Noncoding　RNAs　(ncRNAs)　regulate　gene　expression　in　essential　cellular　processes　and　play　key　roles　in　many　human　diseases.　Small　nucleolar　RNAs　(snoRNAs)　are　a　relatively　large　group　of　ncRNAs.　Many　studies　have　identified　significant　alterations　of　different　snoRNAs　in　prostate,　breast,　and　lung　malignancies　and　that　many　of　these　snoRNAs　have　been　shown　to　be　processed　into　microRNA-like　molecules　known　as　snoRNA-derived　RNAs　(sdRNAs).　Similarly,　several　small　RNAs　have　also　recently　been　found　to　be　excised　from　well　characterized　tRNAs　to　function　in　both　normal　cellular　metabolism　and　disease.　In　this　paper,　we　present　a　new　computational　methodology　for　the　identification,　analysis,　and　visualization　of　ncRNA-derived　RNAs　(ndRNAs)　with　linear　time　and　space　complexity　(named　SURFr　for　Short　Uncharacterized　R　NA　Finder).　We　provide　the　results　of　our　algorithm＇s　running　time　by　analyzing　several　publicly　available　datasets　and　finally　discuss　future　research　directions　to　our　work.　©　2019　IEEE.