The Indonesian digital library network is born to struggle with the digital divide

IndonesiaDLN –The Indonesian Digital Library Network– is a distributed collection of digital library networks, digital library servers, full local contents, metadata, and people for the development of the Indonesian knowledge-based society. Beside the general issues of digital library such as publishing, quality control, authentication, networking, and information retrieval, we also face other issue –namely digital divide– in designing and implementing the Network. This paper describes basic design of the Network that able to handle the typical problems in developing digital library network in Indonesia as a developing country, such as internet accessibility, bandwidth capacity, and network delays. We also will describe our experiences in implementing the Network that currently has 14 successfully connected partners and more than 15 partners are in progress of developing their digital library servers

Shifting Gears: State Innovation to Advance Workers and the Economy in the Midwest

Outlines five states' policy actions to expand access to postsecondary credentials and careers and innovations implemented through Joyce's initiative, including combining basic skills content with workforce readiness, support services, and specialization

Web Mining Functions in an Academic Search Application

This paper deals with Web mining and the different categories of Web mining like content, structure and usage mining. The application of Web mining in an academic search application has been discussed. The paper concludes with open problems related to Web mining. The present work can be a useful input to Web users, Web Administrators in a university environment.Database, HITS, IR, NLP, Web mining

Strategic enterprise management systems : tools for the 21st century

https://egrove.olemiss.edu/aicpa_guides/1228/thumbnail.jp

Web Data Mining: A Case Study

With an enormous amount of data stored in databases and data warehouses, it is increasingly important to develop powerful tools for analysis of such data and mining interesting knowledge from it. Data mining is a process of inferring knowledge from such huge data. The main problem related to the retrieval of information from the World Wide Web is the enormous number of unstructured documents and resources, i.e., the difficulty of locating and tracking appropriate sources. In this article, a survey of the research in the area of web mining and suggest web mining categories and techniques. Furthermore, a presentation of a web mining environment generator that allows naive users to generate a web mining environment specific to a given domain by providing a set of specification

Evaluating Web Data for Data Mining

Organizations’ operational data constructs the major data source for their data warehouse. The exponential development of WWW has made Internet an immense database containing all kinds of information with various types of data structures. Organizations are increasingly interested in capturing web data into their data warehousing systems to enlarge their data source for decision supporting, therefore improving accuracy and effectiveness of their decision making. This research thoroughly analyzes the data value of web data to data warehousing as well as business decision making, discusses the feasibility and potential problem of loading web data into data warehouse system, and provides a framework for evaluating web data for data warehousing purpose. Web data analysis and evaluation is regarded as a prerequisite for Web Integration - a breakthrough approach in furnishing data warehouse input: extracting, scrubbing, transforming web data and loading it into data warehouse systems to support organization decision making

Examining Quality Factors Influencing the Success of Data Warehouse

Increased organizational dependence on data warehouse (DW) systems has drived the management attention towards improving data warehouse systems to a success. However, the successful implementation rate of the data warehouse systems is low and many firms do not achieve intended goals. A recent study shows that improves and evaluates data warehouse success is one of the top concerns facing IT/DW executives. Nevertheless, there is a lack of research that addresses the issue of the data warehouse systems success. In addition, it is important for organizations to learn about quality needs to be emphasized before the actual data warehouse is built. It is also important to determine what aspects of data warehouse systems success are critical to organizations to help IT/DW executives to devise effective data warehouse success improvement strategies. Therefore, the purpose of this study is to further the understanding of the factors which are critical to evaluate the success of data warehouse systems. The study attempted to develop a comprehensive model for the success of data warehouse systems by adapting the updated DeLone and McLean IS Success Model. Researcher models the relationship between the quality factors on the one side and the net benefits of data warehouse on the other side. This study used quantitative method to test the research hypotheses by survey data. The data were collected by using a web-based survey. The sample consisted of 244 members of The Data Warehouse Institution (TDWI) working in variety industries around the world. The questionnaire measured six independent variables and one dependent variable. The independent variables were meant to measure system quality, information quality, service quality, relationship quality, user quality, and business quality. The dependent variable was meant to measure the net benefits of data warehouse systems. Analysis using descriptive analysis, factor analysis, correlation analysis and regression analysis resulted in the support of all hypotheses. The research results indicated that there are statistically positive causal relationship between each quality factors and the net benefits of the data warehouse systems. These results imply that the net benefits of the data warehouse systems increases when the overall qualities were increased. Yet, little thought seems to have been given to what the data warehouse success is, what is necessary to achieve the success of data warehouse, and what benefits can be realistically expected. Therefore, it appears nearly certain and plausible that the way data warehouse systems success is implemented in the future could be changed