• Journal of Information Science Theory and Practice
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• v.8 no.1
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• pp.6-19
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• 2020

When information professionals deal with other disciplines in the course of digital humanities projects, they often assume that they are dealing with 'needful users' who have an 'information gap' to fill. This paper argues that the traditional view that information/knowledge is transferred from an information specialist donor to a domain specialist receiver is no longer appropriate in the digital humanities context, where the gap-and-search (or gap-and-filler) approach to information has given way to more direct, explorative engagement with information. The paper asks whether information science and the practising profession are ready for this paradigm shift and examines information science conservatism in two common collaboration scenarios, library support and digital development. It is shown that information science theory still assumes a traditional donor role in both scenarios. How information scientists deal with conservatism in practice is discussed in the example of the Prior project, in which the information science team exerted an ambiguous, hybrid approach with both conservative and non-conservative elements. Finally, two rather hypothetical answers are offered to the question of how information professionals should approach scholarly collaboration in the digital humanities context, where users have ceased to be supplicants. From a purely pragmatic perspective, information scientists need to shift their focus from information needs to research practices and the implications of these practices for digital information systems. More fundamentally, the emergence of digital humanities challenges information professionals to transform information systems designed for searching into digital objects that can be explored more freely by the digital humanities community.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.