SYSTEM AND METHOD FOR PREDICTING HARMFUL MATERIALS

• US 20160178590A1
• Filed: 09/22/2015
• Published: 06/23/2016
• Est. Priority Date: 12/23/2014
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: PARK, Hyeon, KANG, Sung Soo, JEONG, Hoon
• Application Number:
  US14/860,799
• Publication Number:
  US 20160178590A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01N 33/02 Food

Abstract

Provided herein is a harmful material prediction system including a harmful material feature collecting unit configured to collect harmful material features of food; a preprocessing unit configured to preprocess the harmful material features collected and generate harmful material information of an analyzable format; and a Hadoop-based dispersed cluster configured to generate a similarity group where similarity base points are grouped based on a correlation per variable included in the harmful material information, thereby predicting in real time the harmful materials over the overall phases of distribution.

Claims

1. A harmful material prediction system comprising:

a harmful material feature collecting unit configured to collect harmful material features of food;

a preprocessing unit configured to preprocess the harmful material features collected and generate harmful material information of an analyzable format; and

a Hadoop-based dispersed cluster configured to generate a similarity group where similarity base points are grouped, based on a correlation per variable included in the harmful material information.

2. The system according to claim 1,

wherein the Hadoop-based dispersed cluster comprises:

a similarity measuring unit per variable configured to generate a similarity matrix per variable based on the correlation per variable included in the harmful material information;

a final similarity measuring unit configured to generate a final similarity matrix based on the similarity material per variable; and

a similarity group computing unit configured to generate the similarity group where similarity base points are grouped, based on the final similarity matrix.

3. The system according to claim 1,

wherein the harmful material features comprise at least one of a human big data, system big data and social big data, and

the harmful material feature collecting unit collects the human big data based on a sqoop, collects the system big data based on a flume, and collects the social big data based on a crawler.

4. The system according to claim 3,

wherein the human big data comprises an institution generated data, the system big data comprises data generated from a detection equipment, and the social big data comprises text data generated through social media.

5. The system according to claim 1,

wherein the Hadoop-based dispersed cluster comprises a plurality of storages that store the harmful material information.

6. A harmful material prediction method comprising:

collecting harmful material features of food;

preprocessing the collected harmful material features, and generating harmful material information of an analyzable format; and

generating, by a Hadoop-based dispersed cluster, a similarity group where similarity base points are grouped, based on a correlation per variable included in the harmful material information.

7. The method according to claim 6,

wherein the generating a similarity group where similarity base points are grouped based on a correlation per variable included in the harmful material information comprises:

storing the harmful material information in the Hadoop-based dispersed cluster;

analyzing, by the Hadoop-based dispersed cluster, the correlation per variable included in the harmful material information and generating a similarity matrix per variable;

generating, by the Hadoop-based dispersed cluster, a final similarity matrix based on the similarity matrix per variable; and

generating a similarity group where similarity base points are grouped based on the final similarity matrix.

8. The method according to claim 6,

wherein the storing the harmful material information in the Hadoop-based dispersed cluster stores the harmful material information repeatedly in at least two slave nodes included in the Hadoop-based dispersed cluster.

9. The method according to claim 6,

further comprising visualizing and displaying the generated similarity group.

10. The method according to claim 6,

wherein the harmful material features comprise at least one of human big data, system big data, and social big data, and

the collecting harmful material features of food collects the human big data based on a sqoop, collects the system big data based on a flume, and collects the social big data based on a crawler.