|Sponsor||Ling Liu / James Caverlee
223 / 225B CCB
|Area||Systems and Databases|
The unabated growth of the Web has resulted in a situation in which more information is available to more people than ever in human history. Along with this unprecedented growth has come the inevitable problem of information overload. To counteract this information overload, users typically rely on search engines (like Google and AllTheWeb) or on manually-created categorization hierarchies (like Yahoo! and the Open Directory Project). Though excellent for accessing Web pages on the so-called "crawlable" web, these approaches overlook a much more massive and high-quality resource: the Deep Web.
The Deep Web (or Hidden Web) comprises all information that resides in autonomous databases behind portals and information providers' web front-ends. Web pages in the Deep Web are dynamically-generated in response to a query through a web site's search form and often contain rich content. A recent study has estimated the size of the Deep Web to be more than 500 billion pages, whereas the size of the "crawlable" web is only 1% of the Deep Web (i.e., less than 5 billion pages). Even those web sites with some static links that are "crawlable" by a search engine often have much more information available only through a query interface. Unlocking this vast deep web content presents a major research challenge.
In analogy to search engines over the "crawlable" web, we argue that one way to unlock the Deep Web is to employ a fully automated approach to extracting, indexing, and searching the query-related information-rich regions from dynamic web pages. For this miniproject, we focus on the first of these: extracting data from the Deep Web.
Extracting the interesting information from a Deep Web site requires many things: including scalable and robust methods for analyzing dynamic web pages of a given web site, discovering and locating the query-related information-rich content regions, and extracting itemized objects within each region. By full automation, we mean that the extraction algorithms should be designed independently of the presentation features or specific content of the web pages, such as the specific ways in which the query-related information is laid out or the specific locations where the navigational links and advertisement information are placed in the web pages.
There are many possible 7001-miniprojects. Feel free to talk to either of us for more details. Here are a few possibilities to consider:
Background: Knowledge of Java or Python would be helpful. Some knowledge of information retrieval and machine learning may be useful but is not required.
Deliverables: You should submit a report that clearly describes what you have learned and what you have accomplished. The report should include useful references. You should also provide any source code you may have written to validate your ideas.
Evaluation: You will be graded on the novelty and quality of your report and implementation.