• Korean Journal of Soil Science and Fertilizer
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• v.7 no.3
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• pp.177-184
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• 1974

An attempt was made to determine simple and the most reasonable fertilizer recommendation for barley utilizing the present knowledge about the effect of soil and climatic factors on barley response to NPK fertilizer in Korea and establishing the critical contents of available nutrients in soils. The results were summarized as follows. 1. The relationships between relative yields or fertilizers rates for maximum yields from quadratic response curves and contents of organic matter, available $P_2O_5$, exchangeable K in soils were examined. The trend was more prospective with relative yields because of smaller variation than with fertilizer rates. 2. Since the relationship between N relative yields and organic matter contents in soils was almost linear over the practical range, it was difficult to determine the critical content for nitrogen response by quadrant methods. However, 2.6%, country average of organic matter content in upland soils was recommended as the critical point. 3. There showed a trend that average optimum nitrogen rater was higher in heavy texture soils, colder regions. 4. The critical $P_2O_5$ contents in soil were 96 or 118 ppm in two different years, which were very close to the country average, 114 ppm of $P_2O_5$ contents in upland soils. The critical K content in soil was 0.32 me/100g, which was exactly coincident to the country average of exchangeable K in upland soils. 5. According to the contents of avaiiable $P_2O_5$ and exchangeable K, several ranges were established for the purpose of convenience in fertilizer recommendation, that is, very low, Low, Medium, High and very High. 6. More phosphate was recommended in the northern region, clayey soils, and paddy soils, whereas less in the southern region and sandy soils. More potash was recommended in the northern region and sandy soils, whereas less in the southern region and clayey soils. 7. The lower the PH, the more fertilizers were recommended. However, liming was considered to be more effective than increas in amount of fertilizers.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.