Data - Based Agricultural Business Continuity Management Policies

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Data - Based Agricultural Business Continuity Management Policies

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Data - Based Agricultural Business Continuity Management Policies

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

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

ISBN

978-3-030-84147-8

Počet stran výsledku

25

Strana od-do

209-233

Počet stran knihy

288

Název nakladatele

Springer Nature

Místo vydání

Cham

Kód UT WoS kapitoly

—