Data - Based Agricultural Business Continuity Management Policies

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24310%2F22%3A00008229" target="_blank" >RIV/46747885:24310/22:00008229 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-84148-5_9" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-84148-5_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-84148-5_9" target="_blank" >10.1007/978-3-030-84148-5_9</a>

Result language

angličtina

Original language name

Data - Based Agricultural Business Continuity Management Policies

Original language description

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.

Czech name

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Czech description

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Type

C - Chapter in a specialist book

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name

Information and Communication Technologies for Agriculture—Theme II: Data. Springer Optimization and its Applications, Vol. 183

ISBN

978-3-030-84147-8

Number of pages of the result

25

Pages from-to

209-233

Number of pages of the book

288

Publisher name

Springer Nature

Place of publication

Cham

UT code for WoS chapter

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