Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
first last
cannonical link button


Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

Tytuł artykułu

A classification of real time analytics methods. An outlook for the use within the smart factory

Autorzy Trinks, S. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN The creation of value in a factory is transforming. The spread of sensors, embedded systems, and the development of the Internet of Things (IoT) creates a multitude of possibilities relating to upcoming Real Time Analytics (RTA) application. However, already the topic of big data had rendered the use of analytical solutions related to a processing in real time. Now, the introduced methods and concepts can be transferred into the industrial area. This paper deals with the topic of the current state of RTA having the objective to identify applied methods. In addition, the paper also includes a classification of these methods and contains an outlook for the use of them within the area of the smart factory.
Słowa kluczowe
PL analiza w czasie rzeczywistym   inteligentna fabryka   Przemysł 4.0   inteligentna produkcja   Internet rzeczy   uczenie maszynowe  
EN real time analytics   smart factory   Industry 4.0   smart manufacturing   internet of things   machine learning  
Wydawca Wydawnictwo Politechniki Śląskiej
Czasopismo Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
Rocznik 2018
Tom z. 119
Strony 313--329
Opis fizyczny Bibliogr. 49 poz.
autor Trinks, S.
1. A.A.E.S.Y., &. Baldominos, I.P. (2014). A scalable machine learning online service for big data real-time analysis. Computational Intelligence in Big Data (CIBD), IEEE Symposium. Orlando.
2. Ari, I., Olmezogullari, E., and Çelebi, Ö.F. (2012). Data stream analytics and mining in the cloud. Cloud Computing Technology and Science (CloudCom), 4th International Conference on IEEE.
3. Bifet, A., Maniu, S., Qian, J., Tian, G., He, C., and Fan, W. (2015). StreamDM: Advanced data mining in Spark streaming. Data Mining Workshop (ICDMW), IEEE International Conference. Atlantic City.
4. Bößwetter, D. (2010). Spaltenorientierte Datenbanken. Informatik-Spektrum, 33, 1, 61-65.
5. Chamoni, P., and Gluchowski, P. (2017). Business Analytics – State of the Art. Controlling & Management Review, 61, 4, 8-17.
6. Chen, J., Jindel, S., Walzer, R., Sen, R., Jimsheleishvilli, N., and Andrews, M. (2016). The MemSQL Query Optimizer: A modern optimizer for real-time analytics in a distributed database. Proceedings of the VLDB Endowment, 9, 13, 1401-1412.
7. Chen, T., Man, Z., Li, H., Sun, X., Wong, R.K., and Yu, Z. (2014). Building a massive stream computing platform for flexible applications. Big Data (BigData Congress). IEEE International Congress, 414-421.
8. Cleve, J., and Lämmel, U. (2016). Data Mining. Berlin: De Gruyter Oldenbourg.
9. Cooper, H.M. (1998). Synthesizing Research: A Guide for Literature Reviews. Thousand Oaks: Sage Publ.
10. Cundius, C., and Alt, R.A. (2013). Real-Time or Near Real-Time? Towards a Real-Time Assessment Model. International Conference on Information Systems (ICIS), Mailand.
11. Dais, S. (2017). Industrie 4.0 – Anstoß, Vision, Vorgehen. Handbuch Industrie 4.0, 4. Berlin-Heidelberg: Springer, 261-277.
12. Dinsmore, T.W. (2016). Disruptive Analytics: Charting Your Strategy for Next-Generation Business Analytics. Apress.
13. Djorgovski, S.G., Graham, M.J., Donalek, C., Mahabal, A.A., Drake, A.J., Turmon, M., and Fuchs, T. (2016). Real-time data mining of massive data streams from synoptic sky surveys. Future Generation Computer Systems, 59, 95-104.
14. Dua, S., and Du, X. (2016). Data mining and machine learning in cybersecurity. CRC Press.
15. Felden, C. (2017). Business Analytics. Enzyklopaedie der Wirtschaftsinformatik,, 19.05.2017.
16. Felden, C. (28.11.2016). Künstliche Intelligenz, Enzyklopaedie der Wirtschaftsinformatik, KI-und-Softcomputing/Kunstliche-Intelligenz, 23.01.2018.
17. Floratou, A., Agrawal, A., Graham, B., Rao, S., and Ramasamy, K. (2017). Dhalion: self-regulating stream processing in heron. Proceedings of the VLDB Endowment, 10, 12, 1825-1836.
18. Furtner, E.-M., Wildhölzl, H., Schlager-Weidinger, N., and Promberger, K. (2016). Impacts of SAP HANA on Business Intelligence. Multidimensional Views on Enterprise Information Systems: Proceedings of ERP Future 2014. Switzerland: Springer International Publishing, 163-171.
19. Gewiehs, C. (2017). Maschinelles Lernen für die industrielle Fertigung Die Infrastruktur soll sich nachjustieren. Maschinenmarkt, 754, 030, 30-31.
20. Hackathorn, R. (2004). Real-time to real-value. Information Management, 14, 1, 24.
21. Herden, O. (2013). Spaltenbasierte Datenbanken-Ein Konzept zur Handhabung großer Datenmengen. GIL Jahrestagung.
22. Iqbal, M.H., and Soomro, T.R. (2015). Big data analysis: Apache storm perspective. International Journal of Computer Trends and Technology, 19, 1, 9-14.
23. Kagermann, H. (2017). Chancen von Industrie 4.0 nutzen. Handbuch Industrie 4.0, 4. Berlin-Heidelberg: Springer, 235-246.
24. Katsipoulakis, N.R., Labrinidis, A., and Chrysanthis, P.K. (2017). A holistic view of stream partitioning costs. Proceedings of the VLDB Endowment, 10, 11, 1286-1297.
25. Kejariwal, A., Kulkarni, S., and Ramasamy, K. (2015). Real time analytics: algorithms and systems. Proceedings of the VLDB Endowment, 8, 12, 2040-2041.
26. Kumaraguru, S., and Morris, K.C. (2014). Integrating real-time analytics and continuous performance management in smart manufacturing systems. In Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World. APMS 2014. IFIP Advances in Information and Communication Technology. Berlin-Heidelberg: Springer, 175-182.
27. Lanquillon, C., and Mallow, H. (2015). Advanced Analytics mit Big Data. In Praxishandbuch Big Data: Wirtschaft-Recht-Technik. Wiesbaden: Gabler, 55-89.
28. Larson, P.Å., Birka, A., Hanson, E.N., Huang, W., Nowakiewicz, M., and Papadimos, V. (2015). Real-time analytical processing with SQL server. Proceedings of the VLDB Endowment. Hawaii.
29. Laudon, K.C., Laudon, J.P., and Schoder, D. (2010). Wirtschaftsinformatik – Eine Einführung. Pearson Deutschland GmbH.
30. Lee, J., Bagheri, B., and Kao, H.-A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23.
31. Luo, L.P.M. (2014). Relational OLAP query optimization. Proceedings of 24th Annual International Conference on Computer Science and Software Engineering. Markham, Ontario, Canada.
32. Marascu, A., Pompey, P., Bouillet, E., Wurst, M., Verscheure, O., Grund, M., and Cudre-Mauroux, P. (2014). TRISTAN: Real-time analytics on massive time series using sparse dictionary compression. Big Data (Big Data), IEEE International Conference. Washington.
33. Mertens, P., Bodendorf, F., König, W., Picot, A., Schumann, M., and Hess, T. (2017). Grundzüge der Wirtschaftsinformatik. Berlin-Heidelberg: Springer.
34. Nishihara, R., Moritz, P., Wang, S., Tumanov, A., Paul, W., Schleier-Smith, J., and Stoica, I. (2017). Real-time machine learning: The missing pieces. Proceedings of the 16th Workshop on Hot Topics in Operating Systems. ACM.
35. Oxford Dictionaries. Oxford University Press, real_time, 11.07.2017.
36. Parikh, D., and Tirkha, P. (2013). Data Mining & Data Stream Mining – Open Source Tools. International Journal of Innovative Research in Science, Engineering and Technology, 2, 10, 5234-5239.
37. Perera, S., and Suhothayan, S. (2015). Solution patterns for realtime streaming analytics. Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems.
38. Prassol, P. (2015). SAP HANA als Anwendungsplattform für Real-Time Business. HMD Praxis der Wirtschaftsinformatik, 52, 3, 358-372.
39. Rahnama, A.H.A. (2014). Distributed real-time sentiment analysis for big data social streams. Control, Decision and Information Technologies (CoDIT). IEEE International Conference, 789-794.
40. Russom, P. (2011). Big data analytics. TDWI best practices report.
41. Shoro ,A.G., and Soomro, T.R. (2015). Big data analysis: Apache spark perspective. Global Journal of Computer Science and Technology, 15, 1.
42. Siegel, E. (2013). Predictive analytics. Hoboken: Wiley.
43. Szpisják, P., and Rádai, L. (2016). Performance issues of In-Memory Databases in OLTP systems. Applied Computational Intelligence and Informatics (SACI), 11th International Symposium on IEEE.
44. Trinks, S., and Felden, C. (2017). Real time analytics – State of the art: Potentials and limitations in the smart factory. 2017 IEEE International Conference on Big Data (Big Data). Boston.
45. van Rotterdam, J. (2016). It's Time for Real-Time Analytics. KM World, 25, 3, 17.
46. Venner, J., Wadkar, S., and Siddalingaiah, M. (2014). Pro Apache Hadoop. Apress.
47. Wang, S., Wan, J., Li, D., and Zhang, C. (2016). Implementing smart factory of industrie 4.0: an outlook. International Journal of Distributed Sensor Networks 12.1, 12, 1, 3159805.
48. Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2016). Data Mining – Practical machine learning tools and techniques. Morgan Kaufmann.
49. Yadav, V. (2017). Real-Time Analytics with Storm. Processing Big Data with Azure HDInsight. Berkeley, CA: Apress.
PL Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-7fd619a6-f814-4887-8047-128173263805
DOI 10.29119/1641-3466.2018.119.22