Data error detection method, apparatus, software, and medium

• US 20040078730A1
• Filed: 08/12/2002
• Published: 04/22/2004
• Est. Priority Date: 08/15/2001
• Status: Abandoned Application

• Current Assignee: Communications Research Laboratory Independent Administrative Institution
• Original Assignee: Communications Research Laboratory Independent Administrative Institution
• Inventors: Lu, Bao-Liang, Ma, Qing
• Application Number:
  US10/216,214
• Publication Number:
  US 20040078730A1
• Time in Patent Office: Days
• US Class Current: 714/54
• CPC Class Codes: G06F 11/0727 in a storage system, e.g. in a DASD or network based storage system drivers for digital recording or reproducing units G06F3/06; circuits for error detection or correction within digital recording or reproducing units G11B20/18; for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS], H04L67/1097
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06

Abstract

The aim of this invention is to provide a fast, highly efficient, and highly accurate data error detection method for a database that includes at least two types of data and in which one type of data can be classified by another type of data. The classification in the database is regarded as a class in a neural network. The original classification problem is divided into smaller two-class subproblems to provide a number of modules, and calculation is made to check whether or not each of the said module converges in the learning process in the neural network. If a module does not converge, the module is regarded as having pattern classification errors and is then extracted.

Claims

1. A data error detection method for a database containing at least two kinds of data and in which one kind of data can be classified by another kind of data. The detection method consists of the following steps:

treating the classification as a class in a neural network;

dividing the classification problem into smaller two-class problems for a plurality of modules;

making calculations to check whether or not each of the said modules converges in the learning process in the neural network; and

,

regarding said module having pattern classification errors in the case of convergence failure, extracting it.

2. A data error detection apparatus for a database containing at least two kinds of data and in which one kind of data can be classified by another kind of data. The apparatus consists of the following:

a means for memorizing said database;

a means of calculation for treating the classification as a class in a neural network, dividing the classification problem into smaller two-class problems for a plurality of modules, checking whether or not each of the said modules converges in the learning process in the neural network; and

a means of error extraction for regarding said module as having pattern classification errors in the case of convergence failure and extracting it.

3. A data error detection software program for a database containing at least two kinds of data and in which one kind of data can be classified by another kind of data. The detection program consists of the following steps:

treating the classification as a class in a neural network and dividing the classification problem into smaller two-class problems for a plurality of modules;

making calculations to check whether or not each of the said modules converges in the learning process in the neural network; and

regarding said module as having pattern classification errors in the case of convergence failure and extracting it.

4. A medium storing a data error detection software program for a database containing at least two kinds of data and in which one kind of data can be classified by another kind of data. The program consists of the following:

a memory unit treating the classification as a class in a neural network and dividing the classification problem into smaller two-class problems for a plurality of modules;

a memory unit making calculations to check whether or not each of the said modules converges in the learning process in the neural network; and

a memory unit regarding said module as having pattern classification errors in the case of convergence failure and extracting it.