Automate Information Extraction from Scan Data

1998 ◽  
Author(s):  
Joseph H. Nurre
Keyword(s):  
Information Extraction ◽  
Scan Data ◽  
Automate Information

scholarly journals A Contrast of Guideline Recommendations and Tullis's Prediction Model for Computer Displays: Should Text be Left-Justified?

1987 ◽  
Vol 31 (11) ◽  
pp. 1226-1228
Author(s):  
Gail A. Fontenelle
Keyword(s):  
Prediction Model ◽  
Information Extraction ◽  
Search Time ◽  
Guideline Recommendation ◽  
Subjective Ratings ◽  
Multiple Data ◽  
Scan Data ◽  
Types Of Information

Two experiments investigated the effect of layout complexity for performance at varying levels of practice on four types of information extraction tasks. Layout complexity is defined as the number of unique horizontal and vertical starting positions of items in the display (Tullis, 1984). In the first study, layout complexity was manipulated by either left-justifying or not left-justifying text. In the second study, subject veiwed a third experimental screen that displayed the starting positions of items in a completely unpredictable pattern. Moderate violations of the typical guideline recommendation that alphanumeric data be left-justified did not increase user search time across all four tasks in either the first or second study. Furthermore, severe violations of the recommendation did not increase user search time for three tasks (find label, scan data, and compare label). However when subjects compared multiple data values, the random format did increase user search time. Though performance using the three experimental screens was comparable across the four tasks with only one exception, subjective ratings demonstrated differences between the three formats.

A FRAME WORK FOR WEB INFORMATION EXTRACTION AND ANALYSIS

2013 ◽  
Vol 7 (2) ◽  
pp. 574-579 ◽  
Author(s):  
Dr Sunitha Abburu ◽  
G. Suresh Babu
Keyword(s):  
Information Extraction ◽  
Data Extraction ◽  
Research Work ◽  
Web Pages ◽  
Web Documents ◽  
E Learning ◽  
Structured Information ◽  
Frame Work ◽  
Effective Decision ◽  
The Web

Day by day the volume of information availability in the web is growing significantly. There are several data structures for information available in the web such as structured, semi-structured and unstructured. Majority of information in the web is presented in web pages. The information presented in web pages is semi-structured.  But the information required for a context are scattered in different web documents. It is difficult to analyze the large volumes of semi-structured information presented in the web pages and to make decisions based on the analysis. The current research work proposed a frame work for a system that extracts information from various sources and prepares reports based on the knowledge built from the analysis. This simplifies  data extraction, data consolidation, data analysis and decision making based on the information presented in the web pages.The proposed frame work integrates web crawling, information extraction and data mining technologies for better information analysis that helps in effective decision making.   It enables people and organizations to extract information from various sourses of web and to make an effective analysis on the extracted data for effective decision making.  The proposed frame work is applicable for any application domain. Manufacturing,sales,tourisum,e-learning are various application to menction few.The frame work is implemetnted and tested for the effectiveness of the proposed system and the results are promising.

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