Top

The International Journal of Advanced Manufacturing Technology

Published in:

10-09-2019 | ORIGINAL ARTICLE

Reference architecture of common service platform for Industrial Big Data (I-BD) based on multi-party co-construction

Authors: Xianyu Zhang, Xinguo Ming, Dao Yin

Published in: The International Journal of Advanced Manufacturing Technology | Issue 5-6/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

In today’s information age with rapid development, all kinds of data have exploded. The Industrial Big data Application (I-BD) is generated by the continuous infiltration of big data in industry. Scholars have done a lot of research on the method, technology, and architecture of industrial big data from the perspective of data flow. However, there are relatively few studies on the reference model, reference architecture, and implementation path for industrial big data from the perspectives of detailed application scenarios, common service platform, and specific implementation. In this paper, firstly, the current situation of I-BD is analyzed. Secondly, a general reference model for I-BD is proposed, which consists of Industry (I) dimension, Application scenario (A) dimension, and common service platform (P) dimension. Further, the overall planning of application scenarios for I-BD based on industrial value chain for I-BD is studied. Again, a reference architecture of common service platform for I-BD based on multi-party co-construction is proposed. Finally, the implementation path of common service platform for I-BD is given. It can be used as a reference for industry and government to design, set, and carry out I-BD.

previous article The plastic flow stability of chip materials in metal cutting process

next article Comparative life cycle assessment and cost analysis of autoclave and pressure bag molding for producing CFRP components

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Fan J, Han F, Liu H (2014) Challenges of big data analysis. Natl Sci Rev 1(2):293–314 https://www.researchgate.net/publication/255687770_Challenges_of_Big_Data_Analysis

Bello-Orgaz G, Jung JJ, Camacho D (2016) Social big data: recent achievements and new challenges. Information Fusion 28:45–59 https://www.researchgate.net/publication/281412960

Kuo YH, Kusiak A (2018) From data to big data in production research: the past and future trends. Int J Prod Res 11:1–26 https://www.researchgate.net/publication/323519636

Li X, Li X (2018) Big data and its key Technology in the Future. Computing in Science & Engineering 20(4):75–88 https://www.researchgate.net/publication/326310354

Gaff BM, Sussman HE, Geetter J (2014) Privacy and big data. Computer 47(6):7–9 https://www.researchgate.net/publication/263285388

Bertot JC, 2013 Choi H big data and e-government:issues, policies, and recommendations. Conference: Proceedings of the 4th Annual International Conference on Digital Government Research https://www.researchgate.net/publication/262158521. Accessed 20 June 2019

Chen HM, Kazman R, Haziyev S (2016) Strategic prototyping for developing big data systems. IEEE Softw 33(2):36–43 http://www.researchgate.net/publication/296480105

Kong X, Feng M, Wang R (2015) The current status and challenges of establishment and utilization of medical big data in China. European Geriatric Medicine 6(6):515–517 http://www.researchgate.net/publication/283907774

Dubey R, Gunasekaran A, Childe SJ, Wamba SF, Papadopoulos T (2016) The impact of big data on world-class sustainable manufacturing. Int J Adv Manuf Technol 84(1–4):631–645. https://doi.org/10.1007/s00170-015-7674-1 http://link.springer.com/article/10.1007/s00170-015-7674-1 CrossRef

10.

Williams E, Moore J, Li SW, Rustici G, Tarkowska A, Chessel A, Leo S, Antal B, Ferguson RK, Sarkans U (2017) The image data resource: a bioimage data integration and publication platform. Nat Methods 14(8):775. http://www.researchgate.net/publication/318173201–781

11.

Gijzen H (2013) Development: big data for a sustainable future. Nature 502(7469):38 http://www.researchgate.net/publication/257350114

12.

Ramakrishnan N, Kumar R (2016) Big Data. Computer 49(4):20–22 https://www.researchgate.net/publication/301306984

13.

Sezer OB, Dogdu E, Ozbayoglu AM (2017) Context aware computing, learning and big data in internet of things: a survey. IEEE Internet of Things Journal PP 99:1–1 https://www.researchgate.net/publication/321090552

14.

Wu D, Birge JR (2017) Risk intelligence in big data era: a review and introduction to special issue. IEEE Transactions on Cybernetics 46(8):1718–1720 https://www.researchgate.net/publication/305733389

15.

Joseph RC, Johnson NA (2013) Big data and transformational government. It Professional 15(6):43–48 https://www.researchgate.net/publication/260721212

16.

Xiang C, Fang L, Hong X, Yang L (2017) Exploiting Mobile big data: sources, features, and applications. IEEE Netw 31(1):72–79 http://www.researchgate.net/publication/312668677

17.

Qi Q, Fei T (2018) Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6(99):1–1 http://www.researchgate.net/publication/322512249

18.

Tseng C-M, Chau SC-K, Liu X (2017) Improving viability of electric taxis by taxi service strategy optimization: a big data study of new York City. IEEE Transactions on Intelligent Transportation Systems PP 99:1–13 https://www.researchgate.net/publication/320033276

19.

Shu Y, Liang M, Cheng F, Zhang Z, Zhao J (2016) Abnormal situation management: challenges and opportunities in the big data era. Comput Chem Eng 91:104–113 https://www.researchgate.net/publication/301352896

20.

Akter S, Wamba SF (2017) Big data and disaster management: a systematic review and agenda for future research. Ann Oper Res 9:1–21 https://www.researchgate.net/publication/319231257

21.

Chen X, Shuai S, Tian Z, Zhen X, Peng Y (2016) Impacts of air pollution and its spatial spillover effect on public health based on China's big data sample. J Clean Prod 142:915–925 https://www.researchgate.net/publication/297659435

22.

Woo J, Shin S-J, Seo W, Meilanitasari P (2018) Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation. Int J Adv Manuf Technol 99(9–12):2193–2217. https://doi.org/10.1007/s00170-018-2416-9 https://link.springer.com/article/10.1007/s00170-018-2416-9 CrossRef

23.

Alves W, Martins D, Bezerra U, Klautau A (2017) A hybrid approach for big data outlier detection from electric power SCADA system. IEEE Lat Am Trans 15(1):57–64 http://www.researchgate.net/publication/312668624

24.

Draxl C, Scheffler M (2018) NOMAD: the FAIR concept for big-data-driven materials science. MRS Bull 43(09):676–682 http://www.researchgate.net/publication/325142892

25.

Li Z, Fei RY, Wang Y, Ning B, Tao T (2018) Big data analytics in intelligent transportation systems: a survey. IEEE Trans Intell Transp Syst 20(1):383–398 https://www.researchgate.net/publication/324712011

26.

Zhang X, Ming X, Liu Z, Qu Y, Yin D (2019) A framework and implementation of customer platform-connection manufactory to service (CPMS) model in product service system. J Clean Prod 230:798–819. https://doi.org/10.1016/j.jclepro.2019.04.382 http://www.sciencedirect.com/science/article/pii/S0959652619314830 CrossRef

27.

Zhang Y, Shan R, Yang L, Sakao T, Huisingh D (2017) A framework for big data driven product lifecycle management. J Clean Prod 159:229–240 https://www.researchgate.net/publication/316894948

28.

Zhu J, Yuan Y, Li D, Gao F (2018) Monitoring big process data of industrial plants with multiple operating modes based on Hadoop. J Taiwan Inst Chem Eng. 91:10–21 http://www.researchgate.net/publication/325601320

29.

Choi TM, Chan HK, Yue X (2016) Recent development in big data analytics for business operations and risk management. IEEE Transactions on Cybernetics 47(1):81–92 https://www.researchgate.net/publication/290442195

30.

Prasad S, Zakaria R, Altay N (2016) Big data in humanitarian supply chain networks: a resource dependence perspective. Ann Oper Res 270:1–31. http://link.springer.com/10.1007/s10479-016-2280-7 MathSciNet

31.

Tao F, Cheng J, Qi Q, Zhang M, Zhang H, Sui F (2018) Digital twin-driven product design, manufacturing and service with big data. Int J Adv Manuf Technol 94(9–12):3563–3576. https://doi.org/10.1007/s00170-017-0233-1 https://link.springer.com/article/10.1007%2Fs00170-017-0233-1 CrossRef

32.

Vagliano I, Günther F, Heinz M, Apaolaza A, Bienia I, Breitfuss G, Blume T, Collyda C, Fessl A, Gottfried S (2018) Open innovation in the big data era with the MOVING platform: an integrated working and training approach for data-savvy information professionals. IEEE MultiMedia PP 99:1–1 http://ieeexplore.ieee.org/document/8494800/

33.

Esposito C, Ficco M, Palmieri F, Castiglione A (2015) A knowledge-based platform for big data analytics based on publish/subscribe services and stream processing. Knowl-Based Syst 79(C):3–17 https://www.researchgate.net/publication/262342402

34.

See-To EWK, Ngai EWT (2016) Customer reviews for demand distribution and sales nowcasting: a big data approach. Ann Oper Res 6:1–17 http://link.springer.com/10.1007/s10479-016-2296-z

35.

Zhan Y, Tan KH, Li Y, Ying KT (2016) Unlocking the power of big data in new product development. Ann Oper Res 270:1–19. http://link.springer.com/10.1007/s10479-016-2379-x

36.

Chang A-F, Liu YA (2010) Integrated process modeling and product Design of Biodiesel Manufacturing. Ind Eng Chem Res 49(3):1197–1213. https://doi.org/10.1021/ie9010047 https://www.researchgate.net/publication/231390821 CrossRef

37.

Mishra D, Gunasekaran A, Papadopoulos T, Childe SJ (2016) Big data and supply chain management: a review and bibliometric analysis. Ann Oper Res 270:1–24. http://link.springer.com/10.1007/s10479-016-2236-y MATH

38.

Kusiak A (2017) Smart manufacturing must embrace big data. Nature 544(7648):23–25 http://www.researchgate.net/publication/315792168

39.

Moniz S, Barbosa-Povoa AP, de Sousa JP, Duarte P (2014) Solution methodology for scheduling problems in batch plants. Ind Eng Chem Res 53(49):19265–19281. https://doi.org/10.1021/ie403129y http://www.researchgate.net/publication/272784489 CrossRef

40.

Xu X, Sheng QZ, Zhang LJ, Fan Y, Dustdar S (2015) From big data to big service. Computer 48(7):80–83 https://www.researchgate.net/publication/282544285

41.

Spiess J, T'Joens Y, Dragnea R, Spencer P, Philippart L (2014) Using big data to improve customer experience and business performance. Bell Labs Technical Journal 18(4):3–17 https://www.researchgate.net/publication/263128852

42.

Zhang X, Ming X, Liu Z, Qu Y, Yin D (2019) An overall framework and subsystems for smart manufacturing integrated system (SMIS) from multi-layers based on multi-perspectives. Int J Adv Manuf Technol 103:703–722. https://doi.org/10.1007/s00170-019-03593-6 CrossRef

43.

Tran QT, Nguyen SD, Seo TI (2019) Algorithm for Estimating Online Bearing Fault Upon the Ability to Extract Meaningful Information from Big Data of Intelligent Structures. IEEE Trans Ind Electron 66(5):3204–3813 https://doi.org/10.1109/TIE.2018.2847704

44.

Serb A, Bill J, Khiat A, Berdan R, Legenstein R, Prodromakis T (2016) Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nat Commun 7(1261):12611 http://www.researchgate.net/publication/308755993

45.

Onel M, Kieslich CA, Guzman YA, Floudas CA, Pistikopoulos EN (2018) Big data approach to batch process monitoring: simultaneous fault detection and diagnosis using nonlinear support vector machine-based feature selection. Comput Chem Eng 115:46–63 https://www.researchgate.net/publication/324070122

46.

Kumar M, Rath NK, Rath SK (2016) Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier. J Biomed Inform 60:395–409 https://www.researchgate.net/publication/297891757

47.

Lupiani E, Juarez JM, Palma J, Marin R (2017) Monitoring elderly people at home with temporal case-based reasoning. Knowl-Based Syst https://www.researchgate.net/publication/318594717 134:116–134

48.

Li X, Song J, Huang B (2016) A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics. Int J Adv Manuf Technol 84(1–4):119–131. https://doi.org/10.1007/s00170-015-7804-9 https://link.springer.com/article/10.1007/s00170-015-7804-9 CrossRef

49.

Zhou Z, Gao C, Chen X, Yan Z, Mumtaz S, Rodriguez J (2018) Social big-data-based content dissemination in internet of vehicles. IEEE Transactions on Industrial Informatics 14(2):768–777 https://www.researchgate.net/publication/318766784

50.

He R, Bo A, Molisch AF, Stuber GL, Li Q, Zhong Z, Jian Y (2018) Clustering enabled Wireless Channel modeling using big data algorithms. IEEE Communications Magazine PP 99:1–7 https://www.researchgate.net/publication/322994586

51.

Zhang X, Ming X, Liu Z, Yin D, Chen Z, Chang Y (2019) A reference framework and overall planning of industrial artificial intelligence (I-AI) for new application scenarios. Int J Adv Manuf Technol 101(9–12):2367–2389. https://doi.org/10.1007/s00170-018-3106-3 https://link.springer.com/article/10.1007/s00170-018-3106-3 CrossRef

Title: Reference architecture of common service platform for Industrial Big Data (I-BD) based on multi-party co-construction
Authors: Xianyu Zhang
Xinguo Ming
Dao Yin
Publication date: 10-09-2019
Publisher: Springer London
Published in: The International Journal of Advanced Manufacturing Technology / Issue 5-6/2019
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-019-04374-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 5-6/2019

Dry hyperbaric welding of HSLA steel up to 35 bar ambient pressure with CMT arc mode

Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel

Measurement of internal residual stress field in multi-layer fillet welded joint of ultra-thick circular plate and tube

Chatter suppression analysis in milling process with variable spindle speed based on the reconstructed semi-discretization method

Study on magnetic abrasive finishing of AlSi10Mg alloy prepared by selective laser melting

Digitalization of a standard robot arm toward 4th industrial revolution

Premium Partners