107. Decision support in the open service platform Akkerweb for precision agriculture

Kempenaar, C.; Been, T.H.; Evert, F.K. van; Hoving, I.E.; Kessel, G.J.T.; Dantuma, W.; Booij, Johan; Molendijk, L.P.G.; Sijbrandij, F.D.; Boheemen, Koen van

doi:10.3920/978-90-8686-916-9_107

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The grounded theory was undertaken in this research. Because the nature of the research seeks to understand and explains social phenomenon or process within the context of a given application and it demands to 239 approve the validity of the theory from the point of those employees of the company who participated within the context during the study [14]. A summary of guidelines for information system researchers applied in the research of BDA, which includes research question, data collection, data analysis and result interpretation [20].…”

Section: Methodsmentioning

confidence: 99%

“…For example, Hadoop and cloud-based analytics were applied in the crop identification with comparisons to prices in different seasons [22]. Hence, "big data analysis" is the term used to describe a new generation of practices [14,24] designed so that farmers and related organizations can extract economic value from very large volumes of a wide variety of data by enabling high-velocity capture, discovery, and/or analysis [18,27].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Challenges of Identifying and Utilizing Big Data Analytics in a Resource-Constrained Environment: In the Case of Ethiopia

Akal

Beshah

Sackmann

et al. 2019

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Big data analytics (BDA) is the process of capturing and storing huge volume of data which has different formats and generated in high Velocity. It also refers to the process of analyzing big data for the purpose of decision making, strategic planning and policy formulation. Some of the applications of BDA include market segmentation, sales forecasting, weather forecasting, payment fraud detection, crop diseases detection, e-commerce analysis and users purchasing recommendation and others. The application of BDA is not only left for economically developed regions. It is also important for resource-constrained environments. In this study, challenges of identifying and utilizing big data analytics in the resource-constrained environment in the case of Ethiopia have been explored using some case. The case studies considered potential industries that can generate big data in Ethiopia. Ethiopian Telecommunication Corporation, Agricultural Transformation Agency, Payment systems like Hello Cash and Ethiopian Educational Networks (EthERNet) were considered as a case study. In the study, a qualitative grounded approach has been applied. Data was collected using a semi-structured interview approach. As data analysis result and discussion indicated that even if the selected potential industries have been generated big data they are not using it fully for the purpose of decision making. Potential challenges were identified in the identifying and utilizing of BDA in a resource-constrained environment. Some of these areas: lack of BDA awareness, data integration challenge, lack of skilled experts in the area, lack of data correctness and completeness, lack of standardized data registry, lack of leadership and management skill, issue of data privacy and infrastructure challenges including a huge volume of storage device constraint. Based on the identified challenges of BDA implementations in this study and possible application areas of BDA in those industries, a conceptual framework of the study were formulated.

show abstract

Section: Methodsmentioning

confidence: 99%