• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.527-535
• /
• 2021

Traditional business intelligence (BI) systems have been used widely as tools for better decision-making on time. On the other hand, building a data warehouse (DW) for the efficient analysis of rapidly growing data is time-consuming and complex. In particular, the ETL (Extract, Transform, and Load) process required to build a data warehouse has become much more complex as the BI platform moves to a cloud environment. Various BI solutions based on the NoSQL database, such as MongoDB, have been proposed to overcome these ETL issues. Decision-makers want easy access to data without the help of IT departments or BI experts. Recently, self-service BI (SSBI) has emerged as a way to solve these BI issues. This paper proposes a self-service BI system with farming data using the MongoDB cloud as DW to support the selection of new crops by return-farmers. The proposed system includes functions to provide insights to decision-makers, including data visualization using MongoDB charts, reporting for advanced data search, and monitoring for real-time data analysis. Decision makers can access data directly in various ways and can analyze data in a self-service method using the functions of the proposed system.

• Journal of KIISE:Computing Practices and Letters
• /
• v.12 no.4
• /
• pp.264-274
• /
• 2006

Recently, a variety of organizations including enterprises tend to try to use reporting tools as a data analysis tool for decision making support because reporting tools are capable of formatting data flexibly. Traditional reporting tools have thin-client structure in which all of dynamic documents are generated in the server side. This structure enables reporting tools to avoid repetitive process to generate dynamic documents, when many clients intend to access the same dynamic document. However, generating dynamic documents for data analysis doesn't consider a number of potential readers and increases requests to the server by making clients input various parameters at short intervals. In the structure of the traditional reporting tools, the increase of these requests leads to the increase of processing load in the server side. Thus, we present the reporting tool that can generate dynamic documents at the client side. This reporting tool has a processing mechanism to deal with a number of data despite the limited memory capacity of the client side.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.3
• /
• pp.154-166
• /
• 2009

Database query and reporting tools, OLAP tools and data mining tools are typical front-end tools in Business Intelligence environment which is able to support gathering, consolidating and analyzing data produced from business operation activities and provide access to the result to enterprise's users. Traditional reporting tools have an advantage of creating sophisticated dynamic reports including SQL query result sets, which look like documents produced by word processors, and publishing the reports to the Web environment, but data source for the tools is limited to RDBMS. On the other hand, OLAP tools and data mining tools have an advantage of providing powerful information analysis functions on each own way, but built-in visualization components for analysis results are limited to tables or some charts. Thus, this paper presents a system that integrates three typical front-end tools to complement one another for BI environment. Traditional reporting tools only have a query editor for generating SQL statements to bring data from RDBMS. However, the reporting tool presented by this paper can extract data also from OLAP and data mining servers, because editors for OLAP and data mining query requests are added into this tool. Traditional systems produce all documents in the server side. This structure enables reporting tools to avoid repetitive process to generate documents, when many clients intend to access the same dynamic document. But, because this system targets that a few users generate documents for data analysis, this tool generates documents at the client side. Therefore, the tool has a processing mechanism to deal with a number of data despite the limited memory capacity of the report viewer in the client side. Also, this reporting tool has data structure for integrating data from three kinds of data sources into one document. Finally, most of traditional front-end tools for BI are dependent on data source architecture from specific vendor. To overcome the problem, this system uses XMLA that is a protocol based on web service to access to data sources for OLAP and data mining services from various vendors.