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Mining Databases for Hidden Information Goal of Researchers

August 20, 2008

Structured hidden databases are widely prevalent on the Internet. A hidden database is one that is only accessible to external users by way of a web form. For example, an airline reservation system uses a database, but users cannot directly write programs to access hidden information. Instead, they have to rely on the web form that the airline provides to search for flights, compare pricing, make bookings, etc.

However, a traveler would find it useful to access these databases via the web form to discover what percentage of an airline’s flights arrive or depart on time at a particular city. Retrieving this kind of information is the goal of Computer Science & Engineering Drs. Gautam Das and Nan Zhang at The University of Texas at Arlington. They have received a $120,000 grant from the National Science Foundation for their project “Data Analytics over Hidden Databases.”

Drs. Das and Zhang research involves developing effective algorithms (techniques) for performing data analytics, especially sampling, of hidden structured databases. Although search interfaces for hidden databases are originally designed for focused search queries, it may be advantageous to gather more comprehensive views of the data.

Restricted form-like search interfaces allow users to execute search queries by specifying desired attribute values of the sought-after tuples (ordered lists of values or components, such as departure airport/destination/flight number/scheduled arrive time/actual arrive time), and the system responds by returning a few tuples that satisfy the selection conditions, sorted by a suitable ranking function.

Drs. Das and Zhang believe the outcomes of their project will facilitate learning data distributions or constructing mining models, which can then be used to power and optimize a multitude of data analytical applications, all having a broad impact on the nation’s high education system and high-tech industries

In addition, parts of this project will be integrated into teaching opportunities and carried out by students as part of advanced class projects, which will potentially attract motivated students to pursue doctoral degrees.