Query expansion with naive bayes for searching distributed collections

The proliferation of online information resources increases the importance of effective and efficient distributed searching. However, the problem of word mismatch seriously hurts the effectiveness of distributed information retrieval. Automatic query expansion has been suggested as a technique for dealing with the fundamental issue of word mismatch. In this paper, we propose a method - query expansion with Naive Bayes to address the problem, discuss its implementation in IISS system, and present experimental results demonstrating its effectiveness. Such technique not only enhances the discriminatory power of typical queries for choosing the right collections but also hence significantly improves retrieval results

Long Duration Exposure Facility (LDEF) Archive System

The Long Duration Exposure Facility (LDEF) Archive System is designed to provide spacecraft designers and space environment researchers single point access to all available resources from LDEF. These include data, micrographs, photographs, technical reports, papers, hardware and test specimens, as well as technical expertise. Further, the LDEF Archive System is planned such that it could be the foundation for a NASA Space Environments and Effects (SEE) Archive System, with the addition of other spaceflight, laboratory and theoretical space environments and effects data and associated materials. This paper describes the current status and plans of the LDEF Archive System

Knowledge Discovery in Databases: An Information Retrieval Perspective

The current trend of increasing capabilities in data generation and collection has resulted in an urgent need for data mining applications, also called knowledge discovery in databases. This paper identifies and examines the issues involved in extracting useful grains of knowledge from large amounts of data. It describes a framework to categorise data mining systems. The author also gives an overview of the issues pertaining to data pre processing, as well as various information gathering methodologies and techniques. The paper covers some popular tools such as classification, clustering, and generalisation. A summary of statistical and machine learning techniques used currently is also provided

Electronic Document Distribution: Design of the Anonymous FTP Langley Technical Report Server

An experimental electronic dissemination project, the Langley Technical Report Server (LTRS), has been undertaken to determine the feasibility of delivering Langley technical reports directly to the desktops of researchers worldwide. During the first six months, over 4700 accesses occurred and over 2400 technical reports were distributed. This usage indicates the high level of interest that researchers have in performing literature searches and retrieving technical reports at their desktops. The initial system was developed with existing resources and technology. The reports are stored as files on an inexpensive UNIX workstation and are accessible over the Internet. This project will serve as a foundation for ongoing projects at other NASA centers that will allow for greater access to NASA technical reports

World wide web implementation of the Langley technical report server

On January 14, 1993, NASA Langley Research Center (LaRC) made approximately 130 formal, 'unclassified, unlimited' technical reports available via the anonymous FTP Langley Technical Report Server (LTRS). LaRC was the first organization to provide a significant number of aerospace technical reports for open electronic dissemination. LTRS has been successful in its first 18 months of operation, with over 11,000 reports distributed and has helped lay the foundation for electronic document distribution for NASA. The availability of World Wide Web (WWW) technology has revolutionized the Internet-based information community. This paper describes the transition of LTRS from a centralized FTP site to a distributed data model using the WWW, and suggests how the general model for LTRS can be applied to other similar systems

World Wide Web Implementation of the Langley Technical Report Server

On January 14, 1993, NASA Langley Research Center (LaRC) made approximately 130 formal, \u27unclassified, unlimited\u27 technical reports available via the anonymous FTP Langley Technical Report Server (LTRS). LaRC was the first organization to provide a significant number of aerospace technical reports for open electronic dissemination. LTRS has been successful in its first 18 months of operation, with over 11,000 reports distributed and has helped lay the foundation for electronic document distribution for NASA. The availability of World Wide Web (WWW) technology has revolutionized the Internet-based information community. This paper describes the transition of LTRS from a centralized FTP site to a distributed data model using the WWW, and suggests how the general model for LTRS can be applied to other similar systems

A Study on the Restructure of Multi-Platform Databases for MIRAS

It is important to retrieve information that a user requires on the web. The web is an open system. The amount of information is increasing rapidly. While each of information was compiled into the database piece at a single platform in the past, it is now compiled into complicated structure at a multi-platform. Restructuring the multi-platform database is needed to efficiently retrieve information. MIRAS (Meta Information Retrieval Agent System) has a multi-platform database on the web. This study applies the classification of the existing sites' categories to restructure the database systematically. The empirical analysis shows that the suggested method is effective for information retrieval and multi-platform database restructuring. This study helps users to save on time-cost of searching information.목차 I 서론 = 1 1. 연구의 개요 = 1 2. 연구의 목적 및 범위 = 1 3. 논문의 구성 = 2 II 이론적 배경 = 3 1. 에이전트 = 3 1.1 에이전트의 정의 = 3 1.2 고객-서버 모형에서의 에이전트의 필요성 = 3 2. 정보검색 = 4 2.1 단일플랫폼의 정보검색 = 4 2.2 다중플랫폼의 메타 검색 = 4 2.3 데이터베이스의 선택문제 = 5 2.4 문서 분류 및 카테고리 = 6 3. MIRAS의 정의 및 구조 = 7 III MIRAS의 실험설계 및 데이터베이스 구조 분석 = 10 1. MIRAS 실험설계 = 10 2. MIRAS의 성능 평가 = 11 3. 사전(事前) 카테고리 구조 = 12 4. 실험검색키워드 = 14 IV 데이터 수집 및 분석 = 16 1. 데이터 수집 환경 = 16 2. 카테고리간의 관련도 = 17 3. 키워드와 카테고리간의 관련도 = 20 V MIRAS의 검색 및 성능 평가 = 22 1. MIRAS의 검색 = 22 2. MIRAS의 데이터 분석 = 24 3. MIRAS의 성능 분석 = 28 VI 결론 = 3