Case Study.  Client needed to build a tool to crawl through their data set and identify duplicates  The algorithm should identify exact as well as near.

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Presentation transcript:

Case Study

 Client needed to build a tool to crawl through their data set and identify duplicates  The algorithm should identify exact as well as near de duplicates  The data set can have files for varying formats like word, pdf, PowerPoint, excel, s etc  The solution needs to identify the similarity of documents and provide a report which would say document X is 95% similar to document Y  The documents were then fed into a review cycle and thus it was important to identify near duplicate documents to save cost and time

 Various file formats to support  Bit by bit comparison would work for exact finding out exact duplicates however does not work even if small portion of document has changed  We also need to consider the overall document size. In case a document is huge a small paragraph of change can be considered as near duplicate however for small documents this change can make them totally different

 ProsperaSoft developed an algorithm to identify near de duplicates in any format of document  Algorithm inputs were taken from the problem already solved by Google and we also referred to various other mathematical studies done to identify duplicates based on sliding window of bytes and identifying similarities based on hash that are generated from the file data.

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