A fuzzy string matching algorithm with multiple filters, for quicker, more accurate results
PROBLEM: Searching, indexing or cleaning noise across a large-scale database is slow and inaccurate
SOLUTION: A fuzzy string matching algorithm with multiple filters, for quicker, more accurate results
While many search engines exist, the ability to execute fast and efficient BIG DATA searches or indexing over large-scale databases, when there are many unknowns, has remained allusive.
AlgoMatch addresses this deficiency by focusing on the problem of string matching while allowing for errors, with a technique known as approximate string matching or the K-mismatch problem. The invention provides a novel method that is more efficient than the known methods, particularly in cases where the number of mismatches is large.
This is done by implementing a fuzzy string matching algorithm with a multi-filter approach. The method starts by calculating the optimal set of configurations (i.e. filters) for a given search task, and the available allocated computer memory, in order to find a necessary condition.
AlgoMatch has already proven to be hundreds of times faster than other methods in certain scenarios, proving itself as the next stage in the evolution of search and index operations.
BENEFITS
– Users can raise as many queries as desired to retrieve the best match from a database.
– The more unknowns, the more efficient AlgoMatch is over others methods.
– Automatically generates a full set of filters instead of just one.
– Easily embedded within different software codes.
– Successfully demonstrated in a bioinformatics application.
OPPORTUNITY
– BIG DATA computational biology, copyright infringement, spell check, virus detection, spam detection, speech recognition, online applications, and any other field in which searching and indexing across large-scale databases with unknown factors is required.
– Similarity and not complete identity search.
– The algorithm can also be used for SQL and other databases, efficient indexing.
Inventors: Dr. Zakharia Frenkel and Prof. Zeev Volkovich
