The Impact of Foreign Direct Investment in Japan: Case Studies of the Automobile, Finance, and Health Care Industries

Having historically received very little foreign direct investment, Japan has experienced a substantial increase in such inflows in recent years. This paper analyzes the impact of the growing presence of foreign firms on the Japanese economy through detailed case studies on the automobile, finance, and health care industries. The wholesale & retail and the telecommunications sector are also briefly examined. The case studies show that in the sectors considered, foreign firms in one way or another are contributing to a greater degree of competition, are exposing domestic firms to global best practice, and are increasing the range of products and services available in Japan. In many of the sectors, they are also contributing to changes in industry structure and employment practices. The case studies thus illustrate that foreign direct investment - even at its present levels, which, although large by Japanese standards, are still low in international comparison - can be an important catalyst for change and hence help to reinvigorate the Japanese economy.

Data mining and database systems: integrating conceptual clustering with a relational database management system.

Many clustering algorithms have been developed and improved over the years to cater for large scale data clustering. However, much of this work has been in developing numeric based algorithms that use efficient summarisations to scale to large data sets. There is a growing need for scalable categorical clustering algorithms as, although numeric based algorithms can be adapted to categorical data, they do not always produce good results. This thesis presents a categorical conceptual clustering algorithm that can scale to large data sets using appropriate data summarisations. Data mining is distinguished from machine learning by the use of larger data sets that are often stored in database management systems (DBMSs). Many clustering algorithms require data to be extracted from the DBMS and reformatted for input to the algorithm. This thesis presents an approach that integrates conceptual clustering with a DBMS. The presented approach makes the algorithm main memory independent and supports on-line data mining

Data Mining Applications On Web Usage Analysis & User Profiling

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2003Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2003Tez çalışmasında veri madenciliği teknolojisi, fonksiyonları ve uygulamaları özetlenmiştir. OLAP teknolojilerine ve veri ambarlarına da veri madenciliğinin anahtar kavramları olarak değinilmiştir. Uygulama kısmında müşteri ve alışveriş kalıpları analizi için bir internet parakendecisinin işlemsel verileri kullanılmıştır. Müşteri segmentasyonu ve kullanıcı betimleme gibi konulardaki kurumsal kararları desteklemek amacıyla veri içerisindeki kalıplar çıkarılmaya çalışılmıştır.This thesis gives a summary of data mining technology, its functionalities and applications. OLAP technology and data warehouses are also introduced as the key concepts in data mining. The usage of data mining on the internet and the decisions based on internet usage data are introduced. In the application section a web retailer’s transactional data is used for analyzing customer and shopping patterns.Hidden patterns within the data are tried to be extracted in order to support business decisions such as user profiling and customer segmentation.Yüksek LisansM.Sc

Web structure mining of dynamic pages

Web structure mining in static web contents decreases the accuracy of mined outcomes and affects the quality of decision making activity. By structure mining in web hidden data, the accuracy ratio of mined outcomes can be improved, thus enhancing the reliability and quality of decision making activity. Data Mining is an automated or semi automated exploration and analysis of large volume of data in order to reveal meaningful patterns. The term web mining is the discovery and analysis of useful information from World Wide Web that helps web search engines to find high quality web pages and enhances web click stream analysis. One branch of web mining is web structure mining. The goal of which is to generate structural summary about the Web site and Web pages. Web structure mining tries to discover the link structure of the hyperlinks at the inter-document level. In recent years, Web link structure mining has been widely used to infer important information about Web pages. But a major part of the web is in hidden form, also called Deep Web or Hidden Web that refers to documents on the Web that are dynamic and not accessible by general search engines; most search engine spiders can access only publicly index able Web (or the visible Web). Most documents in the hidden Web, including pages hidden behind search forms, specialized databases, and dynamically generated Web pages, are not accessible by general Web mining applications. Dynamic content generation is used in modern web pages and user forms are used to get information from a particular user and stored in a database. The link structure lying in these forms can not be accessed during conventional mining procedures. To access these links, user forms are filled automatically by using a rule based framework which has robust ability to read a web page containing dynamic contents as activeX controls like input boxes, command buttons, combo boxes, etc. After reading these controls dummy values are filled in the available fields and the doGet or doPost methods are automatically executed to acquire the link of next subsequent web page. The accuracy ratio of web page hierarchical structures can phenomenally be improved by including these hidden web pages in the process of Web structure mining. The designed system framework is adequately strong to process the dynamic Web pages along with static ones

Data mining and database systems : integrating conceptual clustering with a relational database management system

Many clustering algorithms have been developed and improved over the years to cater for large scale data clustering. However, much of this work has been in developing numeric based algorithms that use efficient summarisations to scale to large data sets. There is a growing need for scalable categorical clustering algorithms as, although numeric based algorithms can be adapted to categorical data, they do not always produce good results. This thesis presents a categorical conceptual clustering algorithm that can scale to large data sets using appropriate data summarisations. Data mining is distinguished from machine learning by the use of larger data sets that are often stored in database management systems (DBMSs). Many clustering algorithms require data to be extracted from the DBMS and reformatted for input to the algorithm. This thesis presents an approach that integrates conceptual clustering with a DBMS. The presented approach makes the algorithm main memory independent and supports on-line data mining.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Longitudinal study of first-time freshmen using data mining

In the modern world, higher education is transitioning from enrollment mode to recruitment mode. This shift paved the way for institutional research and policy making from historical data perspective. More and more universities in the U.S. are implementing and using enterprise resource planning (ERP) systems, which collect vast amounts of data. Although few researchers have used data mining for performance, graduation rates, and persistence prediction, research is sparse in this area, and it lacks the rigorous development and evaluation of data mining models. The primary objective of this research was to build and analyze data mining models using historical data to find out patterns and rules that classified students who were likely to drop-out and students who were likely to persist.;Student retention is a major problem for higher education institutions, and predictive models developed using traditional quantitative methods do not produce results with high accuracy, because of massive amounts of data, correlation between attributes, missing values, and non-linearity of variables; however, data mining techniques work well with these conditions. In this study, various data mining models were used along with discretization, feature subset selection, and cross-validation; the results were not only analyzed using the probability of detection and probability of false alarm, but were also analyzed using variances obtained in these performance measures. Attributes were grouped together based on the current hypotheses in the literature. Using the results of feature subset selectors and treatment learners, attributes that contributed the most toward a student\u27s decision of dropping out or staying were found, and specific rules were found that characterized a successful student. The performance measures obtained in this study were significantly better than previously reported in the literature

Concept demonstrator for a decision support tool for agricultural applications

Industrial Engineerin

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse