizational performance, and, specifically, most of the research on IC in healthcare delivered results in terms of theoretical contribution and qualitative analyzes. The authors even contributed to analyze the healthcare industry in the light of the possible existence of synergies and networks among countries.

Download Full-text

Big data and performance measurement research: trends, evolution and future opportunities

Measuring Business Excellence ◽

10.1108/mbe-06-2019-0053 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Cited By ~ 1

Author(s):

Alberto Sardi ◽

Enrico Sorano ◽

Valter Cantino ◽

Patrizia Garengo

Keyword(s):

Big Data ◽

Performance Measurement ◽

Performance Measurement System ◽

Future Research ◽

Holistic View ◽

Content Type ◽

Digital Revolution ◽

Research Opportunities ◽

And Performance ◽

Number Of Publications

Purpose Current literature recognised big data as a digital revolution affecting all organisational processes. To obtain a competitive advantage from the use of big data, an efficient integration in a performance measurement system (PMS) is needed, but it is still a “great challenge” in performance measurement research. This paper aims to review the big data and performance measurement studies to identify the publications’ trends and future research opportunities. Design/methodology/approach The authors reviewed 873 documents on big data and performance carrying out an extensive bibliometric analysis using two main techniques, i.e. performance analysis and science mapping. Findings Results point to a significant increase in the number of publications on big data and performance, highlighting a shortage of studies on business, management and accounting areas, and on how big data can improve performance measurement. Future research opportunities are identified. They regard the development of further research to explain how performance measurement field can effectively integrate big data into a PMS and describe the main themes related to big data in performance measurement literature. Originality/value This paper gives a holistic view of big data and performance measurement research through the inclusion of numerous contributions on different research streams. It also encourages further study for developing concrete tools.

Download Full-text