• The Journal of Society for e-Business Studies
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• v.4 no.1
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• pp.21-36
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• 1999

Using user monitoring techniques on web, marketing decision makers in cyber shopping malls can gather customer behavior data as well as sales transaction data and customer profiles. In this paper, we present a marketing rule extraction technique for customer behavior analysis in cyber shopping malls, The technique is an application of market basket analysis which is a representative data mining technique for extracting association rules. The market basket analysis technique is applied on a customer behavior log table, which provide association rules about web pages in a cyber shopping mall. The extracted association rules can be used for mall layout design, product packaging, web page link design, and product recommendation. A prototype cyber shopping mall with customer monitoring features and a customer behavior analysis algorithm is implemented using Java Web Server, Servlet, JDBC(Java Database Connectivity), and relational database on windows NT.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.131-139
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• 2010

Recent spread of GIS and the increasing demand of spatial data have brought about the development of web GIS. In addition to sharing and mapping spatial data, web GIS is also required to provide spatial analytic functions on the web. The FOSS(free and open source software) can play an important role in developing such a system for web GIS. In this paper, we proposed a web-based system for raster data analysis using map algebra. We employed GRASS as an open source software and implemented the GRASS functionalities on the web using java methods for invocation of server-side commands. Map algebra and AHP were combined for the raster data analysis in our system. For a feasibility test, the landslide susceptibility in South Korea was calculated using rainfall, elevation, slope angle, slope aspect, and soil layers. It is anticipated that our system will be extensible to other web GIS for raster data analysis with GRASS.

• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.493-504
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• 2009

The web log data analysis is to analysis traces which visitors remain while they drop by a web-site. Ultimately it can help to obtain a lot of useful information that can efficiently manage homepage and perform CRM(customer relationship management) using obtained information. In this paper, we provide a basic information to manage efficiently homepage of D university and to establish strategy for invitation of new pupil, as analyzing web log data for D university.

• Genomics & Informatics
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• v.2 no.3
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• pp.142-146
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• 2004

Biological data and analysis tools are accumulated in distributed databases and web servers. For this reason, biologists who want to find information from the web should be aware of the various kinds of resources where it is located and how it is retrieved. Integrating the data from heterogeneous biological resources will enable biologists to discover new knowledge across the specific domain boundaries from sequences to expression, structure, and pathway. And inevitably biological databases contain noisy data. Therefore, consensus among databases will confirm the reliability of its contents. We have developed WeSAT that integrates distributed and heterogeneous biological databases and analysis tools, providing through Web Services protocols. In WeSAT, biologists are retrieved specific entries in SWISS-PROT/EMBL, PDB, and KEGG, which have annotated information about sequence, structure, and pathway. And further analysis is carried by integrated services for example homology search and multiple alignments. WeSAT makes it possible to retrieve real time updated data and analysis from the scattered databases in a single platform through Web Services.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.355-362
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• 2022

Smart Farm, which combines information and communication technologies with agriculture is moving from simple monitoring of the growth environment toward discovering the optimal environment for crop growth and in the form of self-regulating agriculture. To this end, it is important to collect related data, but it is more important for farmers with cultivation know-how to analyze the collected data from various perspectives and derive useful information for regulating the crop growth environment. In this study, we developed a web service that allows farmers who want to obtain necessary information with data related to crop growth to easily analyze data. Web-based data analysis serivice developed uses R language for data analysis and Express web application framework for Node.js. As a result of applying the developed data analysis service together with the growth environment monitoring system in operation, we could perform data analysis what we want just by uploading a CSV file or by entering raw data directly. We confirmed that a service provider could provid various data analysis services easily and could add a new data analysis service by newly adding R script.

• Culinary science and hospitality research
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• v.23 no.4
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• pp.22-32
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• 2017

The purpose of this study was to explore awareness of food tourism using big data analysis. For this, this study collected data containing 'food tourism' keywords from google web search, google news, and google scholar during one year from January 1 to December 31, 2016. Data were collected by using SCTM (Smart Crawling & Text Mining), a data collecting and processing program. From those data, degree centrality and eigenvector centrality were analyzed by utilizing packaged NetDraw along with UCINET 6. The result showed that the web visibility of 'core service' and 'social marketing' was high. In addition, the web visibility was also high for destination, such as rural, place, ireland and heritage; 'socioeconomic circumstance' related words, such as economy, region, public, policy, and industry. Convergence of iterated correlations showed 4 clustered named 'core service', 'social marketing', 'destinations' and 'social environment'. It is expected that this diagnosis on food tourism according to changes in international business environment by using these web information will be a foundation of baseline data useful for establishing food tourism marketing strategies.

• The Journal of Bigdata
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• v.2 no.2
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• pp.105-114
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• 2017

Today, many services are supported on the web sites. Analysis of usage patterns of web site visitors is very important to optimize the use and efficiency of the web sites. In this study, analysis of usage patterns and comparative analysis of user groups were conducted by analyzing web access log provided by BPI Challenge 2016. This data provides access logs to the web site in the IT system of a Dutch Employee Insurance Agency (UWV). The customer information, and the click data describing the customers' behavior when using the agency's web site. In this study, we use process mining techniques to analyze the usage patterns of customers and the characteristics of customer groups, and ultimately improve the service quality of customers using web services.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.575-584
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• 2013

Data types used for big-data analysis are very widely, such as news, blog, SNS, papers, patents, sensed data, and etc. Particularly, the utilization of web documents offering reliable data in real time is increasing gradually. And web crawlers that collect web documents automatically have grown in importance because big-data is being used in many different fields and web data are growing exponentially every year. However, existing web crawlers can't collect whole web documents in a web site because existing web crawlers collect web documents with only URLs included in web documents collected in some web sites. Also, existing web crawlers can collect web documents collected by other web crawlers already because information about web documents collected in each web crawler isn't efficiently managed between web crawlers. Therefore, this paper proposed a distributed web crawler. To resolve the problems of existing web crawler, the proposed web crawler collects web documents by RSS of each web site and Google search API. And the web crawler provides fast crawling performance by a client-server model based on RMI and NIO that minimize network traffic. Furthermore, the web crawler extracts core content from a web document by a keyword similarity comparison on tags included in a web documents. Finally, to verify the superiority of our web crawler, we compare our web crawler with existing web crawlers in various experiments.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.27-36
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• 2014

Statistical analysis provides information that can be applied to draw final decisions in many fields. However, statistical analysis program for PC (personal computer) is yet restricted by time and space. To minimize this issue, a server based PC statistic analysis program using internet in addition to web based system allowing statistical analysis have been continually developed. However, the current web based analysis system is limited to the data that is saved on the server. Data that is modified or newly inserted must go through a server administrator before its use in analysis. In order to solve this problem, we have developed a web based system using HTML, java, JSP scripts to incorporate dynamic data without much restriction.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.831-840
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• 2001

In this study, We introduce the process of web usage mining, which has lately attracted considerable attention with the fast diffusion of world wide web, and explain the web log data, which Is the main subject of web usage mining. Also, we illustrate some real examples of analysis for web log data and look into practical application of web usage mining for eCRM.