Classification and Focused Crawling for Semistructured Data

2003 ◽  
pp. 145-157 ◽  
Author(s):  
Martin Theobald ◽  
Ralf Schenkel ◽  
Gerhard Weikum
Keyword(s):  
Semistructured Data ◽  
Focused Crawling

Semantic-Based Focused Crawling Approach

2011 ◽  
Vol 22 (9) ◽  
pp. 2075-2088 ◽  
Author(s):  
Yu-Xin YE ◽  
Dan-Tong OUYANG
Keyword(s):  
Focused Crawling

Integrating dynamically-fetched external information into a DBMS for semistructured data

1997 ◽  
Vol 26 (4) ◽  
pp. 24-31 ◽  
Author(s):  
Jason McHugh ◽  
Jennifer Widom
Keyword(s):  
Semistructured Data ◽  
External Information

Extracting schema from semistructured data

1998 ◽  
Author(s):  
Svetlozar Nestorov ◽  
Serge Abiteboul ◽  
Rajeev Motwani
Keyword(s):  
Semistructured Data

ANALYSIS OF PARSING AND WEBSCRAPING TOOLS WITHIN THE FRAMEWORK DEVELOPMENT OF ARBITRATION INVESTMENT STRATEGY ON THE MARKET SPORT BETS

2021 ◽  
Vol 1 (2) ◽  
pp. 65-77
Author(s):  
T. E. Vildanov ◽  
◽  
N. S. Ivanov ◽  
Keyword(s):  
Data Extraction ◽  
Investment Strategy ◽  
Semistructured Data ◽  
Graphical Form ◽  
Great Demand ◽  
Rapid Extraction ◽  
Web Scraping ◽  
Quantitative Metrics ◽  
Framework Development ◽  
Number Of Iterations

This article explores both popular and newly invented tools for extracting data from sites and converting them into a form suitable for analysis. The paper compares the Python libraries, the key criterion of the compared tools is their performance. The results will be grouped by sites, tools used and number of iterations, and then presented in graphical form. The scientific novelty of the research lies in the field of application of data extraction tools: we will receive and transform semistructured data from the websites of bookmakers and betting exchanges. The article also describes new tools that are currently not in great demand in the field of parsing and web scraping. As a result of the study, quantitative metrics were obtained for all the tools used and the libraries that were most suitable for the rapid extraction and processing of information in large quantities were selected.

Cooperative Queries in Semistructured Data Model

2003 ◽  
pp. 237-247
Author(s):  
Kartik Menon ◽  
Sanjay Madria ◽  
A. Badia
Keyword(s):  
Data Model ◽  
Semistructured Data

Functional Dependencies for Semistructured Data

2018 ◽  
pp. 1553-1555
Author(s):  
Gillian Dobbie ◽  
Tok Wang Ling
Keyword(s):  
Semistructured Data ◽  
Functional Dependencies

Semistructured Data and XML

2000 ◽  
pp. 9-30 ◽  
Author(s):  
Dan Suciu
Keyword(s):  
Semistructured Data

scholarly journals Extraction of Tag Tree Patterns with Contractible Variables from Irregular Semistructured Data

2003 ◽  
pp. 430-436 ◽  
Author(s):  
Tetsuhiro Miyahara ◽  
Yusuke Suzuki ◽  
Takayoshi Shoudai ◽  
Tomoyuki Uchida ◽  
Sachio Hirokawa ◽  
...  
Keyword(s):  
Semistructured Data ◽  
Tree Patterns
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