In the paper the phenomenon of big data is presented. I pay my special attention to the relation of this phenomenon to research work in experimental sciences. I search for answers to two questions. First, do the research methods proposed within the paradigm big data can be applied in experimental sciences? Second, does applying the research methods subject to the big data paradigm lead, in consequence, to a new understanding of science?

A common observation of everyday life reveals the growing importance of data science methods, which are increasingly more and more important part of the mainstream of knowledge generation process. Digital technologies and their potential for data collection and data processing have initiated the birth of the fourth paradigm of science, based on Big Data. Key to these transformations is datafication and data mining that allow the discovery of knowledge from contaminated data. The main purpose of the considerations presented here is to describe the phenomena that make up these processes and indicate their possible epistemological consequences. It has been assumed that increasing datafication tendencies may result in the formation of a data- centric perception of all aspects of reality, making data and the methods of their processing a kind of higher instance shaping human thinking about the world. This research is theoretical in nature. Such issues as the process of datafication and data science have been analyzed with a focus on the areas that raise doubts about the validity of this form of cognition.

Lifetime biographical and publication histories of 2,326 full professors were examined. A combination of administrative, biographical, and bibliometric data was used. Retrospectively constructed productivity, promotion age and speed classes were examined. About 50% of current top productive professors have been top productive throughout their academic careers, over 30–40 years. Topto- bottom and bottom-to-top transitions in productivity classes over academic careers are very rare. We used prestige-normalized productivity in which more weight is given to articles in high-impact than in low-impact journals, recognizing the highly stratified nature of academic science. The combination of biographical and demographic data with raw Scopus publication data from the past 50 years (N = 935,167 articles) made it possible to assign all full professors retrospectively to different productivity, promotion age, and promotion speed classes. In logistic regression models, there were two powerful predictors of belonging to the Top productivity class for full professors: being highly productive as associate professor and as assistant professor (increasing the odds by 180% and 360%). Neither gender nor age (biological or academic) emerged as statistically significant. Our findings have important implications for hiring policies as scientists stay in Polish academia usually for several decades.

In this paper we analyze the phenomenon of quitting academic science and show how quitting differs between men and women, academic disciplines and over time. The approach presented is comprehensive: global, based on cohorts of scientists, and longitudinal – we observe the publication activity of individual scientists over time. Using metadata from Scopus, a global bibliometric database of publications and citations, we analyze the publication careers of scientists from 38 OECD countries who began publishing in 2000 ( N = 142 776) and 2010 ( N = 232 843). The paper tests the usefulness of large bibliometric datasets for a global analysis of academic careers.

Wikipedia, one of the world’s most popular websites, owes its success to its authors – i.e. to all of us. But how do we know if the information it offers is reliable?

Mathematics offers tools renowned for their objectivity, which is a cornerstone of scientific inquiry. Yet the question arises: how accurately do statistical methods really reflect the complexities of the real world?

This research analyzes factors affecting the scientific success of
central bankers. We combine data from the RePEc and EDIRC databases,
which contain information about economic publications of authors from
182 central banks. We construct a dataset containing information about
3312 authors and almost 80,000 scientific papers published between 1965
and 2020. The results from Poisson regressions of citation impact
measure (called the h-index) on a number of research features
suggest that economists from the U.S. Federal Reserve Banks,
international financial institutions, and some eurozone central banks
are cited more frequently than economists with similar characteristics
from central banks located in emerging markets. Researchers from some
big emerging economies like Russia or Indonesia are cited particularly
infrequently by the scientific community. Beyond these outcomes, we
identify a significant positive relationship between research networking
and publication success. Moreover, economists cooperating with highly
cited scientists also obtain a high number of citations even after
controlling for the size of their research networks.

Power big data contains a lot of information related to equipment fault. The analysis and processing of power big data can realize fault diagnosis. This study mainly analyzed the application of association rules in power big data processing. Firstly, the association rules and the Apriori algorithm were introduced. Then, aiming at the shortage of the Apriori algorithm, an IM-Apriori algorithm was designed, and a simulation experiment was carried out. The results showed that the IM-Apriori algorithm had a significant advantage over the Apriori algorithm in the running time. When the number of transactions was 100 000, the running of the IM-Apriori algorithm was 38.42% faster than that of the Apriori algorithm. The IM-Apriori algorithm was little affected by the value of supportmin. Compared with the Extreme Learning Machine (ELM), the IM-Apriori algorithm had better accuracy. The experimental results show the effectiveness of the IM-Apriori algorithm in fault diagnosis, and it can be further promoted and applied in power grid equipment.

With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data. Remote sensing data clear showing the characteristics of Big Data such as hyper spectral, high spatial resolution, and high time resolution, thus, resulting in a significant increase in the volume, variety, velocity and veracity of data.This paper proposes a feature supporting, salable, and efficient data cube for timeseries analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change. In this system, the feature data cube building and distributed executor engine are critical in supporting large spatiotemporal RS data analysis with spatial features. The feature translation ensures that the geographic object can be combined with satellite data to build a feature data cube for analysis. Constructing a distributed executed engine based on dask ensures the efficient analysis of large-scale RS data. This work could provide a convenient and efficient multidimensional data services for many remote sens-ing applications.

W obliczu rewolucji technologii informatycznych badacze nauk społecznych mają przed sobą nie lada wyzwanie. Oto bowiem wraz ze zwiększającą się popularnością Internetu pojawiły się ogromne ilości danych zawierających opinie, poglądy i zainteresowania jego użytkowników. Chociaż analiza tych danych stawia przed badaczami poważne problemy metodologiczne, za ich użyciem przemawia fascynujący materiał powstający bez ingerencji badaczy. Dużą część tego materiału stanowią dane z najpopularniejszej na świecie wyszukiwarki Google. Co minutę jej użytkownicy ze wszystkich miejsc na świecie zadają ponad 3 miliony zapytań, które są następnie klasyfikowane i udostępniane za pomocą aktualizowanych na bieżąco narzędzi. W artykule tym omówione są próby adaptacji tych danych do potrzeb nauk społecznych, a także dotychczasowe badania na ten temat. Omówione są także praktyczne aspekty pracy z narzędziami Google’a: Google Trends oraz Google Keyword Planner. Artykuł jest przeznaczony przede wszystkim dla badaczy nauk społecznych zainteresowanych internetowymi źródłami Big Data oraz wykorzystaniem tych danych w pracy naukowej.

With the increasing demand of customisation and high-quality products, it is necessary for

the industries to digitize the processes. Introduction of computers and Internet of things

(IoT) devices, the processes are getting evolved and real time monitoring is got easier.

With better monitoring of the processes, accurate results are being produced and accurate

losses are being identified which in turn helps increasing the productivity. This introduction

of computers and interaction as machines and computers is the latest industrial revolution

known as Industry 4.0, where the organisation has the total control over the entire value chain

of the life cycle of products. But it still remains a mere idea but an achievable one where IoT,

big data, smart manufacturing and cloud-based manufacturing plays an important role. The

difference between 3rd industrial revolution and 4th industrial revolution is that, Industry

4.0 also integrates human in the manufacturing process. The paper discusses about the

different ways to implement the concept and the tools to be used to do the same.

The application of churn prevention represents an important step for mobile communication

companies aiming at increasing customer loyalty. In a machine learning perspective,

Customer Value Management departments require automated methods and processes to

create marketing campaigns able to identify the most appropriate churn prevention approach.

Moving towards a big data-driven environment, a deeper understanding of data

provided by churn processes and client operations is needed. In this context, a procedure

aiming at reducing the number of churners by planning a customized marketing campaign

is deployed through a data-driven approach. Decision Tree methodology is applied to drow

up a list of clients with churn propensity: in this way, customer analysis is detailed, as well

as the development of a marketing campaign, integrating the individual churn model with

viral churn perspective. The first step of the proposed procedure requires the evaluation of

churn probability for each customer, based on the influence of his social links. Then, the

customer profiling is performed considering (a) individual variables, (b) variables describing

customer-company interactions, (c) external variables. The main contribution of this work

is the development of a versatile procedure for viral churn prevention, applying Decision

Tree techniques in the telecommunication sector, and integrating a direct campaign from

the Customer Value Management marketing department to each customer with significant

churn risk. A case study of a mobile communication company is also presented to explain

the proposed procedure, as well as to analyze its real performance and results.

Aiming at the problems of low accuracy, low efficiency and low stability of traditional methods and recent developments in advanced technology incite the industries to be in sync with modern technology. With respect to various available techniques, this paper designs a fuzzy comprehensive evaluation model of the manufacturing industry for transferring risk based on economic big-data analytics. The big-data analysis method is utilized to obtain the data source of fuzzy evaluation of the manufacturing industry to transfer risk using data as the basis of risk evaluation. Based on the risk factors, the proposed model establishes the risk index system of the manufacturing industry and uses the expert evaluation method to design the scoring method of the evaluation index system. To ensure the accuracy of the evaluation results, the manufacturing industry's fuzzy comprehensive model is established using the entropy weight method, and the expert evaluation results are modified accordingly. The experimental results show that the highest efficiency of the proposed method is 96%, the highest accuracy of the evaluation result is 75%. The evaluation result's stability is higher than the other existing methods, which fully verifies the effectiveness and can provide a reliable theoretical basis for enterprise risk evaluation research.

The road network development programme, as well as planning and design of transport systems of cities and agglomerations require complex analyses and traffic forecasts. It particularly applies to higher-class roads (motorways and expressways), which in urban areas, support different types of traffic. Usually there is a conflict between the needs of long-distance traffic, in the interest of which higher-class roads run through undeveloped areas, and the needs of bringing such road closer to potential destinations, cities [1]. By recognising the importance of this problem it is necessary to develop the research and methodology of traffic analysis, especially trip models. The current experience shows that agglomeration models are usually simplified in comparison to large city models, what results from misunderstanding of the significance of these movements for the entire model functioning, or the lack of input data. The article presents the INMOP 3 research project results, within the framework of which it was attempted to increase the accuracy of traffic generation in agglomeration model owing to the use of BigData – the mobile operator’s data on SIM card movements in the Warsaw agglomeration.

Modern IT and telecommunications technologies create new possibilities of data acquisition for the needs of traffic analyses and transport planning. At the same time, the current experience suggests that it is becoming increasingly difficult to obtain data on interurban travels of people in a traditional way (among others, in Poland there has been no comprehensive survey of drivers on the sections of non-urban roads since 2006). Within the framework of the INMOP 3 research project, an attempt was made to analyse the use of the Big Data application possibilities including data from SIM cards of the mobile telephony operator [1] and data from probe vehicle data (also known as “floating car data”), as data sources for carrying out the traffic analyses and modelling of travels by all means of transport in Poland. The article presents the manner, in which the data were used, as well as methodological recommendations for creating transport models at the national, regional and local levels. Especially the results of work can be applied for systematic passenger cars trip matrix update

Lean thinking and Industry 4.0 have been broadly investigated in recent years in intelligent manufacturing. Lean Production is still one of the most efficient industrial solutions in business and research, despite being implemented for a long time. On the other hand, Industry 4.0 has been introduced referring to the fourth industrial revolution. This study aims to analyze the combination of both Industry 4.0 and Lean production practices through a systematic literature review from a Lean Automation perspective. In this field, 189 articles are examined using VOSviewer for cluster analysis. Then, a more detailed analysis is provided to explore how Industry 4.0 and Lean techniques are integrated from a practical perspective. Results highlighted Big Data Analysis and Value Stream Mapping as the most common techniques, also emphasizing a growing trend toward new publications. Nevertheless, few practical applications are identified in the literature highlighting six gaps in the correlation of LA practices.

Recent rapid developments in information and network technology have profoundly influenced manufacturing research and its application. However, the product’s functionality and complexity of the manufacturing environments are intensifying, and organizations need to sustain the advantage of huge competitiveness in the markets. Hence, collaborative manufacturing, along with computer-based distributed management, is essential to enable effective decisions and to increase the market. A comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework and to shed light on the future research avenues. In this review paper, the use of technology and management by means of collaborative and cloud manufacturing process and big data in networked manufacturing system have been discussed. A systematic review of research papers is done to draw conclusion and moreover, future research opportunities for collaborative manufacturing system were highlighted and discussed so that manufacturing enterprises can take maximum benefit.