Top

Published in:

2022 | OriginalPaper | Chapter

Data-Based Agricultural Business Continuity Management Policies

Author : Athanasios Podaras

Published in: Information and Communication Technologies for Agriculture—Theme II: Data

Publisher: Springer International Publishing

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

search-config

AI-assisted search

Off

Abstract

Data-driven decisions are crucial for modern enterprises regardless of the sector in which they operate. In agriculture, data processing, storage, and manipulation are crucial for boosting agricultural productivity. Nevertheless, the reliance of modern agriculture on information technologies has triggered a great concern regarding the exposure of agricultural processes to various threats that can cause unexpected interruptions. Business continuity deals with these types of threats. Data collection, storage, and processing which can be effectively implemented by modern business intelligence systems can undoubtedly help modern agricultural enterprises implement standard business continuity policies. The present chapter introduces a novel multidimensional approach for facilitating effective data-based business continuity management policies in agriculture. The approach relies on realistic business continuity data from two agrarian industries that are used for the design of two business intelligence multidimensional schemas which facilitate decisions based on descriptive data and for conducting data mining predictions. Examples of descriptive data-based decision-making processes are depicted using business process modeling notation tools and the predictive decisions are conducted via machine learning classifiers. In this way, agricultural business continuity experts in collaboration with agronomists, researchers, and farmers can be motivated to apply fully data-driven agricultural business continuity policies in specific agricultural companies.

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

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Air drill Seeder Distributor Head Evaluation: A Comparison between Laboratory Tests and Computational Fluid Dynamics Simulations

next chapter Soybean Price Trend Forecast Using Deep Learning Techniques Based on Prices and Text Sentiments

The full datasets are also available and can be provided by the author upon request.

Kaul, M., Storey, V. C., & Woo, K. (2017). A framework for managing complexity in information systems. J Database Manage, 28(1), 31–42. https://doi.org/10.4018/JDM.2017010103CrossRef

Koks, E., Pant, R., Thacker, S., & Hall, J. W. (2019). Understanding business disruption and economic losses due to electricity failures and flooding. Int J Disaster Risk Sci, 10, 421–438. https://doi.org/10.1007/s13753-019-00236-yCrossRef

IEEE Risk Insurance. Available at: https://www.ieee.org/about/volunteers/risk-insurance/business-continuity-management.html, Accessed on Sep. 13, 2019.

Engemann, K. J. A., & Henderson, D. M. (2012). Business continuity and risk management: Essentials for an organizational resilience. Rothstein Associates.

Lytos, A., Lagkas, T., Sarigiannidis, P., Zervakis, M., & Livanos, G. (2020). Towards smart farming: Systems, frameworks and exploitation of multiple sources. Computer Netowrks, 172(2020).

Miller, H. E., & Engemann, K. J. (2012). Using analytical methods in Bussiness continuity planning. In K. J. Engemann, A. M. Gil-Lafuente, & J. M. Merigó (Eds.), Modeling and simulation in engineering, economics and management. MS (2012). Lecture notes in business information processing, Vol. 115 (pp. 2–12). Springer.

Caelli, W. J., Kwok, L. F., & Longley, D. (2010). A business continuity management simulator. In K. Rannenberg, V. Varadharajan, & V. C. Weber (Eds.), Security and privacy – Silver linings in the cloud. SEC 2010. IFIP advances in information and communication technology, vol 330 (pp. 9–18). Springer.

Fasolis, E., Vassalos, V., & Kokkinaki, A. (2013). Designing and developing a business continuity plan based on collective intelligence. In C. Douligeris, N. Polemi, A. Karantjias, & W. Lamersdorf (Eds.), Collaborative, trusted and privacy – Aware e/m-services, I3E 2013, IFIP advances in information and communication technology (Vol. 399, pp. 278–285). Springer.

Šimonová, S., & Šprync, O. (2011). Proactive IT/IS monitoring for business continuity planning, E M Ekon. Manag., 14, 57–65.

10.

Bialas, a. (2012). Computer support in business continuity and information security management. In A. Kapczyński, E. Tkacz, & M. Rostanski (Eds.), Internet – Technical developments and applications 2, advances in intelligence and soft computing, vol. 118 (pp. 155–169). Springer – Verlag.

11.

International Organization for Standardizations (ISO). (2019). Security and resilience — Business continuity management systems — Requirements. Available at: https://www.iso.org/obp/ui/#iso:std:iso:22301:ed-2:v1:en (Accessed 10 August 2020).

12.

Gartner. (2017). Magic Quadrant for Disaster Recovery as a Service. Available at: https://www.gartner.com/doc/3746618/magic-quadrant-disaster-recovery-service, 2017 (Accessed 13 January 2019).

13.

Urbanec, J., & Urbancova, H. (2014). Adoption of business continuity management standards in Czech organizations. Scientia Agriculturae Bohemica, 45(1), 66–74. https://doi.org/10.7160/sab.2014.450109CrossRef

14.

The Panhandle Regional Planning Commission (PRPC). Texas Dairy Farm Business Continuity Guide. Available at: http://www.theprpc.org/Programs/RegionalServices/Dairy-Industry/prpc_dairy/bcdoc/Texas%20Dairy%20Farm%20BCG%20Template.docx. (Accessed 20 April 2020).

15.

The Panhandle Regional Planning Commission (PRPC). Texas Cattle Feed Yard Business Continuity Guide. Available at: http://theprpc.org/Programs/RegionalServices/Cattle-Feedyard-Industry/PRPC_CattleFeed/bcdoc/Texas%20Cattle%20Feedyard%20BCG%20Supplement.docx. Accessed 17 March 2020.

16.

BPMN. (2011). Business Process Model and Notation (BPMN), Version 2.0, OMG Document Number: formal/2011-01-03, Standard document. http://www.omg.org/spec/BPMN/2.0. Accessed 20 May 2020.

17.

Rausch, P., Sheta, A. F., & Ayesh, A. (Eds.). (2013). Business intelligence and performance management: Theory, systems and industrial applications. Springer.

18.

Vaisman, A., & Zimanyi, E. (2014). Data warehouse systems: Design and implementation. In Data centric systems and applications. Springer-Verlag.

19.

Starr, R. (2003). Enterprise Resilience: Managing Risk in the Networked Economy. Available at: http://www.scrlc.com/articles/sbreader_Enterprise_Resilience.pdf. Accessed 8 July 2020. p.66.

20.

Snedaker, S. (2007). Business continuity and disaster recovery planning for IT professionals. Elsevier.

21.

Karmas, A., Tzotsos, A., & Karantzalos, K. (2016). Geospatial big data for environmental and agricultural applications. In S. Yu & S. Guo (Eds.), Big data concepts, theories, and applications. Springer. https://doi.org/10.1007/978-3-319-27763-9_10CrossRef

22.

Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy, 40, 79–96.CrossRef

23.

Food and Agriculture Organization of the United Nations. (2020). Available at: http://www.fao.org/2019-ncov/q-and-a/en/. Accessed 21 May 2020.

24.

Tyrychtr, J., Brozek, J., & Vostrovsky, V. (2015). Multidimensional modelling from open data for precision agriculture. In Enterprise and organizational Modeling and simulation - 11th international workshop, EOMAS 2015, June, 08 2015 (pp. 141–152). Springer.

25.

Tyrychtr, J., Ulman, M., & Vostrovský, V. (2015). Evaluation of the state of the business intelligence among small Czech farms. Agricultural Economics – Czech, 61, 63–71.CrossRef

26.

Wang, Z., Li, C., & Liu, W. (2011). A regression analysis of the effect of natural disaster on agriculture. In IEEE Proc. ICBMEI ‘11 (pp. 369–372). Guangzhou.

27.

Kimball, R., & Ross, M. (2010). The Kimball Group reader. Relentlessly practical tools for data warehousing and business intelligence. Wiley.

28.

Rainardi, V. (2008). Multidimensional Database. In Building a data warehouse. Apress. https://doi.org/10.1007/978-1-4302-0528-9_12CrossRef

29.

Caniupán, M., Bravo, L., & Hurtado, C. A. (2012). Repairing inconsistent dimensions in data warehouses. Data & Knowledge Engineering, 79–80(2012), 17–39. https://doi.org/10.1016/j.datak.2012.04.002CrossRef

30.

Pedrycz, W., Al-Hmouz, R., Morfeq, A., & Balamash, A. S. (2014). Building granular fuzzy decision support systems. Knowl. Based Syst., 58(2014), 3–10. https://doi.org/10.1016/j.knosys.2013.07.022CrossRef

31.

Grossmann, W., & Rinderle-Ma, S. (2015). Fundamentals of business intelligence. Springer-Verlag.CrossRef

32.

Podaras, A. (2017). Conceptual model of the BIA data warehouse. In R. Pergl, R. Lock, E. Babkin, & M. Molhanec (Eds.), Enterprise and organizational Modeling and simulation, EOMAS 2017, lecture notes in business information processing, vol 298 (pp. 125–133). Springer.

33.

Nielsen, M., Plotkin, G., & Winskel, G. (1981). Petri nets, event structures and domains, part I. Theoretical Computer Science, 13(1), 85–108.MathSciNetCrossRef

34.

Milani, F. (2019). Business pocesses. In F. Milani (Ed.), Digital business anaysis. Springer International Publishing. https://doi.org/10.1007/978-3-030-05719-0CrossRef

35.

Grąbczewski, K. (2014). Meta-learning in decision tree induction. Springer International Publishing.CrossRef

36.

Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Chapman and Hall.MATH

37.

Pinem, A. F. A., & Setiawan, E. B. (2015). Implementation of classification and regression tree (CART) and fuzzy logic algorithm for intrusion detection system. In Proc. IEEE 3rd international conference on information and communication technology (ICoICT) (pp. 266–271). Nusa Dua, Bali.

38.

R Core Team. (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Available at: http://www.R-project.org. Accessed 20 March 2019.

39.

Kärkkäinen, T. (2014). On cross-validation for MLP model evaluation. In P. Fränti, G. Brown, M. Loog, F. Escolano, & M. Pelillo (Eds.), Structural, syntactic, and statistical pattern recognition. S+SSPR 2014. Lecture notes in computer science, vol 8621. Springer. https://doi.org/10.1007/978-3-662-44415-3_30CrossRef

40.

Jeong, J. H., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., et al. (2016). Random forests for global and regional crop yield predictions. PLoS One, 11(6), e0156571. https://doi.org/10.1371/journal.pone.0156571CrossRef

41.

Gibson, D. (2011). Mitigating risk with a business impact analysis, in managing risks in information systems (2nd ed.). Jones & Bartlett Learning.

42.

Podaras, A. (2018). Measuring the accuracy levels regarding the dual business function criticality classifier. IEEE Access, 6, 41598–41606. https://doi.org/10.1109/ACCESS.2018.2860976CrossRef

43.

Yang, F., Watson, P., Koukoula, M., & Anagnostou, E. N. (2020). Enhancing weather-related power outage prediction by event severity classification. IEEE Access, 8, 60029–60042. https://doi.org/10.1109/ACCESS.2020.2983159CrossRef

44.

Golab, L., Johnson, T., & Shkapenyuk, V. (2012). Scalable scheduling of updates in streaming data warehouses. IEEE T. Knowl. Data En., 24(2012), 1092–1105. https://doi.org/10.1109/TKDE.2011.45CrossRef

Title: Data-Based Agricultural Business Continuity Management Policies
Author: Athanasios Podaras
Publisher: Springer International Publishing
Book: Information and Communication Technologies for Agriculture—Theme II: Data
Print ISBN: 978-3-030-84147-8

Electronic ISBN: 978-3-030-84148-5

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-84148-5_9

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"

Springer Professional "Wirtschaft"

Premium Partner