Value Of Big Data Research Articles

A Look at the Focus on Big Data for Information Technology and Decision Making During 1994 to 2020

Big data starts booming in 2013 and has multiple applications in all walks of life. In such an environment, big data for information technology (BDI) and decision making (BDD) have formed some hot topics in common. This paper reviews the body of BDI and BDD research studies from 1994 to 2020, using bibliometrics analysis. The aim of this paper is to explore the current status, the correlation between BDI and BDD, the future trends and challenges. From time and space dimensions, CiteSpace and VOS viewer are used to obtain the annual trends of documents, the distribution of countries and sources, the citations and the h-index of BDI and BDD. The top three productive countries are the USA, China and the UK. From the perspective of h-index, the USA and the UK are at the forefront of the world. The value of big data is realized through information acquisition, storage, analysis, expression transmission and service sharing technologies, and the decision-making techniques exist throughout the process of big data analysis. “Business” and “Information science library science” are the latest hotspots of BDI. The appliances in the organization, supply chain management, education, and the environment are recent themes of BDD. Big data technology processing capabilities and network security issues are the main challenges in the future. This study contributes to the body of knowledge on BDI and BDD, and hops to help in understanding the evolution of them in relevant fields.

A Healthcare Quality Assessment Model Based on Outlier Detection Algorithm

With the extremely rapid growth of data in various industries, big data is gradually recognized and valued by people. Medical big data, which can best reflect the significance of big data value, has also received attention from various parties. In Saudi Arabia, healthcare quality assessment is mostly based on human experience and basic statistical methods. In this paper, we proposed a healthcare quality assessment model based on medical big data in a region of Saudi Arabia, which integrated traditional evaluation methods and machine learning based techniques. Healthcare data has been accurate and effective after noise processing, and the outliers could reflect certain medical quality information. An improved k-nearest neighbors (KNN) algorithm has been proposed and its time complexity have been reduced to be more suitable for big data processing. An outlier indicator has been established based on statistical methods and the improved KNN algorithm. Experimental results showed that the proposed approach has good potential for detecting hospitals with financial fraud and poor-quality medical care.

BIG DATA IN THE MARKETING EDUCATION PROGRAMS: EDUCATORS’ AND STUDENTS’ VIEWS AND ATTITUDES

ABSTRACT This research explores the perspectives of marketing students and educators about the Big Data courses in marketing education programs. It also examines drivers that predict interest on the part of marketing students in taking Big Data courses. Data was collected through interviews with 20 marketing educators, and a survey was completed by 480 marketing students. Results of this research show how marketing educators can introduce Big Data concepts in their marketing classes and incorporate new teaching techniques. The results of our first study show that ability factors, affective factors, and interest in big data have an impact on marketing students’ attitudes toward big data courses. Nevertheless, our analysis shows that the students’ competitiveness traits and the academic value of big data do not have an impact on marketing students’ attitudes toward big data courses. Our second study emphasizes the importance of integrating big data courses into marketing education programs. It also lists the requirements for teaching big data in marketing programs, including the skills, qualifications, and experience of big data instructors. Collectively, this research provides insights associated with the growing desire to upskill in big data by marketers and the belief that these skills will be necessary for the future.

Big Data using Cloud Computing: Security Issues and Approaches

Big Data is a data analysis methodology made possible by current technological and architectural advancements. However, big data requires a significant investment in hardware and processing power, making it prohibitively expensive for small and medium-sized firms to implement. Small and medium-sized enterprises can benefit from cloud computing promise of big data implementation. The MapReduce programming paradigm is used to process large amounts of data. The MapReduce paradigm typically necessitates networked connected storage and parallel computation. The computing requirements of MapReduce programming are frequently beyond the capabilities of small and medium-sized businesses. Cloud computing is network access to computing resources on demand, given by a third party Hardware cost reduction, processing cost reduction, and the flexibility to verify the value of big data are three significant reasons for small and medium-sized organizations to embrace cloud computing for big data technology implementation. Security and loss of control are two main concerns with cloud computing. Key Words: Big data, MapReduce, cloud computing, private cloud, public cloud, and hybrid cloud.

A Collective Intelligence Platform to Support Older Cancer Survivors: Towards the Definition of LifeChamps System and Big Data Reference Architecture.

Within the most recent years, most of the cancer patients are older age, which implies the necessity to a better understanding of aging and cancer connection. This work presents the LifeChamps solution built on top of cutting-edge Big Data architecture and HPC infrastructure concepts. An innovative architecture was envisioned supported by the Big Data Value Reference Model and answering the system requirements from high to low level and from logical to physical perspective, following the "4+1 architectural model".

Accounting perspectives on the business value of big data during and beyond the COVID-19 pandemic

Research Question: How can business organizations develop accounting practices to use big data to create competitive intelligence advantages to survive during and beyond the COVID-19 conditions? Motivation: We aim to provide new accounting perspectives for using big data techniques in business organizations beyond the COVID-19. Idea: We argue that the massive reduction in business capacity and operations will interpose the accounting and financial practices beyond the COVID-19 pandemic. There is a crucial need to uncover the underlying business practices and market circumstances toward the radical shifts in business and society. Data: Our paper uses a desk study method to investigate companies' possible challenges and opportunities to use big data analytics as a survival method during and beyond the COVID-19 conditions. Tools: Critical contingencies represent one of the leading business strategies to manage the shift in customer demand and business risks. Big data can be used in this sense as a valuable intangible asset to create these critical contingencies and help a business survive beyond this pandemic. Findings: This study provides policy and practitioner implications in relation to establishing new accounting perspectives on big data analytics in the context of achieving economic sustainability for corporations during and beyond the COVID-19 pandemic. It offers new forms of trust, accountability and governance practices to integrate big data into accounting practices to create more competitive intelligence for businesses. Contribution: This study extends previous accounting literature by offering unique insights and critical developmental thoughts about accounting perspectives of big data applications, opportunities and challenges beyond the COVID-19 pandemic. It provides a critical understanding of the predictive analytics of big data from an accounting perspective as an intangible asset

Research on the Construction of University Campus Economic Management System Based on the Concept of Big Data

At present, in order to adapt to the development of the era of big data, more and more industries use the concept of big data, and the economic and financial management system is no exception. In the context of the concept of big data, the campus economy has also ushered in new opportunities and challenges. Combined with the basic knowledge of big data, this paper makes a systematic research on industrial economic information analysis, which will contribute to the application practice of industrial big data. Using the value of big data will promote revolutionary changes in the work of the school’s economic management department. Taking this as the starting point, this paper systematically analyzes the analysis methods of campus economic information under the background of big data and its role in macro decision-making.

Continuous multi-query optimization for subgraph matching over dynamic graphs

There is a growing need to perform real-time analytics on dynamic graphs in order to deliver the values of big data to users. An important problem from such applications is continuously identifying and monitoring critical patterns when fine-grained updates at a high velocity occur on the graphs. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we study the problem of continuous multi-query optimization for subgraph matching over dynamic graph data. (1) We propose annotated query graph, which is obtained by merging the multi-queries into one. (2) Based on the annotated query, we employ a concise auxiliary data structure to represent partial solutions in a compact form. (3) In addition, we propose an efficient maintenance strategy to detect the affected queries for each update and report corresponding matches in one pass. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach and confirm a two orders of magnitude improvement of the proposed solution.

Research on evaluation system of ideological and political education of college students based on decision system

Nowadays, the ideological and political (IAP) training of the college and university students is of a great interest for everyone due to the rapid change in the political issues and the race of super power. By analyzing the present states and research on the system of evaluating the IAP training of college students, it is obvious that the current evaluation systems are not satisfactory and have different deficiencies such as openness, transparency, and lack of supervision. The existing systems are pretty straightforward, and the assessment process is not open enough. The evaluation systems are not solid, and there are still many challenges for the evaluation system of IAP training of college students. Students can only accomplish a comparably fair and scientific assessment result by building an efficient and innovative decision-making system with the help of analyzing the evaluation system of the IAP education of the students. This allows the students to get to know each other more closely and better promote their overall growth. In this research work, we examined the present assessment systems of IAP education and presented a paper in order to emphasize its shortcomings and flaws such as openness, transparency, the lack of a distinct evaluation system, and supervision. Further, we examined the values of big data analytics in assessing college students’ IAP education and present a new evaluation method based on big data analytics. The new approach assures the results transparency and openness, and hence, the evaluation system is assessed more accurately. Based on the experimental and evaluation results, it is recommended that the college teachers should understand the value of big data, and a separate platform needs to be developed through which different institutions may exchange data about the assessment system.

Monitoring and Evaluating College Students' Mental Health Based on Big Data Analysis

Objectives: Our aim was to overcome the low evaluation accuracy of traditional random sampling methods for college students' mental health, and to use the values of big data of college students' social network behaviors in the prediction and evaluation of their mental health. Methods: We monitored and evaluated college students' mental health through big data analysis. After generating the samples of college students' social network behaviors, a mental health monitoring and evaluation model was established based on a support vector machine (SVM) and decision tree (DT). Then, the DT model was pruned, and input data of the model were optimized by genetic algorithm (GA). Results: The optimal parameter combination was derived for our model. The maximum number of iterations was 60; the smallest number of samples needed for reclassifying internal nodes was 6; the number of samples with the fewest leaf nodes was 30. The mental health scores of most students fell in the interval [0, 6] for unobvious symptoms of mental crisis. The binary classification results of several models were as follows. On anxiety, all models surpassed the accuracy of 60%, except the traditional SVM. The optimal model, ie, Model 5, achieved an accuracy of 86.7%. On depression, all models exceeded the accuracy of 60%, and the GA-optimized DT 5 realized an accuracy as high as 83.1%. On drooping spirit, the optimal model, ie, GA-optimized DT 5, reached an accuracy of 89.5%, which is comparable to that of the GA-optimized SVM 4. Conclusions: The characteristic dimensions extracted by GA are representative. The primary mental states of college students can be estimated quickly and accurately by our model with a low cost of data storage, through the feature analysis of social network behaviors.

Editorial: Perspectives on the value of Big Data sharing

Editorial: Perspectives on the value of Big Data sharing

Towards a Conceptual Approach of Analytical Engineering for Big Data

Analytics corresponds to a relevant and challenging phase of Big Data. The generation of knowledge from extensive data sets (petabyte era) of varying types, occurring at a speed able to serve decision makers, is practiced using multiple areas of knowledge, such as computing, statistics, data mining, among others. In the Big Data domain, Analytics is also considered as a process capable of adding value to the organizations. Besides the demonstration of value, Analytics should also consider operational tools and models to support decision making. To adding value, Analytics is also presented as part of some Big Data value chains, such the Information Value Chain presented by NIST among others, which are detailed in this article. As well, some maturity models are presented, since they represent important structures to favor continuous implementation of Analytics for Big Data, using specific technologies, techniques and methods. Hence, through an in-depth research, using specific literature references and use cases, we seeks to outline an approach to determine the Analytical Engineering for Big Data Analytics considering four pillars: Data, Models, Tools and People; and three process groups: Acquisition, Retention and Revision; in order to make feasible and to define an organization, possibly designated as an Analytics Organization, responsible for generating knowledge from the data in the field of Big Data Analytics.

Smart big data framework for insight discovery

Big Data deal with new challenges such as data variety, data veracity (correct, incorrect, misleading, etc.) and data completeness (provide a single part of the overall information.). In fact, the knowledge discovered from a single source that can offer incorrect or incomplete data, may have a negative impact on the quality of decisions based on it. Therefore, integrating data coming from multiple sources allows verifying the veracity and ensuring the completeness of the results and thus improving the quality of analysis and enhancing business decisions. In this paper, we present a smart framework that falls within the Big Data value chain process and aims to improve the quality of analytical results by focusing two main concerns regarding Big Data Integration; data completeness and data veracity. The framework integrates Big Data in order to build a complete global and correct insight from heterogeneous sources. The paper presents two implementations of the framework in the context of urban and highway traffic management systems.

Value and organisational implications of big data and advanced analytics in healthcare: a case survey analysis

Despite the possibilities of big data and advanced analytics in healthcare, very few organisations have been able to exploit its potential. One of the key reasons is inadequate practical evidence on the clinical and business impact of advanced analytics. To address the issue, this study proposes an advanced analytics healthcare value creation model. The model suggests that well-defined objectives for utilising the technology and readiness of big data analytics capability (BDAC) are rudimentary for organisations' performance enhancement. It establishes four dimensions of robust BDAC - BDA infrastructure, personnel, management and relational BDA capability. In order to test the theoretical model, case survey method was employed. Data from 254 case studies was used to verify its validity. The findings from case synthesis confirm the need of strategic intent and strong BDAC for effective implementation. The results also illuminate the implications of BDA in terms of improved clinical, research and business value.

The mediating role of supply chain management on the relationship between big data and supply chain performance using SCOR model

Adopting the Supply Chain Operations Reference (SCOR) model, this study aims at investigating the impact of big data (volume, velocity, variety, veracity, and value) on supply chain performance through the mediating role of supply chain management (plan, source, make, deliver, and return) assuming four hypotheses. Data were collected using a questionnaire from managers of food processing companies. The results showed that big data affected supply chain management significantly and positively, which in turn affected supply chain performance significantly and positively. In addition, big data exerted a significant and positive impact on supply chain performance. Based on these links, it was found that supply chain management mediated significantly the effect of big data on supply chain performance. The study contributes to the literature showing that big data plays a pivotal role in improving supply chain performance and supply chain performance from the SCOR model perspective is critical for the relationship between these two constructs.

Review on Intelligent Governance of Physical Fitness of College Students under the Background of Big Data

The advent of the era of big data has promoted the reform of sports governance, and intelligent governance is becoming a strategic choice and a new trend to promote the physical fitness development of college students. Based on the profound analysis of the current situation of college students’ physical fitness, this study examines the great value of big data for the intelligent governance of physical fitness, and further constructs the model to achieve intelligent governance of college students’ physical fitness. The study also provides specific strategies to promote this process. According to the study, although the current situation of college students’ physical fitness is still not optimistic, data‐driven intelligent governance of physical fitness provides a new solution to a personalized, refined, and diversified way of governance. By creating an intelligent governance concept with data thinking, building an intelligent governance platform supported by data, and by such measures as improving the data literacy of physical education teachers in colleges and universities and upgrading stadiums with intelligent techniques, the study seeks to break through the bottleneck of college students’ physical fitness governance and open up a new path to realize the effective governance of college students’ physical fitness.

A Novel Approach for Deciphering Big Data Value Using Dark Data

The last decade has seen a rapid increase in big data, which has led to a need for more tools that can help organizations in their data management and decision making. Business intelligence tools have removed many of the obstacles to data visibility, and numerous data mining technologies are playing an essential role in this visibility. However, the increase in big data has also led to an increase in ‘dark data’, data that does not have any predefined structure and is not generated intentionally. In this paper, we show how dark data can be mined for practical purposes and utilized to gain business insight. The most common type of dark data is a log file generated on a web server. Using the example of log files generated by e-commerce transactions, this paper shows how residual data and data trails can prove to be valuable when an actual dataset is inaccessible, and explains the usage of residual data for modeling purposes. The work uses a system identification approach, based on natural language processing for log file tokenization and feature extraction. The features are then embedded into the next step, which uses a deep neural network to identify customers for targeted advertising. The results achieve a significant accuracy and show how dark data has the potential to deliver value for business. Locating, organizing, and understanding dark data can unlock its relevance, usefulness, and potential monetization, but it is important to act when the benefits of use outweigh the costs of access and analysis.

Creating value from Big Data: a knowledge assets-based view

ABSTRACT The attention of the academic and professional world to the potential benefits of Big Data is growing, as well as the awareness that they can represent fundamental drivers of organisational value creation. Indeed, Big Data is a critical intangible resource and source of value for organisations that can support the achievement of superior performance. Understanding the value of Big Data and how it contributes to value creation mechanisms defines an important area of research that needs to be further developed. The paper analyses the links between Big Data and organisational knowledge assets and proposes a framework to explain how Big Data contributes to organisational value creation mechanisms. It also highlights the role of knowledge assets as factors that influence the use, development and deployment of Big Data for organisational value creation dynamics.

Research on Service Innovation of Financial Data based on Cloud Computing in Big Data Era

With the increasing penetration of the Internet into people's lives, the financial market in the era of big data directly affects the asymmetric information mode between the supply and demand sides, and on this basis, it plays a positive role in guiding the subsequent optimization of financial market services, ensuring social fairness and reducing the exclusion of financial products. Big data finance under the Internet has narrowed the distance between supply and demand, subverted the problem of information asymmetry between the two sides, and injected new innovative means to alleviate financial exclusion, promote social equity and optimize financial services. In order to support the development of the real economy and enhance the core competitiveness of the industry, the process of data value chain has been continuously extended horizontally and vertically, but the data value chain of financial enterprises has been extended. In this paper, starting from the characteristics of the big data era, taking the carrier of value extension as the research goal, the big data value chain model of financial enterprises is constructed based on cloud computing technology, and the service innovation of China's financial data in the big data era is discussed.

Heterogeneous Analysis of the Nexus between Big Data Analytics and Value Co-Creation: Insight from Selected Service Businesses in Ghana

The study examines big datas role analytics ininterfirm value co-creation in selected service businesses in Ghana by focusing specifically on the heterogenous big data characteristics. This is in response to the ongoing debate on value co-creation and big data nexus. Using data from 61 purposively sampled service businesses in Accra-Ghana, the structural equation model provides that; big data analytics positively influences interfirm value co-creation in the selected businesses. Further, aside from big data volume, all the other big data characteristics (velocity, veracity, value, and variety) also individually influence value co-creation. Therefore, this challenges businesses of all sizes to invest in the application of big data for strategic decisions like value co-creation. Again, although businesses should be willing to collaborate with others, big data teams should be fully equipped for optimal analytics.