Management Report for Marketing in Higher Education Based On Data Warehouse and Data Mining

Rudy,Eka Miranda 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.4

Elements of globalization in the world of education has expanded and developed, and made an Higher Education institutions market has been developed as a global phenomenon, so that Higher Education institutions aware to show their existence in global and high competition. The objective of this study was to apply data warehouse and data mining techniques that can be used by universities to obtain relevant information about their current condition, and to track the institution's development, which is required by the management to monitor organization performance in marketing area, and to support information in decision making process. The research method began with the collection of data and information, analyzes the current condition in marketing area, design the data warehouse model and mining the data. The obtained results were the data warehouse model and evaluation model using data mining technique to support the management in marketing decision-making process.

데이터 마이닝을 이용한 산업재해 예측모델에 관한 연구

이관형,정호근,박정선 大韓産業醫學會 2000 대한직업환경의학회지 Vol.12 No.4

목적 : 우리나라 전체 산업재해의 발생패턴과 추이를 파악하구 미래시점에 발생할 수 있는 산업재해자수를 예측 개발하여 장단기 산업보건 예방정책을 수립하는데 기여하고자 한다. 방법 : 예측모형에 사용된 자료는 1986년 1월부터 1999년 7월 까지 발생된 월별 누적 재해자수이며, 이 자료로부터 테이터 마이닝 기법을 사용하여 미래시점의 산업재해자 예측모델을 개발하였다. 결과 : 163개월 분의 산업재해 발생자료로부터 미래시점의 산업재해자수를 예측한 결과, Robust한 예측모형은 Winter∼method multiplicative in exponential smoothing로 예측력이 95%을 보였다. 산업재해 시도표를 탐색하면 전체적으로 산업재해자는 감소추세를 보이며, 순환주기를 1년으로 보면 2월과 9월이 가장 낮고, 6, 7, 10, 11월에 재해가 가장 많이 발생됨을 알 수 있었다. 월 평균 재해자 발생규모는 8,709명이다(95% CI;8277명, 9140명), 개발된 예측모형으로부터 1999년 8월 이후의 산업재해 발생자 규모를 보면, 1999년 12월과 2000년 1/4분기에 급격히 감소추세를 보이다가 2/4분기 시점을 정점으로 다시 재해자수가 증가할 것으로 예측된다. 결론 : 개발된 윈터스 모형을 이용한 미래시점의 산업재해 월별 발생 예측치는 (Table 3)과 같다. 예측치를 보면 1999년 긴월에서 2000년 1월, 2월에 급격히 감소추세에서 2000년도 2/4분기에 다시 서서히 증가하고 있다. 그리고 과거시점과 미래시점의 월별 산업재해 발생 실측치와 예측치 시도표는 Fig. 12와 같다. 또한 1998년에는 전반적인 발생추이 경향이 무너졌는데, 이는 한국 경제의 크나 큰 사건인 1997년 10월에 발표한 IMF에 의한 산업전반의 침체가 개입된 것으로 판단되며, 1999년에는 경기침체에서 벗어나 경제가 활성화 국면이 된다면 10월, 11월에는 이전보다 다소 재해자가 증가할 것으로 예상된다. 그리고 시간이 지남에 따라 추가적으로 발생된 월별 산업재해자수를 개발된 모델에 투입시키면서 검증과 평가를 통해모델을 정립할 계획이다. Objectives : This study is to see the transition and pattern of the industrial i울ureal worker, and to develop the prediction model. Methods : The data of the study are based on the samples from data-warehouse of Occupational Safety & Health Research Institute and are summed monthly from Jan 1986 to Dec 1999. This study data used data mart and Meta data from DW in KOSHA. The prediction model of the injured worker in Industry is designed by using a winters time series method after data preparing (i. e. sample, explore, modify) from DW. Results : Thls predicted model obtained Winters-method multiplicative in exponential smoothing among applied all models, after the tlme series (total 163 months). It showed that the prediction power was 95.5 %. Conclusions : In the process of exploring the data, totally the rate of industrial injureal workers reduced, and in the yearly circulation, in February and September the number is the lowest but in June, July, October and November the higher. The number of monthly average injureal workers is 8709 (95 % confidence interval 8277, 9140). From the developed prediction model, since Aug 1999 the industrial injureal worker reduced rapidly in Dec 1999 and first period of 2000. But In second period of 2000 the number of the injured workers is increasing. To conclude, as the total economic situation is becoming better in 2000 than In 1999, its is supposed that the injured workers will increase more than the predictive injured workers because of the increase of production rate and labor force.

보험사의 고객 이탈에 대한 예측모형 개발

한상태,강현철,최호식,도종두,신선화 한국자료분석학회 2009 Journal of the Korean Data Analysis Society Vol.11 No.1

Recently, with development of information technology, a great deal of data accumulate in companies' operational database. To manage these data more efficiently, many companies already set database of information such as Data Warehouse (DW) and they are trying to make good use of these data. In this study, we're going to introduce the real project format for utilizing these data at work. Especially we aim to show that how data mining is being used for searching information. Also, specifically, we are going to show you whole process of searching information modeling by using customers attribute and information of their transactions in insurance company. 최근 정보기술 분야의 급속한 발전과 더불어 기업들의 운영계 데이터베이스에는 엄청난 양의 데이터가 쌓이고 있다. 이러한 데이터를 보다 효율적으로 관리하기 위하여 많은 기업들에서는 데이터웨어하우스(DW)로 대변되는 정보계 데이터베이스를 이미 구축하였고 이를 활용하려는 움직임이 활발하게 진행되고 있다. 본 연구에서는 이러한 기업의 데이터를 업무에 활용하기 위한 실제 프로젝트의 모습을 소개하고자 한다. 특히 데이터마이닝이 지식발견에 어떻게 활용되고 있는가를 보여주고자 하는 것이 목적인데, 구체적으로 보험사의 고객 속성 및 거래 정보를 이용하여 지식발견 모델링의 구체적 프로세스를 보여줄 것이다.

Emerging Data Management Tools and Their Implications for Decision Support

Eorm, Sean B.,Novikova, Elena,Yoo, Sangjin Korea Society of Industrial Information Systems 1997 한국산업정보학회논문지 Vol.2 No.2

Recently, we have witnessed a host of emerging tools in the management support systems (MSS) area including the data warehouse/multidimensinal databases (MDDB), data mining, on-line analytical processing (OLAP), intelligent agents, World Wide Web(WWW) technologies, the Internet, and corporate intranets. These tools are reshaping MSS developments in organizations. This article reviews a set of emerging data management technologies in the knowledge discovery in databases(KDD) process and analyzes their implications for decision support. Furthermore, today's MSS are equipped with a plethora of AI techniques (artifical neural networks, and genetic algorithms, etc) fuzzy sets, modeling by example , geographical information system(GIS), logic modeling, and visual interactive modeling (VIM) , All these developments suggest that we are shifting the corporate decision making paradigm form information-driven decision making in the1980s to knowledge-driven decision making in the 1990s.

A Data Mining Approach for Selecting Bitmap Join Indices

Ladjel Bellatreche,Rokia Missaoui,Hamid Necir,Habiba Drias 한국정보과학회 2007 Journal of Computing Science and Engineering Vol.1 No.2

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

A Data Mining Approach for Selecting Bitmap Join Indices

Bellatreche, Ladjel,Missaoui, Rokia,Necir, Hamid,Drias, Habiba Korean Institute of Information Scientists and Eng 2007 Journal of Computing Science and Engineering Vol.1 No.2

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

유통업에서 MBA분석과 시뮬레이션을 이용한 물류센타 재고배치 효율화에 관한 연구

여성주,성길영,왕지남 대한산업공학회 2009 산업공학 Vol.22 No.3

It is most important for distribution center in retail business to delivery commodities in a timely manner. Accordingly, many companies try to make distribution center effective using the Warehouse Management System(WMS) integrated legacy system. Also, the Customer Relationship Management(CRM) is the most typical paradigm in management lately. Even though the WMS and CRM are independent system of each other, WMS, coupled with CRM makes customer satisfied more effectively. In this paper, we proposed the methodology for inventory location after analyzing and applying customer buying pattern data in the CRM through the MBA(Market Basket Analysis), which is part of data mining. We used an example modeling a real distribution center in retail through a 3D simulation tool and examined correlation between commodities using customer buying pattern. After that, we applied it to the inventory location system through the MBA in an example. Finally, we identified decrease in the time for picking, which is the majority of distribution center. Besides, we proposed a simulation methodology before applying new methodology. Consequently, it removes potential errors in advance and makes a optimized inventory location system.

Building Data Warehousing and Data Mining from Course Management Systems : A Case Study of FUTA Course Management Information Systems

Akintola K.G,Adetunmbi A.O.,Adeola O.S. 보안공학연구지원센터 2011 International Journal of Database Theory and Appli Vol.4 No.3

A Framework for Data Warehouse Using Data Mining and Knowledge Discovery for a Network of Hospitals in Pakistan

Muhammad Arif,Asad Khatak,Mehdi Hussain 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.3

Now-a-days, in Pakistan especially in strategic, military and private sector hospitals, there is an increasing use of hospital information systems. It has been seemed that a localize approach for developing HMIS is prevailing i.e. conventional use of relational database system, which are isolated from others hospitals or remote data collection centers. In this, paper a framework is proposed for establishment of data warehouse to centralize data from remote hospitals and collection centers. Data mining and knowledge discovery modal is also discussed.

XMLA를 사용한 OLAP과 데이타 마이닝 분석이 가능한 리포팅 툴의 구현

최지웅(Jeewoong Choe),김명호(Myungho Kim) 한국정보과학회 2009 정보과학회 컴퓨팅의 실제 논문지 Vol.15 No.3

기업 운영에서 발생하는 데이타의 수집과 통합에서부터 의사결정을 위한 정보의 분석 및 그 결과로의 접근을 제공하기 위한 BI 환경에서 최종 사용자들을 위한 프론트-엔드 툴로서는 데이타베이스 쿼리 및 리포팅 툴, OLAP 툴, 데이타 마이닝 툴이 대표적이다. 데이타베이스 쿼리 및 리포팅 툴은 SQL 쿼리 결과 셋을 반영하는 워드프로세서가 생성하는 문서 수준의 정교한 동적 문서의 생성과 웹 환경을 통한 문서 배포 능력이 장점이지만 데이타 소스가 RDBMS로 제한되어 있다. 반면, OLAP 툴과 데이타 마이닝 툴은 각기 고유한 방식으로 데이타를 분석할 수 있는 능력은 강력하지만 차트와 표 등의 제한적인 컴포넌트들 만으로 분석 결과를 제공할 수 있다는 한계를 가지고 있다. 본 논문에서는 상호 보완적으로 사용될 수 있는 BI 환경을 위한 프론트-엔드 툴들을 통합하였다. 본 논문에서 제안하는 리포팅 툴은 RDBMS에서 데이타를 추출하기 위한 SQL 기반의 쿼리 편집기 만을 내장한 기존의 리포팅 툴과 달리 OLAP과 데이타 마이닝을 위한 쿼리 편집기를 추가하여 OLAP과 데이타 마이닝 서버로부터도 데이타를 추출할 수 있다. 그리고 기존의 리포팅 툴은 동일한 문서를 다수의 사용자들이 조회하는 상황에서 반복된 문서 생성을 피하기 위하여 서버 측에서 문서를 생성하는 구조를 갖지만 이 시스템은 다수의 사용자들을 위한 문서배포 목적이 아닌 사용자들이 데이타 분석 목적으로 서로 다른 문서를 생성하는 상황에 적합하도록 서버 측에 비해 제한된 리소스 환경을 갖는 클라이언트 측에서 동작하는 리포트 뷰어에서 대량의 데이타를 포함하는 문서를 생성할 수 있는 구조와 처리방식을 갖고 있다. 또한 이 시스템에서 접근하는 세 가지 종류의 데이타 소스에서 추출한 데이타들을 연계하여 하나의 문서에서 통합할 수 있도록 하는 자료 구조를 갖추고 있다. 마지막으로 이 시스템은 특정 벤더의 OLAP과 데이타 마이닝 서버에 종속적으로 동작하지 않기 위하여 웹 서비스 기반의 XMLA를 이들 서버와의 통신 프로토콜로써 선택하였다. 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.