Many organizations have large amounts of information, such as consumer data, that need to be processed. Traditional searching algorithms only attempt to find exact matches to particular queries. This is undesirable when data are missing, outdated, or inaccurate. Therefore, a new type of search must be developed to locate records that are considered "interesting" to the user. This research paper examines past attempts to solve this problem and explores a new method involving ordered token lists to achieve this goal. The algorithm was developed, implemented, tested, and optimized.
"Relative Searching using an Ordered Token List,"
Inquiry: The University of Arkansas Undergraduate Research Journal: Vol. 9
, Article 15.
Available at: https://scholarworks.uark.edu/inquiry/vol9/iss1/15