First Advisor

Mason, Robert T.

Second Advisor

Barnes, Stephen D.

Third Advisor

Plantz-Masters, Shari


College for Professional Studies

Degree Name

MS Database Technologies


School of Computer & Information Science

Document Type

Thesis - Open Access

Number of Pages

335 pages


In this study, I intend to show how text based unstructured data, such as word processor documents and e-mails, can be systematically parsed for instances of data classes. Data classes can be any data type that can be fully described using the defined syntax of regular expressions. Examples of data classes can include SHA1, or MD5 hash values, Internet Protocol (IP) addresses, or any other data type whose format is well defined. Furthermore, possible correlations between datasets may be identified by grouping instances of equal or similar values within a data class. This approach of utilizing regular expressions to define the search criteria negates the need to predetermine what keyword to search for.

Date of Award

Spring 2012

Location (Creation)

Denver, Colorado

Rights Statement

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