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SIGMOD
2001
ACM
101views Database» more  SIGMOD 2001»
14 years 7 months ago
Probe, Count, and Classify: Categorizing Hidden Web Databases
Panagiotis G. Ipeirotis, Luis Gravano, Mehran Saha...
WSDM
2010
ACM
322views Data Mining» more  WSDM 2010»
14 years 4 months ago
Inferring Search Behaviors Using Partially Observable Markov (POM) Model
This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant o...
Kuansan Wang, Nikolas Gloy, Xiaolong Li
FLAIRS
2006
13 years 9 months ago
An Empirical Exploration of Hidden Markov Models: From Spelling Recognition to Speech Recognition
Hidden Markov models play a critical role in the modelling and problem solving of important AI tasks such as speech recognition and natural language processing. However, the stude...
Shieu-Hong Lin
JUCS
2008
124views more  JUCS 2008»
13 years 7 months ago
Structure-Based Crawling in the Hidden Web
: The number of applications that need to crawl the Web to gather data is growing at an ever increasing pace. In some cases, the criterion to determine what pages must be included ...
Márcio L. A. Vidal, Altigran Soares da Silv...
ICDE
2006
IEEE
146views Database» more  ICDE 2006»
14 years 8 months ago
Query Selection Techniques for Efficient Crawling of Structured Web Sources
The high quality, structured data from Web structured sources is invaluable for many applications. Hidden Web databases are not directly crawlable by Web search engines and are on...
Ping Wu, Ji-Rong Wen, Huan Liu, Wei-Ying Ma