Concept Of Big Data Research Articles

Big data method and its application in innovation education research

Big data technology is a new stage of information development. In recent years, it has been widely used in many fields, especially in social science research. This paper analyzes the development status and significance of the combination of big data technology and social science research, on the basis of summarizing and combing the concept of big data and its important role. Taking the application of big data method in the research of innovation education as an example, this paper makes a series of visualization analysis with Citespace software on the related literature with the theme of “big data and innovation education” collected by CNKI, such as annual analysis, literature source analysis, co-occurrence analysis of authors, organization analysis, keyword clustering analysis and keyword timing analysis. This paper also draws the corresponding knowledge mapping, clarifies its research status, hot spots and development trend, and provides scientific basis for the research of innovation education. Thus the paper believes that the research on big data and innovation education needs to strengthen interdisciplinary communication and cooperation, refine and deepen the research theme and content.

Big Data as a tool helpful in communication management

The boundaries between the online and offline worlds have become irretrievably blurred, especially as mobile devices have proliferated. As a result, more and more activities are transferred to the Internet. Every activity in the network leaves a trace, which is why the volume of available data is growing rapidly. The amount of increasing information affects all market participants, and the necessity to constantly collect and process large amounts of data becomes an everyday reality. The aim of the article is to present the concept of big data and to indicate examples of the use of big data to manage marketing communication with the environment. It should be emphasized that not only data transfer devices, but also human interaction contribute to the creation of very large data sets. Acquiring and correctly interpreting them plays an important role in market entities in terms of management, including communication management. Contemporary multi-directional communication, including communication in a hypermedia environment, creates new challenges and threats. The article was prepared based on a literature review, research reports and an analysis of secondary sources. It also outlines the practical implications. The considerations provided are the basis for further activities and empirical research.

Big data concept implementation for energy consumption

Optimizarea producției, distribuției și consumului de energie constituie una dintre preocupările majore ale colectivităților, fiind considerată ca unul dintre principalele beneficii ale prelucrării datelor, cu scopul de a permite o mai bună orientare a acțiunilor și o mai bună gestionare a informațiilor disponibile. Colectivitățile se orientează mai ales spre gestionarea cererii de energie, măsura eficienței […]

AHP BASED SELECTION OF THIRD-PARTY LOGISTICS SERVICE PROVIDER

Today, as in all other sectors, competition in the logistics sector is getting harder day by day. In order to be successful, firms should be able to take the right decisions and fast as they can to be able to catch changing competition conditions. The first stage of making the right decisions is to reach the correct and necessary data quickly. Although the concept of big data, which is one of the innovations brought by the Industry 4.0 revolution, has significantly facilitated access to information, it is still considered an important challenge to analyze this data obtained by companies during daily business processes in a meaningful way. Like many other sectors, the import / export sector has difficulties in the selection process of third party logistics companies in outsourcing activities. Hence, the purpose of this study is to decide the criteria that import export companies should pay attention while choosing a 3rd party logistics company and to determine the priority and importance of these criteria. Both qualitative and quantitative methods used in the study, in the criteria selection phase, one of the directors of Turkey's leading import / export company’s was interviewed, in the second phase of the study, obtained criteria were prioritized using the AHP methodology. As a result of the study, a total of 16 criteria, 4 main and 12 sub-criteria were determined, and cost was determined as the most important main criterion in 3rd party logistics company selection process.

Big Data and Open Data Analytics: The Applications and the Path Taken in Turkey

Having data is becoming more and more important for companies and countries. This is because data is very useful for generating added value and providing transparent government policies. The fact that the data sources are diverse today and the data flow is continuous and very fast also brings the data to be very large and too complex to be processed. In this study, the concepts of big data and open data are introduced technically and the importance and gains of open data that have social and economic effects are mentioned with international examples. In addition, topics covered in data analysis and the use of machine learning in data analysis are given. Following the introduction of services provided using data analysis in Turkey, the study is completed presenting data portals and government policies of Turkey and obstacles and problems hindering the development of open data in Turkey.

Research on Data Analysis of Chinese Public Accounting Firms in the Big Data Era

The rapid development of computer and Internet technology has brought human society into the era of information technology, and it has a profound impact on human’s past work, production, management and consumption methods (AICPA, 2014). Even the social economic structure has changed in this general environment. With the rapid development of information technology, tradition has been unable to meet current needs. By combining the concept of big data and the characteristics of audit work in the era of big data, this article explores the effective countermeasures for optimizing the data analysis in the era of big data (Richins et al., 2017).

A Review on Big Data Optimization Techniques

Abstract Analysis of representative tools for SQL query processing on Hadoop (SQL-on-Hadoop systems), such as Hive, Impala, Presto, Shark, show that they are not still sufficiently efficient for complex analytical queries and interactive query processing. Existing SQL-on-Hadoop systems have many benefits from the application of modern query processing techniques that have been studied extensively for many years in the database community. It is expected that with the application of advanced techniques, the performance of SQL-on-Hadoop systems can be improved. The main idea of this paper is to give a review of big data concepts and technologies, and summarize big data optimization techniques that can be used for improving performance when processing big data.

Understanding the Concept of BIG Data Testing, Strategies and Methods

This paper explains the concept of BIG data and the process of testing BIG data technology. There are various procedures which are used to perform testing of BIG data technology. It is to make sure that all the functions of BIG data work smoothly without getting errors. This paper will explain the concept and strategies used to perform the BIG data testing.

Optimization of real-time wireless sensor based big data with deep autoencodernetwork: a tourism sector application with distributed computing

Internet usage has increased rapidly with the development of information communication technologies. The increase in internet usage led to the growth of data volumes on the internet and the emergence of the big data concept. Therefore, it has become even more important to analyze the data and make it meaningful. In this study, 690 million queries and approximately 5.9 quadrillion data collected daily from different servers were recorded on the Redis servers by using real-time big data analysis method and load balance structure for a company operating in the tourism sector. Here, wireless networks were used as a triggering factor to gather data from visitors of the hotels and the analysis was supportedwithanoptimizationapproachthroughthedeepautoencodernetwork. Accordingtothedatadensitygathered from the structure developed with distributed computing and the API software in C# language, server group numbers were increased to list the most affordable hotel in the desired times. Thanks to the developed architecture and software, response times of the servers were significantly reduced. In detail, it was seen that the HAProxy responded 11 times faster than NetScaler as the new architecture responded 1160 times faster than the old one. Also, the HashSet system in the newly developed architecture responded 18 times faster than the List system and as general, the new architecture was found to be 9 times faster than the old architecture.

A Review of Data Science and Big Data Computing

Data Science emerged as an important discipline and its education is essential for success in almost every aspect of life. Here comes the age of Big data. Big data impacts all aspects of our lives and society is admitting it. Data processing and other techniques are combined to convert abundant data into valuable information for society, organizations, and people. Specific strategies and approaches are needed to provide better to educate future data scientists to overcome the challenges of Big data. In this paper, we discussed the general concept of data science, Big data, and areas of Big data computing.

Airport capacity evaluation based on air traffic activities big data

In order to evaluate the airport's comprehensive service capabilities, this paper considers the impact of air quality and noise on the airport environment under the big data of air traffic activities. In this study, the concept of environmental traffic capacity and big data are applied to the air traffic field. Recently, the airport air and noise pollution has been widely investigated and has become one of the major concerns of the potentially exposed people. This study explores the usage of governmental ambient air quality and noise standards to evaluate the airport operation capacities in the context of the era of big data. The first step is to analyze the typical airport operation scenario as the evaluation scenario. The second step is to use the air and noise emission assessment model for calculating the airport maximum air pollutant concentration and noise level. The final step is to establish a complete airport environment traffic capacity (AETC) evaluation process. As a case study, the capacity evaluation of Nanjing Lukou international airport (NKG) is performed using the above steps. In this case, significant associations between the pollutant concentrations/noise level and the air traffic volume were observed. The AETC of NKG was calculated with the established evaluation process successfully. The results show that the NKG maximum hourly air traffic volume is 120, daily air traffic volume is 770, and annual air traffic volume is 365,805, meeting the China Ambient Air Quality and Noise Standards. Although different air pollutants were investigated in this research, only the NOx was found to be the species that approaching the China governmental standards in this case. Thus, the airport NOx concentration was selected as the AETC limitation factor.

Challenges and Trends for the Incorporation of Big Data in the Accounting Profession: From the Traditional Approach to the Future Professional Accountant

In the present article we analyse the usage of the Big Data techniques in the accounting profession, the authors’ findings revealing that this system is not as widely used by professional accountants as in other related fields. Firstly, we present modern data techniques to promote the understanding of their application potential highlighting the need for the accounting profession to head towards Big Data and financial review methods. The modern engagements in the accounting profession often imply the review of the clients using Big Data in order to remain competitive and relevant in the current business environment. Thus, the theoretical part is supplemented by a case study regarding the level of knowledge concerning the Big Data concept among professional accountants. In a Big Data environment, the professional accountant has the potential to perform more advanced projection analysis. Lastly, we present a series of conclusions that we hope will contribute to the literature, because professional accountants are interested in this field.

Digital transformation in oil and gas companies - A case study of Bien Dong POC

The fourth industrial revolution (Industry 4.0) with the breakthrough of internet and artificial intelligence has had a strong impact, changing all aspects of global socio-economic life. Digital transformation in the spread of Industry 4.0 is no longer a choice but has become an inevitable development trend for businesses to truly stand up to the times. Digital transformation is the transformation of business activities, processes, products, and models to fully leverage the opportunities of digital technologies, characterised by development, growth, innovation, and disruption. In particular, "digital disruption" is the situation when new technology competes with the traditional business way that we now often refer to under the concepts of cloud computing, big data, and internet of things (IoT). This competition will help businesses utilise digitised data and processes to create a new model that is more efficient and convenient. Digital technologies in oil and gas companies can have a significant business impact as it contributes to increasing hydrocarbon recovery, ensuring safety across the business ecosystem, and improving operational reliability. This paper addresses the oil and gas industry’s trends in digital transformation and the initiatives at Bien Dong POC.

Towards Implementation of Big Data Concepts in a Pharmaceutical Company

AbstractNowadays, big data is a widely utilized concept that has been spreading quickly in almost every domain. For pharmaceutical companies, using this concept is a challenging task because of the permanent pressure and business demands created through the legal requirements, research demands and standardization that have to be adopted. These legal and standards’ demands are associated with human healthcare safety and drug control that demands continuous and deep data analysis. Companies update their procedures to the particular laws, standards, market demands and regulations all the time by using contemporary information technology. This paper highlights some important aspects of the experience and change methodology used in one Macedonian pharmaceutical company, which has employed information technology solutions that successfully tackle legal and business pressures when dealing with a large amount of data. We used a holistic view and deliverables analysis methodology to gain top-down insights into the possibilities of big data analytics. Also, structured interviews with the company’s managers were used for information collection and proactive methodology with workshops was used in data integration toward the implementation of big data concepts. The paper emphasizes the information and knowledge used in this domain to improve awareness for the needs of big data analysis to achieve a competitive advantage. The main results are focused on systematizing the whole company’s data, information and knowledge and propose a solution that integrates big data to support managers’ decision-making processes.

Big Data Analytics and Its Role to Support Groundwater Management in the Southern African Development Community

Big data analytics (BDA) is a novel concept focusing on leveraging large volumes of heterogeneous data through advanced analytics to drive information discovery. This paper aims to highlight the potential role BDA can play to improve groundwater management in the Southern African Development Community (SADC) region in Africa. Through a review of the literature, this paper defines the concepts of big data, big data sources in groundwater, big data analytics, big data platforms and framework and how they can be used to support groundwater management in the SADC region. BDA may support groundwater management in SADC region by filling in data gaps and transforming these data into useful information. In recent times, machine learning and artificial intelligence have stood out as a novel tool for data-driven modeling. Managing big data from collection to information delivery requires critical application of selected tools, techniques and methods. Hence, in this paper we present a conceptual framework that can be used to manage the implementation of BDA in a groundwater management context. Then, we highlight challenges limiting the application of BDA which included technological constraints and institutional barriers. In conclusion, the paper shows that sufficient big data exist in groundwater domain and that BDA exists to be used in groundwater sciences thereby providing the basis to further explore data-driven sciences in groundwater management.

Analysis of Network Security of Port of Port Issues in the Era of Big Data

Lou, J.L., 2020. Analysis of network security of port of port issues in the era of big data. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 223–226. Coconut Creek (Florida), ISSN 0749-0208.In the era of big data, the network has provided convenience and also brought potential threats. Viruses, Trojan horses, and hackers' attacks have caused irretrievable losses for people, and they have become a major obstacle for the expansion of internet. This paper first analyzes the related concepts of big data and the current situation of network security of port of port in the era of big data, then uses the five indicators of network security of port of port to establish a network security of port of port evaluation model, and finally puts forward the countermeasures for ensuring information security of computer networks in the era of big data.

Data-Driven Molecular Dynamics: A Multifaceted Challenge.

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

Research on the Integration of Urban Traffic and Big Data

The powerful data processing ability of big data technology can allocate traffic resources more efficiently, and can deal with various sudden traffic problems flexibly. It is an unprecedented opportunity and challenge for urban transportation and smart cities to effectively collect and utilize traffic big data to meet the application requirements of high timeliness traffic administrative supervision, traffic enterprise management and traffic citizen service. This article expounds the concept and characteristics of big data, discusses the application research of big data in urban transportation at home and abroad in recent years, summarizes its application research scope and trend, points out that intelligent transportation is the focus of the application of big data in urban transportation, and finally looks forward to the future research direction.

Cerebral Approach to Track Malfunction of Assets in a Smart Power Grid Information System

Failure of assets at downstream level of Smart Power Grid being barely addressed, subsequently hoarding lots of superfluous human hours, depletion of valuable time and resources, inefficient and untimely rectification of problems leading to concerns of inefficient operations and hefty loss to the power grid organization in all its aspects. There is a need to address such failures in advance, through the use of prevailing information technological inclusion, implicating efficient operations benefiting the entire sector. With Knowledge Management Systems evolving at its growing needs to address critical issues and providing feasible solutions to the digitization of information already at high demand, we propose an analytical approach merged with the concepts of big data, to track historical data of assets and resources, to identify the possibilities of failure of assets in smart power grid infrastructure. The proposed system would identify the possible failure based on the past records of an assets performance and failures, not limited to, but also based on the expected life span of the assets. The issues will be archived for further sharing while in apposite needs and its solutions would be appropriately applied throughout the power business and its subsidiary. The data would be efficiently stored and archived in the form of digitized formats such as audio, video, images, comments, documents, feedback and other visually recognizable and multimedia formats. Various technological related electronics and gadget equipment’s such as tablets, mobile phones, laptops and other hand held devices could be easily utilized to support the work force teams towards an agile operation.

Challenges and Opportunities of Big data in Health Care

The term big data is referring to huge amount of structured and unstructured data. The concept of bigdata is based on three V’s that are volume velocity and verity. Health care industry is produced hugeamount of data every day. As all the things and industries become globalized, the health care industryalso becomes digitalized. For the purpose to solve the human being health issue the researchapplies big data technique to analysis the huge amount of data. In this article complete review aboutusing of big data health care industry is presented. The main purpose of this article is to discussabout all the challenges which create hurdle to create opportunities in implementation of big data