Rule Discovery from Textual Data

Rule Discovery from Textual Data

Shigeaki Sakurai (Toshiba Corporation, Japan)
Copyright: © 2009 |Pages: 29
DOI: 10.4018/978-1-60566-098-1.ch025

Abstract

This chapter introduces knowledge discovery methods based on a fuzzy decision tree from textual data. The author argues that the methods extract features of the textual data based on a key concept dictionary, which is a hierarchical thesaurus, and a key phrase pattern dictionary, which stores characteristic rows of both words and parts of speech, and generate knowledge in the format of a fuzzy decision tree. The author also discusses two application tasks. One is an analysis system for daily business reports and the other is an e-mail analysis system. The author hopes that the methods will provide new knowledge for researchers engaged in text mining studies, facilitating their understanding of the importance of the fuzzy decision tree in processing textual data.
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Introduction

High availability of software systems has become very critical due to several factors that are related to the environment, processes and development strategies, hardware complexity, and the amount of dollars and human resources invested in the system. High availability cannot be achieved by just implementing a given service level or solution. Systems should be designed such that all factors that may lead the system to go down should be well-treated, if not eliminated.

In today’s competitive business landscape, 24/7 operations become the standard, especially for the e-services-driven areas (e.g., e-commerce, e-government, e-learning, etc.) Downtime of applications, systems, or networks typically translates into significant revenue loss. Industry experts and analysts agreed on that in order to support e-service applications, typical network availability must reach 99.999%. In other words, networks must be at the “5-Nines” availability level (Providing Open Architecture, 2001). Reaching this level of availability requires careful planning and comprehensive end–to-end strategy. To demonstrate the impact of not being at the “5-Nines” availability level, a system with 97% availability will incur approximately 263 hours (6.6 days) of downtime per year. With 99 percent availability, downtime will be 88 hours (2.2 days) per year. Table 1 summarizes the impact of service downtime according to the availability ratings.

Table 1.
Downtime measurements at various availability rates
Availability PercentageDowntime PercentageService Downtime (Minutes/Year)
95%5%50000
97%3%15840
98%2%10512
99%1%3168
99.5%0.5%2640
99.8%0.2%1050
99.9%0.1%528
99.95%0.05%240
99.99%0.01%53
99.999%0.001%5
99.9999%0.0001%0.51
99.99999%0.00001%0.054

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