Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "big data system" wg kryterium: Temat

  Lista filtrów
Wyrównaj
Wyświetlanie 1-15 z 16
Tytuł:
Enterprise service bus architecture for the big data systems
Autorzy:
Orłowski, C.
Szczerbicki, E.
Grabowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/407119.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
enterprise service bus
service oriented architecture
big data system
smart cities
transformation process
conversion process
Opis:
This paper presents the construction of the enterprise service bus architecture in data processing resources for a big data decision-making system for the City Hall in Gdansk. The first part presents the key processes of bus developing: the installation of developing environment, the database connection, the flow mechanism and data presentation. Developing processes were supported by models: KPI (Key Processes Identifier) and SOP (Simple Operating Procedures) (also connected to the bus). The summary indicates the problems of the bus construction, especially processes of routing, conversion, and handling events.
Źródło:
Management and Production Engineering Review; 2014, 5, 1; 28-31
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid scheduler for many task computing in big data systems
Autorzy:
Vasiliu, L.
Pop, F.
Negru, C.
Mocanu, M.
Cristea, V.
Kolodziej, J.
Powiązania:
https://bibliotekanauki.pl/articles/907647.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
many task computing
scheduling heuristics
QoS
big data system
simulation
obliczenia wielofunkcyjne
szeregowanie zadań
duży zbiór danych
Opis:
With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2017, 27, 2; 385-399
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system
Autorzy:
Prasad, M.
Liu, Y.-T.
Li, D.-L.
Lin, C. -T.
Shah, R. R.
Kaiwartya, O. P.
Powiązania:
https://bibliotekanauki.pl/articles/91743.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
fuzzy interference system
collaborative clustering
fuzzy logic
big data
data visualization
Opis:
A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of TakagiSugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within eachother. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only half or less/more than the half of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show the proposed method performs better than existing methods on some benchmark problems.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 1; 33-46
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Resource optimisation in cloud computing: comparative study of algorithms applied to recommendations in a big data analysis architecture
Autorzy:
Ndayikengurukiye, Aristide
Ez-Zahout, Abderrahmane
Aboubakr, Akou
Charkaoui, Youssef
Fouzia, Omary
Powiązania:
https://bibliotekanauki.pl/articles/2141815.pdf
Data publikacji:
2021
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
cloud computing
Big Data
IoT
recommender system
KNN algorithm
Opis:
Recommender systems (RS) have emerged as a means of providing relevant content to users, whether in social networking, health, education, or elections. Furthermore, with the rapid development of cloud computing, Big Data, and the Internet of Things (IoT), the component of all this is that elections are controlled by open and accountable, neutral, and autonomous election management bodies. The use of technology in voting procedures can make them faster, more efficient, and less susceptible to security breaches. Technology can ensure the security of every vote, better and faster automatic counting and tallying, and much greater accuracy. The election data were combined by different websites and applications. In addition, it was interpreted using many recommendation algorithms such as Machine Learning Algorithms, Vector Representation Algorithms, Latent Factor Model Algorithms, and Neighbourhood Methods and shared with the election management bodies to provide appropriate recommendations. In this paper, we conduct a comparative study of the algorithms applied in the recommendations of Big Data architectures. The results show us that the K-NN model works best with an accuracy of 96%. In addition, we provided the best recommendation system is the hybrid recommendation combined by content-based filtering and collaborative filtering uses similarities between users and items.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 65-75
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data locality in Hadoop
Autorzy:
Kałużka, J.
Napieralska, M.
Romero, O.
Jovanovic, P.
Powiązania:
https://bibliotekanauki.pl/articles/397706.pdf
Data publikacji:
2017
Wydawca:
Politechnika Łódzka. Wydział Mikroelektroniki i Informatyki
Tematy:
distributed file system
big data
Apache Hadoop
HDFS
rozproszony system plików
Opis:
The Apache Hadoop framework is an answer to the market tendencies regarding the need for storing and processing rapidly growing amounts of data, providing a fault-tolerant distributed storage and data processing. Dealing with large volumes of data, Hadoop, and its storage system HDFS (Hadoop Distributed File System), face challenges to keep the high efficiency with computing in a reasonable time. The typical Hadoop implementation transfers computation to the data. However, in the isolated configuration, namenode (playing the role of a master in the cluster) still favours the closer nodes. Basically it means that before the whole task has run, significant delays can be caused by moving single blocks of data closer to the starting datanode. Currently, a Hadoop user does not have influence how the data is distributed across the cluster. This paper presents an innovative functionality to the Hadoop Distributed File System (HDFS) that enables moving data blocks on request within the cluster. Data can be shifted either by a user running the proper HDFS shell command or programmatically by other modules, like an appropriate scheduler.
Źródło:
International Journal of Microelectronics and Computer Science; 2017, 8, 1; 16-20
2080-8755
2353-9607
Pojawia się w:
International Journal of Microelectronics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Small Farms as “Data Producers” for the Needs of Agricultural Management Information System
Autorzy:
Zysk, Elżbieta
Mroczkowski, Tomasz
Dawidowicz, Agnieszka
Powiązania:
https://bibliotekanauki.pl/articles/2105521.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Agriculture Management Information System
agriculture
information technology
big data
precision agriculture
Opis:
In the face of current global threats, including the COVID-19 Pandemic, new technological solutions are needed. Globalization, progressing urbanization, the decreasing availability of cultivable land for food production, water contamination, flood risk and climate change, can all be viewed as potential threats to food safety. According to forecasts and trends, the future of both agricultural policy and agricultural innovation will be based on big data, data analytics and machine learning. Therefore, it is and will continue to be important to develop information systems dedicated to agricultural innovation and the management of food security challenges. The main aim of the study is a classification of data for a uniform AMIS from data from IREIS, GC and AIIS based on survey and expert interview data obtained. We propose to expand the range of data produced by small farmers while keeping in mind the protection of farmers and their rights and the possible benefits of the data provided. The literature recognizes the value of such data but it has not yet been legally regulated, protected, managed and, above all, properly used for agricultural and food security policy purposes. Therefore, we develop the idea of extended farmers’ participation in the production of agricultural activity data. The research used a survey questionnaire and expert interviews. A viable AIIS needs current data that farmers already produce as well as additional data needs which we identify in our research. We propose an architecture of databases and describe their flow in the Agriculture Management Information System (AMIS).
Źródło:
Geomatics and Environmental Engineering; 2022, 16, 3; 79--109
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bike-sharing system in Poznan – what will Web API data tell us?
System rowerów miejskich w Poznaniu - co nam powiedzą dane z Web API?
Autorzy:
Dzięcielski, Michał
Radzimski, Adam
Woźniak, Marcin
Powiązania:
https://bibliotekanauki.pl/articles/2089635.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Gdański. Komisja Geografii Komunikacji Polskiego Towarzystwa Geograficznego
Tematy:
bike-sharing
cycling
mobility
big data
Poznań
system rowerów miejskich
zbiór danych duży
Opis:
Bike-sharing systems, also known as public bicycles, are among the most dynamically developing mobility solutions in contemporary cities. In the past decade, numerous Polish cities hoping to increase the modal share of cycling have also adopted bike-sharing. Such systems continuously register user movements through installed sensors. The resulting database allows a highly detailed representation of this segment of urban mobility. This article illustrates how a database accessed via a Web API (Web Application Programming Interface) could be used to investigate the spatial distribution of trips, using the case study of Poznań, the fifth-largest city in Poland. Using geographical information systems, we identify the hot spots of bike-sharing as well as areas with low usage. The research procedure outlined in the paper provides knowledge that allows better responding to users’ needs.
Źródło:
Prace Komisji Geografii Komunikacji PTG; 2020, 23(3); 29-40
1426-5915
2543-859X
Pojawia się w:
Prace Komisji Geografii Komunikacji PTG
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ticket tariffs modelling in urban and regional public transport
Autorzy:
Czerliński, Mirosław
Bańka, Michał Sebastian
Powiązania:
https://bibliotekanauki.pl/articles/1833609.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
public transport
ticket tariff
fare system
fare planning
big data
transport publiczny
taryfa biletowa
system opłat
planowanie taryf
Opis:
Ticket tariff is an important factor influencing the demand for public transport. Among basic problematics regarding ticket tariffs are designing new fare systems and optimization of current systems. The task of optimization is influenced by two main factors: ticket prices and the structure of the tariff. Both elements were researched in this article, based on eleven public transport organizers fare systems in Poland – metropolitan areas and cities of a different scale. The purpose of this article was to define basic tariff types used in urban and regional public transport with a presentation of their function models. Ticket tariffs split into two main groups: flat and differential. Differential group of tariffs covers: distance (usually are encountered fares based on a number of kilometres or stops travelled), quality (e.g. different fares on basic and express lines), time (minutes, hours or days of ticket validity, but also different tariff during on-peak and off-peak hours), sections (between which passenger travel on a transit route) and zones (transport network divided into areas, e.g. designated by municipalities boundaries) tariffs. The concept of this study was to transform as many tariffs as possible from tabular form to the mathematical function. Five types of functions were considered for each tariff schematic: linear, power, polynomial, logarithmic and exponential. Functions and associated with them R-squared parameters were obtained as a result of re-gression analysis. The paper indicates that for time, distance and flat tariffs conformity (R2) was in most cases very high and above 0,90. The results indicate that the power function best describes time tariffs. In the case of distance tariffs, different kind of functions can be used: logarithmic, power or polynomial. The proposed function form of tariffs may speed up the process of creating new fare systems or upgrading existing ones. With general knowledge about the structure of tariffs and their function forms, it would be easier to determine the price of different kinds of tickets. New fare integration solutions could be also proposed in the future by using Big Data analysis.
Źródło:
Archives of Transport; 2021, 57, 1; 103-117
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The choice of cloud technology for big data
Autorzy:
Veres, O.
Kozak, N.
Powiązania:
https://bibliotekanauki.pl/articles/411239.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:
big data
cloud service
decision-making method
Decision Support System
chmura obliczeniowa
metody wspomagające podejmowanie decyzji
System Wspomagania Decyzji
Opis:
This article describes specific features of cloud technology types and their existing classifications, as well as the peculiarities of their implementation in the process of designing the DDS for Big Data Management. The application of the analytic hierarchy process for the choice of cloud technology within the project of DDS for Big Data Management is suggested and described within this paper. Multi-criteria decision making task with a defined set of options and criteria is solved.
Źródło:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 59-66
2084-5715
Pojawia się w:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An approach to managing innovation to protect financial sector against cybercrime
Podejście do zarządzania innowacjami w celu ochrony sektora finansowego przed cyberprzestępczością
Autorzy:
Kuzmenko, Olha
Kubálek, Jan
Bozhenko, Victoria
Kushneryov, Olexandr
Vida, Imre
Powiązania:
https://bibliotekanauki.pl/articles/27315234.pdf
Data publikacji:
2021
Wydawca:
Politechnika Częstochowska
Tematy:
cybercrime
policy
financial system
big data
machine learning
innovation
sustainable development
cyberprzestępczość
polityka
system finansowy
uczenie maszynowe
innowacje
zrównoważony rozwój
Opis:
Ensuring the cyber security management is an ever-increasing challenge for the financial institutions and the national financial regulators. The main purpose of the research is to improve cyber security management through analyzing large data volumes of information which helps to identify potential cyber threats at an early stage. The factors of the rapid cybercrime growth via supervised learning models with associated learning (SVM) were identified and evaluated in the paper. The object of research is 21 EU countries. The paper presents the results of an empirical analysis, which showed that the cyber threats are caused by the growth of using online banking (0.49), improvement of internet user skills (0.42), expansion of activities online (0.41). The results of the research can be useful for financial institutions, national regulators and cybersecurity professionals.
Zapewnienie zarządzania cyberbezpieczeństwem jest coraz większym wyzwaniem dla instytucji finansowych i krajowych organów nadzoru finansowego. Głównym celem badania jest usprawnienie zarządzania cyberbezpieczeństwem poprzez analizę dużych wolumenów danych informacji, co pomaga zidentyfikować potencjalne zagrożenia cybernetyczne na wczesnym etapie. W artykule zidentyfikowano i oceniono czynniki szybkiego wzrostu cyberprzestępczości poprzez nadzorowane modele uczenia się z powiązanym uczeniem (SVM). Przedmiotem badań jest 21 krajów UE. W artykule przedstawiono wyniki analizy empirycznej, która wykazała, że cyberzagrożenia spowodowane są wzrostem korzystania z bankowości internetowej (0,49), poprawą umiejętności internautów (0,42), ekspansją aktywności w sieci (0,41). Wyniki badania mogą być przydatne dla instytucji finansowych, krajowych regulatorów i specjalistów ds. cyberbezpieczeństwa.
Źródło:
Polish Journal of Management Studies; 2021, 24, 2; 276--291
2081-7452
Pojawia się w:
Polish Journal of Management Studies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Industry 4.0: tools and implementation
Autorzy:
Sanghavi, Devansh
Parikh, Sahil
Raj, S. Aravind
Powiązania:
https://bibliotekanauki.pl/articles/407044.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Industry 4.0
Internet of things
cyber physical system
CPS
Smart Manufacturing
cloud based manufacturing
Big Data
Opis:
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.
Źródło:
Management and Production Engineering Review; 2019, 10, 3; 3--13
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The safety management system of rail transport
Autorzy:
Jaworska, K.
Nowacki, G.
Powiązania:
https://bibliotekanauki.pl/articles/393675.pdf
Data publikacji:
2019
Wydawca:
Polskie Stowarzyszenie Telematyki Transportu
Tematy:
safety
railway transport
system
risk management
digital transformation
big data
bezpieczeństwo
transport kolejowy
zarządzanie ryzykiem
transformacja cyfrowa
Opis:
The article presents the essence of security management in railway transport and to identify the priorities of Poland’s transportation system. The main problem of paper was formulated as follows: How is the Safety Management System of Rail Transport functioning, taking into account national and international legal regulations and existing threats? In the solving of mentioned problem, the following research methods were applied: analogy, definition, analysis, synthesis, induction, deduction, modeling and diagnostic survey with expert research sample. The research was carried out with emploees of company PKP PLK S.A. A systemic approach underlies the Safety Management System (SMS) functioning now in Poland. To guarantee the proper conduct of transport, each railway transportation enterprise and each administrator of infrastructure is required to create a system based on effective risk management. Adequately prepared SMS procedures should assure the implementation of risk control means and the monitoring of the effectiveness of the applied solutions in order to warrant the due level of safety. Presently, digital transformation is of major importance to the development of railway transport. New management systems will be based on technologies utilizing the Internet, automatization, robotization, and new tools, such as big data.
Źródło:
Archives of Transport System Telematics; 2019, 12, 2; 3-9
1899-8208
Pojawia się w:
Archives of Transport System Telematics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Recommendation systems with the quantum k-NN and Grover algorithms for data processing
Autorzy:
Sawerwain, Marek
Wróblewski, Marek
Powiązania:
https://bibliotekanauki.pl/articles/330538.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
quantum k-NN algorithm
recommendation system
Grover algorithm
big data
kwantowy algorytm k-NN
system rekomendujący
algorytm Grovera
duży zbiór danych
Opis:
In this article, we discuss the implementation of a quantum recommendation system that uses a quantum variant of the k-nearest neighbours algorithm and the Grover algorithm to search for a specific element in an unstructured database. In addition to the presentation of the recommendation system as an algorithm, the article also shows the main steps in construction of a suitable quantum circuit for realisation of a given recommendation system. The computational complexity of individual calculation steps in the recommendation system is also indicated. The verification of the correctness of the proposed system is analysed as well, indicating an algebraic equation describing the probability of success of the recommendation. The article also shows numerical examples presenting the behaviour of the recommendation system for two selected cases.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 1; 139-150
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Collaborative manufacturing based on cloud, and on other I4.0 oriented principles and technologies: a systematic literature review and reflections
Autorzy:
Varela, M. L. R.
Putnik, G. D.
Manupati, V. K.
Rajyalakshmi, G.
Trojanowska, J.
Machado, J.
Powiązania:
https://bibliotekanauki.pl/articles/406942.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
collaborative manufacturing
networked manufacturing
cloud manufacturing
I4.0
cyber physical system
internet of things
big data management
Opis:
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.
Źródło:
Management and Production Engineering Review; 2018, 9, 3; 90-99
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing
Autorzy:
Lazaroiu, George
Androniceanu, Armenia
Grecu, Iulia
Grecu, Gheorghe
Neguriță, Octav
Powiązania:
https://bibliotekanauki.pl/articles/19322650.pdf
Data publikacji:
2022
Wydawca:
Instytut Badań Gospodarczych
Tematy:
cognitive manufacturing
Artificial Intelligence of Things
cyber-physical system
big data-driven deep learning
real-time scheduling algorithm
smart device
sustainable product lifecycle management
Opis:
Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including "cognitive Industrial Internet of Things", "cognitive automation", "cognitive manufacturing systems", "cognitively-enhanced machine", "cognitive technology-driven automation", "cognitive computing technologies", and "cognitive technologies". The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.
Źródło:
Oeconomia Copernicana; 2022, 13, 4; 1047-1080
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 16

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies